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HoloNets: Spectral Convolutions do extend to Directed Graphs | Within the graph learning community, conventional wisdom dictates that
spectral convolutional networks may only be deployed on undirected graphs: Only
there could the existence of a well-defined graph Fourier transform be
guaranteed, so that information may be translated between spatial- and spectral
domains. Here we show this traditional reliance on the graph Fourier transform
to be superfluous and -- making use of certain advanced tools from complex
analysis and spectral theory -- extend spectral convolutions to directed
graphs. We provide a frequency-response interpretation of newly developed
filters, investigate the influence of the basis used to express filters and
discuss the interplay with characteristic operators on which networks are
based. In order to thoroughly test the developed theory, we conduct experiments
in real world settings, showcasing that directed spectral convolutional
networks provide new state of the art results for heterophilic node
classification on many datasets and -- as opposed to baselines -- may be
rendered stable to resolution-scale varying topological perturbations. | [
"Christian Koke",
"Daniel Cremers"
] | 2023-10-03 17:42:09 | http://arxiv.org/abs/2310.02232v1 | http://arxiv.org/pdf/2310.02232v1 | 2310.02232v1 |
SNIP: Bridging Mathematical Symbolic and Numeric Realms with Unified Pre-training | In an era where symbolic mathematical equations are indispensable for
modeling complex natural phenomena, scientific inquiry often involves
collecting observations and translating them into mathematical expressions.
Recently, deep learning has emerged as a powerful tool for extracting insights
from data. However, existing models typically specialize in either numeric or
symbolic domains, and are usually trained in a supervised manner tailored to
specific tasks. This approach neglects the substantial benefits that could
arise from a task-agnostic unified understanding between symbolic equations and
their numeric counterparts. To bridge the gap, we introduce SNIP, a
Symbolic-Numeric Integrated Pre-training, which employs joint contrastive
learning between symbolic and numeric domains, enhancing their mutual
similarities in the pre-trained embeddings. By performing latent space
analysis, we observe that SNIP provides cross-domain insights into the
representations, revealing that symbolic supervision enhances the embeddings of
numeric data and vice versa. We evaluate SNIP across diverse tasks, including
symbolic-to-numeric mathematical property prediction and numeric-to-symbolic
equation discovery, commonly known as symbolic regression. Results show that
SNIP effectively transfers to various tasks, consistently outperforming fully
supervised baselines and competing strongly with established task-specific
methods, especially in few-shot learning scenarios where available data is
limited. | [
"Kazem Meidani",
"Parshin Shojaee",
"Chandan K. Reddy",
"Amir Barati Farimani"
] | 2023-10-03 17:32:44 | http://arxiv.org/abs/2310.02227v2 | http://arxiv.org/pdf/2310.02227v2 | 2310.02227v2 |
Think before you speak: Training Language Models With Pause Tokens | Language models generate responses by producing a series of tokens in
immediate succession: the $(K+1)^{th}$ token is an outcome of manipulating $K$
hidden vectors per layer, one vector per preceding token. What if instead we
were to let the model manipulate say, $K+10$ hidden vectors, before it outputs
the $(K+1)^{th}$ token? We operationalize this idea by performing training and
inference on language models with a (learnable) $\textit{pause}$ token, a
sequence of which is appended to the input prefix. We then delay extracting the
model's outputs until the last pause token is seen, thereby allowing the model
to process extra computation before committing to an answer. We empirically
evaluate $\textit{pause-training}$ on decoder-only models of 1B and 130M
parameters with causal pretraining on C4, and on downstream tasks covering
reasoning, question-answering, general understanding and fact recall. Our main
finding is that inference-time delays show gains when the model is both
pre-trained and finetuned with delays. For the 1B model, we witness gains on 8
of 9 tasks, most prominently, a gain of $18\%$ EM score on the QA task of
SQuAD, $8\%$ on CommonSenseQA and $1\%$ accuracy on the reasoning task of
GSM8k. Our work raises a range of conceptual and practical future research
questions on making delayed next-token prediction a widely applicable new
paradigm. | [
"Sachin Goyal",
"Ziwei Ji",
"Ankit Singh Rawat",
"Aditya Krishna Menon",
"Sanjiv Kumar",
"Vaishnavh Nagarajan"
] | 2023-10-03 17:32:41 | http://arxiv.org/abs/2310.02226v1 | http://arxiv.org/pdf/2310.02226v1 | 2310.02226v1 |
Structurally guided task decomposition in spatial navigation tasks | How are people able to plan so efficiently despite limited cognitive
resources? We aimed to answer this question by extending an existing model of
human task decomposition that can explain a wide range of simple planning
problems by adding structure information to the task to facilitate planning in
more complex tasks. The extended model was then applied to a more complex
planning domain of spatial navigation. Our results suggest that our framework
can correctly predict the navigation strategies of the majority of the
participants in an online experiment. | [
"Ruiqi He",
"Carlos G. Correa",
"Thomas L. Griffiths",
"Mark K. Ho"
] | 2023-10-03 17:27:30 | http://arxiv.org/abs/2310.02221v1 | http://arxiv.org/pdf/2310.02221v1 | 2310.02221v1 |
What do we learn from a large-scale study of pre-trained visual representations in sim and real environments? | We present a large empirical investigation on the use of pre-trained visual
representations (PVRs) for training downstream policies that execute real-world
tasks. Our study spans five different PVRs, two different policy-learning
paradigms (imitation and reinforcement learning), and three different robots
for 5 distinct manipulation and indoor navigation tasks. From this effort, we
can arrive at three insights: 1) the performance trends of PVRs in the
simulation are generally indicative of their trends in the real world, 2) the
use of PVRs enables a first-of-its-kind result with indoor ImageNav (zero-shot
transfer to a held-out scene in the real world), and 3) the benefits from
variations in PVRs, primarily data-augmentation and fine-tuning, also transfer
to the real-world performance. See project website for additional details and
visuals. | [
"Sneha Silwal",
"Karmesh Yadav",
"Tingfan Wu",
"Jay Vakil",
"Arjun Majumdar",
"Sergio Arnaud",
"Claire Chen",
"Vincent-Pierre Berges",
"Dhruv Batra",
"Aravind Rajeswaran",
"Mrinal Kalakrishnan",
"Franziska Meier",
"Oleksandr Maksymets"
] | 2023-10-03 17:27:10 | http://arxiv.org/abs/2310.02219v1 | http://arxiv.org/pdf/2310.02219v1 | 2310.02219v1 |
An experimental system for detection and localization of hemorrhage using ultra-wideband microwaves with deep learning | Stroke is a leading cause of mortality and disability. Emergent diagnosis and
intervention are critical, and predicated upon initial brain imaging; however,
existing clinical imaging modalities are generally costly, immobile, and demand
highly specialized operation and interpretation. Low-energy microwaves have
been explored as low-cost, small form factor, fast, and safe probes of tissue
dielectric properties, with both imaging and diagnostic potential.
Nevertheless, challenges inherent to microwave reconstruction have impeded
progress, hence microwave imaging (MWI) remains an elusive scientific aim.
Herein, we introduce a dedicated experimental framework comprising a robotic
navigation system to translate blood-mimicking phantoms within an anatomically
realistic human head model. An 8-element ultra-wideband (UWB) array of modified
antipodal Vivaldi antennas was developed and driven by a two-port vector
network analyzer spanning 0.6-9.0 GHz at an operating power of 1 mw. Complex
scattering parameters were measured, and dielectric signatures of hemorrhage
were learned using a dedicated deep neural network for prediction of hemorrhage
classes and localization. An overall sensitivity and specificity for detection
>0.99 was observed, with Rayliegh mean localization error of 1.65 mm. The study
establishes the feasibility of a robust experimental model and deep learning
solution for UWB microwave stroke detection. | [
"Eisa Hedayati",
"Fatemeh Safari",
"George Verghese",
"Vito R. Ciancia",
"Daniel K. Sodickson",
"Seena Dehkharghani",
"Leeor Alon"
] | 2023-10-03 17:17:44 | http://arxiv.org/abs/2310.02215v1 | http://arxiv.org/pdf/2310.02215v1 | 2310.02215v1 |
Language Models Represent Space and Time | The capabilities of large language models (LLMs) have sparked debate over
whether such systems just learn an enormous collection of superficial
statistics or a coherent model of the data generating process -- a world model.
We find evidence for the latter by analyzing the learned representations of
three spatial datasets (world, US, NYC places) and three temporal datasets
(historical figures, artworks, news headlines) in the Llama-2 family of models.
We discover that LLMs learn linear representations of space and time across
multiple scales. These representations are robust to prompting variations and
unified across different entity types (e.g. cities and landmarks). In addition,
we identify individual ``space neurons'' and ``time neurons'' that reliably
encode spatial and temporal coordinates. Our analysis demonstrates that modern
LLMs acquire structured knowledge about fundamental dimensions such as space
and time, supporting the view that they learn not merely superficial
statistics, but literal world models. | [
"Wes Gurnee",
"Max Tegmark"
] | 2023-10-03 17:06:52 | http://arxiv.org/abs/2310.02207v1 | http://arxiv.org/pdf/2310.02207v1 | 2310.02207v1 |
Chunking: Forgetting Matters in Continual Learning even without Changing Tasks | Work on continual learning (CL) has largely focused on the problems arising
from the dynamically-changing data distribution. However, CL can be decomposed
into two sub-problems: (a) shifts in the data distribution, and (b) dealing
with the fact that the data is split into chunks and so only a part of the data
is available to be trained on at any point in time. In this work, we look at
the latter sub-problem -- the chunking of data -- and note that previous
analysis of chunking in the CL literature is sparse. We show that chunking is
an important part of CL, accounting for around half of the performance drop
from offline learning in our experiments. Furthermore, our results reveal that
current CL algorithms do not address the chunking sub-problem, only performing
as well as plain SGD training when there is no shift in the data distribution.
We analyse why performance drops when learning occurs on chunks of data, and
find that forgetting, which is often seen to be a problem due to distribution
shift, still arises and is a significant problem. Motivated by an analysis of
the linear case, we show that per-chunk weight averaging improves performance
in the chunking setting and that this performance transfers to the full CL
setting. Hence, we argue that work on chunking can help advance CL in general. | [
"Thomas L. Lee",
"Amos Storkey"
] | 2023-10-03 17:04:33 | http://arxiv.org/abs/2310.02206v1 | http://arxiv.org/pdf/2310.02206v1 | 2310.02206v1 |
Uncertainty Quantification in Inverse Models in Hydrology | In hydrology, modeling streamflow remains a challenging task due to the
limited availability of basin characteristics information such as soil geology
and geomorphology. These characteristics may be noisy due to measurement errors
or may be missing altogether. To overcome this challenge, we propose a
knowledge-guided, probabilistic inverse modeling method for recovering physical
characteristics from streamflow and weather data, which are more readily
available. We compare our framework with state-of-the-art inverse models for
estimating river basin characteristics. We also show that these estimates offer
improvement in streamflow modeling as opposed to using the original basin
characteristic values. Our inverse model offers 3\% improvement in R$^2$ for
the inverse model (basin characteristic estimation) and 6\% for the forward
model (streamflow prediction). Our framework also offers improved
explainability since it can quantify uncertainty in both the inverse and the
forward model. Uncertainty quantification plays a pivotal role in improving the
explainability of machine learning models by providing additional insights into
the reliability and limitations of model predictions. In our analysis, we
assess the quality of the uncertainty estimates. Compared to baseline
uncertainty quantification methods, our framework offers 10\% improvement in
the dispersion of epistemic uncertainty and 13\% improvement in coverage rate.
This information can help stakeholders understand the level of uncertainty
associated with the predictions and provide a more comprehensive view of the
potential outcomes. | [
"Somya Sharma Chatterjee",
"Rahul Ghosh",
"Arvind Renganathan",
"Xiang Li",
"Snigdhansu Chatterjee",
"John Nieber",
"Christopher Duffy",
"Vipin Kumar"
] | 2023-10-03 16:39:21 | http://arxiv.org/abs/2310.02193v1 | http://arxiv.org/pdf/2310.02193v1 | 2310.02193v1 |
Ask Again, Then Fail: Large Language Models' Vacillations in Judgement | With the emergence of generative conversational large language models (LLMs)
like ChatGPT, serving as virtual assistants in various fields, the stability
and reliability of their responses have become crucial. However, during usage,
it has been observed that these models tend to waver in their judgements when
confronted with follow-up questions from users expressing skepticism or
disagreement. In this work, we draw inspiration from questioning strategies in
education and propose a \textsc{Follow-up Questioning Mechanism} along with two
evaluation metrics to assess the judgement consistency of LLMs before and after
exposure to disturbances. We evaluate the judgement consistency of ChatGPT,
PaLM2-Bison, and Vicuna-13B under this mechanism across eight reasoning
benchmarks. Empirical results show that even when the initial answers are
correct, judgement consistency sharply decreases when LLMs face disturbances
such as questioning, negation, or misleading. Additionally, we study these
models' judgement consistency under various settings (sampling temperature and
prompts) to validate this issue further, observing the impact of prompt tone
and conducting an in-depth error analysis for deeper behavioral insights.
Furthermore, we also explore several prompting methods to mitigate this issue
and demonstrate their
effectiveness\footnote{\url{https://github.com/NUSTM/LLMs-Waver-In-Judgements}}. | [
"Qiming Xie",
"Zengzhi Wang",
"Yi Feng",
"Rui Xia"
] | 2023-10-03 16:08:41 | http://arxiv.org/abs/2310.02174v1 | http://arxiv.org/pdf/2310.02174v1 | 2310.02174v1 |
Lyfe Agents: Generative agents for low-cost real-time social interactions | Highly autonomous generative agents powered by large language models promise
to simulate intricate social behaviors in virtual societies. However, achieving
real-time interactions with humans at a low computational cost remains
challenging. Here, we introduce Lyfe Agents. They combine low-cost with
real-time responsiveness, all while remaining intelligent and goal-oriented.
Key innovations include: (1) an option-action framework, reducing the cost of
high-level decisions; (2) asynchronous self-monitoring for better
self-consistency; and (3) a Summarize-and-Forget memory mechanism, prioritizing
critical memory items at a low cost. We evaluate Lyfe Agents' self-motivation
and sociability across several multi-agent scenarios in our custom LyfeGame 3D
virtual environment platform. When equipped with our brain-inspired techniques,
Lyfe Agents can exhibit human-like self-motivated social reasoning. For
example, the agents can solve a crime (a murder mystery) through autonomous
collaboration and information exchange. Meanwhile, our techniques enabled Lyfe
Agents to operate at a computational cost 10-100 times lower than existing
alternatives. Our findings underscore the transformative potential of
autonomous generative agents to enrich human social experiences in virtual
worlds. | [
"Zhao Kaiya",
"Michelangelo Naim",
"Jovana Kondic",
"Manuel Cortes",
"Jiaxin Ge",
"Shuying Luo",
"Guangyu Robert Yang",
"Andrew Ahn"
] | 2023-10-03 16:06:30 | http://arxiv.org/abs/2310.02172v1 | http://arxiv.org/pdf/2310.02172v1 | 2310.02172v1 |
Editing Personality for LLMs | This paper introduces an innovative task focused on editing the personality
traits of Large Language Models (LLMs). This task seeks to adjust the models'
responses to opinion-related questions on specified topics since an
individual's personality often manifests in the form of their expressed
opinions, thereby showcasing different personality traits. Specifically, we
construct a new benchmark dataset PersonalityEdit to address this task. Drawing
on the theory in Social Psychology, we isolate three representative traits,
namely Neuroticism, Extraversion, and Agreeableness, as the foundation for our
benchmark. We then gather data using GPT-4, generating responses that not only
align with a specified topic but also embody the targeted personality trait. We
conduct comprehensive experiments involving various baselines and discuss the
representation of personality behavior in LLMs. Our intriguing findings uncover
potential challenges of the proposed task, illustrating several remaining
issues. We anticipate that our work can provide the NLP community with
insights. Code and datasets will be released at
https://github.com/zjunlp/EasyEdit. | [
"Shengyu Mao",
"Ningyu Zhang",
"Xiaohan Wang",
"Mengru Wang",
"Yunzhi Yao",
"Yong Jiang",
"Pengjun Xie",
"Fei Huang",
"Huajun Chen"
] | 2023-10-03 16:02:36 | http://arxiv.org/abs/2310.02168v1 | http://arxiv.org/pdf/2310.02168v1 | 2310.02168v1 |
Probabilistically Rewired Message-Passing Neural Networks | Message-passing graph neural networks (MPNNs) emerged as powerful tools for
processing graph-structured input. However, they operate on a fixed input graph
structure, ignoring potential noise and missing information. Furthermore, their
local aggregation mechanism can lead to problems such as over-squashing and
limited expressive power in capturing relevant graph structures. Existing
solutions to these challenges have primarily relied on heuristic methods, often
disregarding the underlying data distribution. Hence, devising principled
approaches for learning to infer graph structures relevant to the given
prediction task remains an open challenge. In this work, leveraging recent
progress in exact and differentiable $k$-subset sampling, we devise
probabilistically rewired MPNNs (PR-MPNNs), which learn to add relevant edges
while omitting less beneficial ones. For the first time, our theoretical
analysis explores how PR-MPNNs enhance expressive power, and we identify
precise conditions under which they outperform purely randomized approaches.
Empirically, we demonstrate that our approach effectively mitigates issues like
over-squashing and under-reaching. In addition, on established real-world
datasets, our method exhibits competitive or superior predictive performance
compared to traditional MPNN models and recent graph transformer architectures. | [
"Chendi Qian",
"Andrei Manolache",
"Kareem Ahmed",
"Zhe Zeng",
"Guy Van den Broeck",
"Mathias Niepert",
"Christopher Morris"
] | 2023-10-03 15:43:59 | http://arxiv.org/abs/2310.02156v3 | http://arxiv.org/pdf/2310.02156v3 | 2310.02156v3 |
Graph Neural Network-based EEG Classification: A Survey | Graph neural networks (GNN) are increasingly used to classify EEG for tasks
such as emotion recognition, motor imagery and neurological diseases and
disorders. A wide range of methods have been proposed to design GNN-based
classifiers. Therefore, there is a need for a systematic review and
categorisation of these approaches. We exhaustively search the published
literature on this topic and derive several categories for comparison. These
categories highlight the similarities and differences among the methods. The
results suggest a prevalence of spectral graph convolutional layers over
spatial. Additionally, we identify standard forms of node features, with the
most popular being the raw EEG signal and differential entropy. Our results
summarise the emerging trends in GNN-based approaches for EEG classification.
Finally, we discuss several promising research directions, such as exploring
the potential of transfer learning methods and appropriate modelling of
cross-frequency interactions. | [
"Dominik Klepl",
"Min Wu",
"Fei He"
] | 2023-10-03 15:40:03 | http://arxiv.org/abs/2310.02152v1 | http://arxiv.org/pdf/2310.02152v1 | 2310.02152v1 |
Finite-Time Analysis of Whittle Index based Q-Learning for Restless Multi-Armed Bandits with Neural Network Function Approximation | Whittle index policy is a heuristic to the intractable restless multi-armed
bandits (RMAB) problem. Although it is provably asymptotically optimal, finding
Whittle indices remains difficult. In this paper, we present Neural-Q-Whittle,
a Whittle index based Q-learning algorithm for RMAB with neural network
function approximation, which is an example of nonlinear two-timescale
stochastic approximation with Q-function values updated on a faster timescale
and Whittle indices on a slower timescale. Despite the empirical success of
deep Q-learning, the non-asymptotic convergence rate of Neural-Q-Whittle, which
couples neural networks with two-timescale Q-learning largely remains unclear.
This paper provides a finite-time analysis of Neural-Q-Whittle, where data are
generated from a Markov chain, and Q-function is approximated by a ReLU neural
network. Our analysis leverages a Lyapunov drift approach to capture the
evolution of two coupled parameters, and the nonlinearity in value function
approximation further requires us to characterize the approximation error.
Combing these provide Neural-Q-Whittle with $\mathcal{O}(1/k^{2/3})$
convergence rate, where $k$ is the number of iterations. | [
"Guojun Xiong",
"Jian Li"
] | 2023-10-03 15:34:21 | http://arxiv.org/abs/2310.02147v1 | http://arxiv.org/pdf/2310.02147v1 | 2310.02147v1 |
Learning Reliable Logical Rules with SATNet | Bridging logical reasoning and deep learning is crucial for advanced AI
systems. In this work, we present a new framework that addresses this goal by
generating interpretable and verifiable logical rules through differentiable
learning, without relying on pre-specified logical structures. Our approach
builds upon SATNet, a differentiable MaxSAT solver that learns the underlying
rules from input-output examples. Despite its efficacy, the learned weights in
SATNet are not straightforwardly interpretable, failing to produce
human-readable rules. To address this, we propose a novel specification method
called "maximum equality", which enables the interchangeability between the
learned weights of SATNet and a set of propositional logical rules in weighted
MaxSAT form. With the decoded weighted MaxSAT formula, we further introduce
several effective verification techniques to validate it against the ground
truth rules. Experiments on stream transformations and Sudoku problems show
that our decoded rules are highly reliable: using exact solvers on them could
achieve 100% accuracy, whereas the original SATNet fails to give correct
solutions in many cases. Furthermore, we formally verify that our decoded
logical rules are functionally equivalent to the ground truth ones. | [
"Zhaoyu Li",
"Jinpei Guo",
"Yuhe Jiang",
"Xujie Si"
] | 2023-10-03 15:14:28 | http://arxiv.org/abs/2310.02133v1 | http://arxiv.org/pdf/2310.02133v1 | 2310.02133v1 |
Unveiling the Pitfalls of Knowledge Editing for Large Language Models | As the cost associated with fine-tuning Large Language Models (LLMs)
continues to rise, recent research efforts have pivoted towards developing
methodologies to edit implicit knowledge embedded within LLMs. Yet, there's
still a dark cloud lingering overhead -- will knowledge editing trigger
butterfly effect? since it is still unclear whether knowledge editing might
introduce side effects that pose potential risks or not. This paper pioneers
the investigation into the potential pitfalls associated with knowledge editing
for LLMs. To achieve this, we introduce new benchmark datasets and propose
innovative evaluation metrics. Our results underline two pivotal concerns: (1)
Knowledge Conflict: Editing groups of facts that logically clash can magnify
the inherent inconsistencies in LLMs-a facet neglected by previous methods. (2)
Knowledge Distortion: Altering parameters with the aim of editing factual
knowledge can irrevocably warp the innate knowledge structure of LLMs.
Experimental results vividly demonstrate that knowledge editing might
inadvertently cast a shadow of unintended consequences on LLMs, which warrant
attention and efforts for future works. Code will be released at
https://github.com/zjunlp/PitfallsKnowledgeEditing. | [
"Zhoubo Li",
"Ningyu Zhang",
"Yunzhi Yao",
"Mengru Wang",
"Xi Chen",
"Huajun Chen"
] | 2023-10-03 15:10:46 | http://arxiv.org/abs/2310.02129v1 | http://arxiv.org/pdf/2310.02129v1 | 2310.02129v1 |
Exploring Collaboration Mechanisms for LLM Agents: A Social Psychology View | As Natural Language Processing (NLP) systems are increasingly employed in
intricate social environments, a pressing query emerges: Can these NLP systems
mirror human-esque collaborative intelligence, in a multi-agent society
consisting of multiple large language models (LLMs)? This paper probes the
collaboration mechanisms among contemporary NLP systems by melding practical
experiments with theoretical insights. We fabricate four unique `societies'
comprised of LLM agents, where each agent is characterized by a specific
`trait' (easy-going or overconfident) and engages in collaboration with a
distinct `thinking pattern' (debate or reflection). Evaluating these
multi-agent societies on three benchmark datasets, we discern that LLM agents
navigate tasks by leveraging diverse social behaviors, from active debates to
introspective reflections. Notably, certain collaborative strategies only
optimize efficiency (using fewer API tokens), but also outshine previous
top-tier approaches. Moreover, our results further illustrate that LLM agents
manifest human-like social behaviors, such as conformity or majority rule,
mirroring foundational Social Psychology theories. In conclusion, we integrate
insights from Social Psychology to contextualize the collaboration of LLM
agents, inspiring further investigations into the collaboration mechanism for
LLMs. We commit to sharing our code and datasets (already submitted in
supplementary materials), hoping to catalyze further research in this promising
avenue (All code and data are available at
\url{https://github.com/zjunlp/MachineSoM}.). | [
"Jintian Zhang",
"Xin Xu",
"Shumin Deng"
] | 2023-10-03 15:05:52 | http://arxiv.org/abs/2310.02124v1 | http://arxiv.org/pdf/2310.02124v1 | 2310.02124v1 |
Symmetric Single Index Learning | Few neural architectures lend themselves to provable learning with gradient
based methods. One popular model is the single-index model, in which labels are
produced by composing an unknown linear projection with a possibly unknown
scalar link function. Learning this model with SGD is relatively
well-understood, whereby the so-called information exponent of the link
function governs a polynomial sample complexity rate. However, extending this
analysis to deeper or more complicated architectures remains challenging.
In this work, we consider single index learning in the setting of symmetric
neural networks. Under analytic assumptions on the activation and maximum
degree assumptions on the link function, we prove that gradient flow recovers
the hidden planted direction, represented as a finitely supported vector in the
feature space of power sum polynomials. We characterize a notion of information
exponent adapted to our setting that controls the efficiency of learning. | [
"Aaron Zweig",
"Joan Bruna"
] | 2023-10-03 14:59:00 | http://arxiv.org/abs/2310.02117v1 | http://arxiv.org/pdf/2310.02117v1 | 2310.02117v1 |
Hierarchical Concept Discovery Models: A Concept Pyramid Scheme | Deep Learning algorithms have recently gained significant attention due to
their impressive performance. However, their high complexity and
un-interpretable mode of operation hinders their confident deployment in
real-world safety-critical tasks. This work targets ante hoc interpretability,
and specifically Concept Bottleneck Models (CBMs). Our goal is to design a
framework that admits a highly interpretable decision making process with
respect to human understandable concepts, on multiple levels of granularity. To
this end, we propose a novel hierarchical concept discovery formulation
leveraging: (i) recent advances in image-text models, and (ii) an innovative
formulation for multi-level concept selection via data-driven and sparsity
inducing Bayesian arguments. Within this framework, concept information does
not solely rely on the similarity between the whole image and general
unstructured concepts; instead, we introduce the notion of concept hierarchy to
uncover and exploit more granular concept information residing in
patch-specific regions of the image scene. As we experimentally show, the
proposed construction not only outperforms recent CBM approaches, but also
yields a principled framework towards interpetability. | [
"Konstantinos P. Panousis",
"Dino Ienco",
"Diego Marcos"
] | 2023-10-03 14:57:31 | http://arxiv.org/abs/2310.02116v1 | http://arxiv.org/pdf/2310.02116v1 | 2310.02116v1 |
FLEDGE: Ledger-based Federated Learning Resilient to Inference and Backdoor Attacks | Federated learning (FL) is a distributed learning process that uses a trusted
aggregation server to allow multiple parties (or clients) to collaboratively
train a machine learning model without having them share their private data.
Recent research, however, has demonstrated the effectiveness of inference and
poisoning attacks on FL. Mitigating both attacks simultaneously is very
challenging. State-of-the-art solutions have proposed the use of poisoning
defenses with Secure Multi-Party Computation (SMPC) and/or Differential Privacy
(DP). However, these techniques are not efficient and fail to address the
malicious intent behind the attacks, i.e., adversaries (curious servers and/or
compromised clients) seek to exploit a system for monetization purposes. To
overcome these limitations, we present a ledger-based FL framework known as
FLEDGE that allows making parties accountable for their behavior and achieve
reasonable efficiency for mitigating inference and poisoning attacks. Our
solution leverages crypto-currency to increase party accountability by
penalizing malicious behavior and rewarding benign conduct. We conduct an
extensive evaluation on four public datasets: Reddit, MNIST, Fashion-MNIST, and
CIFAR-10. Our experimental results demonstrate that (1) FLEDGE provides strong
privacy guarantees for model updates without sacrificing model utility; (2)
FLEDGE can successfully mitigate different poisoning attacks without degrading
the performance of the global model; and (3) FLEDGE offers unique reward
mechanisms to promote benign behavior during model training and/or model
aggregation. | [
"Jorge Castillo",
"Phillip Rieger",
"Hossein Fereidooni",
"Qian Chen",
"Ahmad Sadeghi"
] | 2023-10-03 14:55:30 | http://arxiv.org/abs/2310.02113v1 | http://arxiv.org/pdf/2310.02113v1 | 2310.02113v1 |
CoNO: Complex Neural Operator for Continuous Dynamical Systems | Neural operators extend data-driven models to map between
infinite-dimensional functional spaces. These models have successfully solved
continuous dynamical systems represented by differential equations, viz weather
forecasting, fluid flow, or solid mechanics. However, the existing operators
still rely on real space, thereby losing rich representations potentially
captured in the complex space by functional transforms. In this paper, we
introduce a Complex Neural Operator (CoNO), that parameterizes the integral
kernel in the complex fractional Fourier domain. Additionally, the model
employing a complex-valued neural network along with aliasing-free activation
functions preserves the complex values and complex algebraic properties,
thereby enabling improved representation, robustness to noise, and
generalization. We show that the model effectively captures the underlying
partial differential equation with a single complex fractional Fourier
transform. We perform an extensive empirical evaluation of CoNO on several
datasets and additional tasks such as zero-shot super-resolution, evaluation of
out-of-distribution data, data efficiency, and robustness to noise. CoNO
exhibits comparable or superior performance to all the state-of-the-art models
in these tasks. Altogether, CoNO presents a robust and superior model for
modeling continuous dynamical systems, providing a fillip to scientific machine
learning. | [
"Karn Tiwari",
"N M Anoop Krishnan",
"Prathosh A P"
] | 2023-10-03 14:38:12 | http://arxiv.org/abs/2310.02094v2 | http://arxiv.org/pdf/2310.02094v2 | 2310.02094v2 |
Stochastic Gradient Descent with Preconditioned Polyak Step-size | Stochastic Gradient Descent (SGD) is one of the many iterative optimization
methods that are widely used in solving machine learning problems. These
methods display valuable properties and attract researchers and industrial
machine learning engineers with their simplicity. However, one of the
weaknesses of this type of methods is the necessity to tune learning rate
(step-size) for every loss function and dataset combination to solve an
optimization problem and get an efficient performance in a given time budget.
Stochastic Gradient Descent with Polyak Step-size (SPS) is a method that offers
an update rule that alleviates the need of fine-tuning the learning rate of an
optimizer. In this paper, we propose an extension of SPS that employs
preconditioning techniques, such as Hutchinson's method, Adam, and AdaGrad, to
improve its performance on badly scaled and/or ill-conditioned datasets. | [
"Farshed Abdukhakimov",
"Chulu Xiang",
"Dmitry Kamzolov",
"Martin Takáč"
] | 2023-10-03 14:36:05 | http://arxiv.org/abs/2310.02093v1 | http://arxiv.org/pdf/2310.02093v1 | 2310.02093v1 |
1D-CapsNet-LSTM: A Deep Learning-Based Model for Multi-Step Stock Index Forecasting | Multi-step forecasting of stock market index prices is a crucial task in the
financial sector, playing a pivotal role in decision-making across various
financial activities. However, forecasting results are often unsatisfactory
owing to the stochastic and volatile nature of the data. Researchers have made
various attempts, and this process is ongoing. Inspired by convolutional neural
network long short-term memory (CNN-LSTM) networks that utilize a 1D CNN for
feature extraction to boost model performance, this study explores the use of a
capsule network (CapsNet) as an advanced feature extractor in an LSTM-based
forecasting model to enhance multi-step predictions. To this end, a novel
neural architecture called 1D-CapsNet-LSTM was introduced, which combines a 1D
CapsNet to extract high-level features from 1D sequential data and an LSTM
layer to capture the temporal dependencies between the previously extracted
features and uses a multi-input multi-output (MIMO) strategy to maintain the
stochastic dependencies between the predicted values at different time steps.
The proposed model was evaluated based on several real-world stock market
indices, including Standard & Poor's 500 (S&P 500), Dow Jones Industrial
Average (DJIA), Nasdaq Composite Index (IXIC), and New York Stock Exchange
(NYSE), and was compared with baseline models such as LSTM, recurrent neural
network (RNN), and CNN-LSTM in terms of various evaluation metrics. The
comparison results suggest that the 1D-CapsNet-LSTM model outperforms the
baseline models and has immense potential for the effective handling of complex
prediction tasks. | [
"Cheng Zhang",
"Nilam Nur Amir Sjarif",
"Roslina Ibrahim"
] | 2023-10-03 14:33:34 | http://arxiv.org/abs/2310.02090v1 | http://arxiv.org/pdf/2310.02090v1 | 2310.02090v1 |
Learning Quantum Processes with Quantum Statistical Queries | Learning complex quantum processes is a central challenge in many areas of
quantum computing and quantum machine learning, with applications in quantum
benchmarking, cryptanalysis, and variational quantum algorithms. This paper
introduces the first learning framework for studying quantum process learning
within the Quantum Statistical Query (QSQ) model, providing the first formal
definition of statistical queries to quantum processes (QPSQs). The framework
allows us to propose an efficient QPSQ learner for arbitrary quantum processes
accompanied by a provable performance guarantee. We also provide numerical
simulations to demonstrate the efficacy of this algorithm. The practical
relevance of this framework is exemplified through application in
cryptanalysis, highlighting vulnerabilities of Classical-Readout Quantum
Physical Unclonable Functions (CR-QPUFs), addressing an important open question
in the field of quantum hardware security. This work marks a significant step
towards understanding the learnability of quantum processes and shedding light
on their security implications. | [
"Chirag Wadhwa",
"Mina Doosti"
] | 2023-10-03 14:15:20 | http://arxiv.org/abs/2310.02075v1 | http://arxiv.org/pdf/2310.02075v1 | 2310.02075v1 |
ACE: A fast, skillful learned global atmospheric model for climate prediction | Existing ML-based atmospheric models are not suitable for climate prediction,
which requires long-term stability and physical consistency. We present ACE
(AI2 Climate Emulator), a 200M-parameter, autoregressive machine learning
emulator of an existing comprehensive 100-km resolution global atmospheric
model. The formulation of ACE allows evaluation of physical laws such as the
conservation of mass and moisture. The emulator is stable for 10 years, nearly
conserves column moisture without explicit constraints and faithfully
reproduces the reference model's climate, outperforming a challenging baseline
on over 80% of tracked variables. ACE requires nearly 100x less wall clock time
and is 100x more energy efficient than the reference model using typically
available resources. | [
"Oliver Watt-Meyer",
"Gideon Dresdner",
"Jeremy McGibbon",
"Spencer K. Clark",
"Brian Henn",
"James Duncan",
"Noah D. Brenowitz",
"Karthik Kashinath",
"Michael S. Pritchard",
"Boris Bonev",
"Matthew E. Peters",
"Christopher S. Bretherton"
] | 2023-10-03 14:15:06 | http://arxiv.org/abs/2310.02074v1 | http://arxiv.org/pdf/2310.02074v1 | 2310.02074v1 |
De Novo Drug Design with Joint Transformers | De novo drug design requires simultaneously generating novel molecules
outside of training data and predicting their target properties, making it a
hard task for generative models. To address this, we propose Joint Transformer
that combines a Transformer decoder, a Transformer encoder, and a predictor in
a joint generative model with shared weights. We show that training the model
with a penalized log-likelihood objective results in state-of-the-art
performance in molecule generation, while decreasing the prediction error on
newly sampled molecules, as compared to a fine-tuned decoder-only Transformer,
by 42%. Finally, we propose a probabilistic black-box optimization algorithm
that employs Joint Transformer to generate novel molecules with improved target
properties, as compared to the training data, outperforming other SMILES-based
optimization methods in de novo drug design. | [
"Adam Izdebski",
"Ewelina Weglarz-Tomczak",
"Ewa Szczurek",
"Jakub M. Tomczak"
] | 2023-10-03 14:09:15 | http://arxiv.org/abs/2310.02066v1 | http://arxiv.org/pdf/2310.02066v1 | 2310.02066v1 |
VENOM: A Vectorized N:M Format for Unleashing the Power of Sparse Tensor Cores | The increasing success and scaling of Deep Learning models demands higher
computational efficiency and power. Sparsification can lead to both smaller
models as well as higher compute efficiency, and accelerated hardware is
becoming available. However, exploiting it efficiently requires kernel
implementations, pruning algorithms, and storage formats, to utilize hardware
support of specialized sparse vector units. An example of those are the
NVIDIA's Sparse Tensor Cores (SPTCs), which promise a 2x speedup. However,
SPTCs only support the 2:4 format, limiting achievable sparsity ratios to 50%.
We present the V:N:M format, which enables the execution of arbitrary N:M
ratios on SPTCs. To efficiently exploit the resulting format, we propose
Spatha, a high-performance sparse-library for DL routines. We show that Spatha
achieves up to 37x speedup over cuBLAS. We also demonstrate a second-order
pruning technique that enables sparsification to high sparsity ratios with
V:N:M and little to no loss in accuracy in modern transformers. | [
"Roberto L. Castro",
"Andrei Ivanov",
"Diego Andrade",
"Tal Ben-Nun",
"Basilio B. Fraguela",
"Torsten Hoefler"
] | 2023-10-03 14:08:26 | http://arxiv.org/abs/2310.02065v1 | http://arxiv.org/pdf/2310.02065v1 | 2310.02065v1 |
Lessons Learned from EXMOS User Studies: A Technical Report Summarizing Key Takeaways from User Studies Conducted to Evaluate The EXMOS Platform | In the realm of interactive machine-learning systems, the provision of
explanations serves as a vital aid in the processes of debugging and enhancing
prediction models. However, the extent to which various global model-centric
and data-centric explanations can effectively assist domain experts in
detecting and resolving potential data-related issues for the purpose of model
improvement has remained largely unexplored. In this technical report, we
summarise the key findings of our two user studies. Our research involved a
comprehensive examination of the impact of global explanations rooted in both
data-centric and model-centric perspectives within systems designed to support
healthcare experts in optimising machine learning models through both automated
and manual data configurations. To empirically investigate these dynamics, we
conducted two user studies, comprising quantitative analysis involving a sample
size of 70 healthcare experts and qualitative assessments involving 30
healthcare experts. These studies were aimed at illuminating the influence of
different explanation types on three key dimensions: trust, understandability,
and model improvement. Results show that global model-centric explanations
alone are insufficient for effectively guiding users during the intricate
process of data configuration. In contrast, data-centric explanations exhibited
their potential by enhancing the understanding of system changes that occur
post-configuration. However, a combination of both showed the highest level of
efficacy for fostering trust, improving understandability, and facilitating
model enhancement among healthcare experts. We also present essential
implications for developing interactive machine-learning systems driven by
explanations. These insights can guide the creation of more effective systems
that empower domain experts to harness the full potential of machine learning | [
"Aditya Bhattacharya",
"Simone Stumpf",
"Lucija Gosak",
"Gregor Stiglic",
"Katrien Verbert"
] | 2023-10-03 14:04:45 | http://arxiv.org/abs/2310.02063v1 | http://arxiv.org/pdf/2310.02063v1 | 2310.02063v1 |
Relaxed Octahedral Group Convolution for Learning Symmetry Breaking in 3D Physical Systems | Deep equivariant models use symmetries to improve sample efficiency and
generalization. However, the assumption of perfect symmetry in many of these
models can sometimes be restrictive, especially when the data does not
perfectly align with such symmetries. Thus, we introduce relaxed octahedral
group convolution for modeling 3D physical systems in this paper. This flexible
convolution technique provably allows the model to both maintain the highest
level of equivariance that is consistent with data and discover the subtle
symmetry-breaking factors in the physical systems. Empirical results validate
that our approach can not only provide insights into the symmetry-breaking
factors in phase transitions but also achieves superior performance in fluid
super-resolution tasks. | [
"Rui Wang",
"Robin Walters",
"Tess E. Smidt"
] | 2023-10-03 14:03:21 | http://arxiv.org/abs/2310.02299v2 | http://arxiv.org/pdf/2310.02299v2 | 2310.02299v2 |
The Inhibitor: ReLU and Addition-Based Attention for Efficient Transformers | To enhance the computational efficiency of quantized Transformers, we replace
the dot-product and Softmax-based attention with an alternative mechanism
involving addition and ReLU activation only. This side-steps the expansion to
double precision often required by matrix multiplication and avoids costly
Softmax evaluations but maintains much of the core functionality of
conventional dot-product attention. It can enable more efficient execution and
support larger quantized Transformer models on resource-constrained hardware or
alternative arithmetic systems like homomorphic encryption. Training
experiments on four common benchmark tasks show test set prediction scores
comparable to those of conventional Transformers with dot-product attention.
Our scaling experiments also suggest significant computational savings, both in
plaintext and under encryption. In particular, we believe that the ReLU and
addition-based attention mechanism introduced in this paper may enable
privacy-preserving AI applications operating under homomorphic encryption by
avoiding the costly multiplication of encrypted variables. | [
"Rickard Brännvall"
] | 2023-10-03 13:34:21 | http://arxiv.org/abs/2310.02041v1 | http://arxiv.org/pdf/2310.02041v1 | 2310.02041v1 |
aSAGA: Automatic Sleep Analysis with Gray Areas | State-of-the-art automatic sleep staging methods have already demonstrated
comparable reliability and superior time efficiency to manual sleep staging.
However, fully automatic black-box solutions are difficult to adapt into
clinical workflow and the interaction between explainable automatic methods and
the work of sleep technologists remains underexplored and inadequately
conceptualized. Thus, we propose a human-in-the-loop concept for sleep
analysis, presenting an automatic sleep staging model (aSAGA), that performs
effectively with both clinical polysomnographic recordings and home sleep
studies. To validate the model, extensive testing was conducted, employing a
preclinical validation approach with three retrospective datasets; open-access,
clinical, and research-driven. Furthermore, we validate the utilization of
uncertainty mapping to identify ambiguous regions, conceptualized as gray
areas, in automatic sleep analysis that warrants manual re-evaluation. The
results demonstrate that the automatic sleep analysis achieved a comparable
level of agreement with manual analysis across different sleep recording types.
Moreover, validation of the gray area concept revealed its potential to enhance
sleep staging accuracy and identify areas in the recordings where sleep
technologists struggle to reach a consensus. In conclusion, this study
introduces and validates a concept from explainable artificial intelligence
into sleep medicine and provides the basis for integrating human-in-the-loop
automatic sleep staging into clinical workflows, aiming to reduce black-box
criticism and the burden associated with manual sleep staging. | [
"Matias Rusanen",
"Gabriel Jouan",
"Riku Huttunen",
"Sami Nikkonen",
"Sigríður Sigurðardóttir",
"Juha Töyräs",
"Brett Duce",
"Sami Myllymaa",
"Erna Sif Arnardottir",
"Timo Leppänen",
"Anna Sigridur Islind",
"Samu Kainulainen",
"Henri Korkalainen"
] | 2023-10-03 13:17:38 | http://arxiv.org/abs/2310.02032v1 | http://arxiv.org/pdf/2310.02032v1 | 2310.02032v1 |
OceanGPT: A Large Language Model for Ocean Science Tasks | Ocean science, which delves into the oceans that are reservoirs of life and
biodiversity, is of great significance given that oceans cover over 70% of our
planet's surface. Recently, advances in Large Language Models (LLMs) have
transformed the paradigm in science. Despite the success in other domains,
current LLMs often fall short in catering to the needs of domain experts like
oceanographers, and the potential of LLMs for ocean science is under-explored.
The intrinsic reason may be the immense and intricate nature of ocean data as
well as the necessity for higher granularity and richness in knowledge. To
alleviate these issues, we introduce OceanGPT, the first-ever LLM in the ocean
domain, which is expert in various ocean science tasks. We propose DoInstruct,
a novel framework to automatically obtain a large volume of ocean domain
instruction data, which generates instructions based on multi-agent
collaboration. Additionally, we construct the first oceanography benchmark,
OceanBench, to evaluate the capabilities of LLMs in the ocean domain. Though
comprehensive experiments, OceanGPT not only shows a higher level of knowledge
expertise for oceans science tasks but also gains preliminary embodied
intelligence capabilities in ocean technology. Codes, data and checkpoints will
soon be available at https://github.com/zjunlp/KnowLM. | [
"Zhen Bi",
"Ningyu Zhang",
"Yida Xue",
"Yixin Ou",
"Daxiong Ji",
"Guozhou Zheng",
"Huajun Chen"
] | 2023-10-03 13:17:35 | http://arxiv.org/abs/2310.02031v3 | http://arxiv.org/pdf/2310.02031v3 | 2310.02031v3 |
Between accurate prediction and poor decision making: the AI/ML gap | Intelligent agents rely on AI/ML functionalities to predict the consequence
of possible actions and optimise the policy. However, the effort of the
research community in addressing prediction accuracy has been so intense (and
successful) that it created the illusion that the more accurate the learner
prediction (or classification) the better would have been the final decision.
Now, such an assumption is valid only if the (human or artificial) decision
maker has complete knowledge of the utility of the possible actions. This paper
argues that AI/ML community has taken so far a too unbalanced approach by
devoting excessive attention to the estimation of the state (or target)
probability to the detriment of accurate and reliable estimations of the
utility. In particular, few evidence exists about the impact of a wrong utility
assessment on the resulting expected utility of the decision strategy. This
situation is creating a substantial gap between the expectations and the
effective impact of AI solutions, as witnessed by recent criticisms and
emphasised by the regulatory legislative efforts. This paper aims to study this
gap by quantifying the sensitivity of the expected utility to the utility
uncertainty and comparing it to the one due to probability estimation.
Theoretical and simulated results show that an inaccurate utility assessment
may as (and sometimes) more harmful than a poor probability estimation. The
final recommendation to the community is then to undertake a focus shift from a
pure accuracy-driven (or obsessed) approach to a more utility-aware
methodology. | [
"Gianluca Bontempi"
] | 2023-10-03 13:15:02 | http://arxiv.org/abs/2310.02029v1 | http://arxiv.org/pdf/2310.02029v1 | 2310.02029v1 |
DeepHGCN: Toward Deeper Hyperbolic Graph Convolutional Networks | Hyperbolic graph convolutional networks (HGCN) have demonstrated significant
potential in extracting information from hierarchical graphs. However, existing
HGCNs are limited to shallow architectures, due to the expensive hyperbolic
operations and the over-smoothing issue as depth increases. Although in GCNs,
treatments have been applied to alleviate over-smoothing, developing a
hyperbolic therapy presents distinct challenges since operations should be
carefully designed to fit the hyperbolic nature. Addressing the above
challenges, in this work, we propose DeepHGCN, the first deep multi-layer HGCN
architecture with dramatically improved computational efficiency and
substantially alleviated over-smoothing effect. DeepHGCN presents two key
enablers of deep HGCNs: (1) a novel hyperbolic feature transformation layer
that enables fast and accurate linear maps; and (2) Techniques such as
hyperbolic residual connections and regularization for both weights and
features facilitated by an efficient hyperbolic midpoint method. Extensive
experiments demonstrate that DeepHGCN obtains significant improvements in link
prediction and node classification tasks compared to both Euclidean and shallow
hyperbolic GCN variants. | [
"Jiaxu Liu",
"Xinping Yi",
"Xiaowei Huang"
] | 2023-10-03 13:10:14 | http://arxiv.org/abs/2310.02027v2 | http://arxiv.org/pdf/2310.02027v2 | 2310.02027v2 |
DeepZero: Scaling up Zeroth-Order Optimization for Deep Model Training | Zeroth-order (ZO) optimization has become a popular technique for solving
machine learning (ML) problems when first-order (FO) information is difficult
or impossible to obtain. However, the scalability of ZO optimization remains an
open problem: Its use has primarily been limited to relatively small-scale ML
problems, such as sample-wise adversarial attack generation. To our best
knowledge, no prior work has demonstrated the effectiveness of ZO optimization
in training deep neural networks (DNNs) without a significant decrease in
performance. To overcome this roadblock, we develop DeepZero, a principled ZO
deep learning (DL) framework that can scale ZO optimization to DNN training
from scratch through three primary innovations. First, we demonstrate the
advantages of coordinate-wise gradient estimation (CGE) over randomized
vector-wise gradient estimation in training accuracy and computational
efficiency. Second, we propose a sparsity-induced ZO training protocol that
extends the model pruning methodology using only finite differences to explore
and exploit the sparse DL prior in CGE. Third, we develop the methods of
feature reuse and forward parallelization to advance the practical
implementations of ZO training. Our extensive experiments show that DeepZero
achieves state-of-the-art (SOTA) accuracy on ResNet-20 trained on CIFAR-10,
approaching FO training performance for the first time. Furthermore, we show
the practical utility of DeepZero in applications of certified adversarial
defense and DL-based partial differential equation error correction, achieving
10-20% improvement over SOTA. We believe our results will inspire future
research on scalable ZO optimization and contribute to advancing DL with black
box. | [
"Aochuan Chen",
"Yimeng Zhang",
"Jinghan Jia",
"James Diffenderfer",
"Jiancheng Liu",
"Konstantinos Parasyris",
"Yihua Zhang",
"Zheng Zhang",
"Bhavya Kailkhura",
"Sijia Liu"
] | 2023-10-03 13:05:36 | http://arxiv.org/abs/2310.02025v1 | http://arxiv.org/pdf/2310.02025v1 | 2310.02025v1 |
Nash Regret Guarantees for Linear Bandits | We obtain essentially tight upper bounds for a strengthened notion of regret
in the stochastic linear bandits framework. The strengthening -- referred to as
Nash regret -- is defined as the difference between the (a priori unknown)
optimum and the geometric mean of expected rewards accumulated by the linear
bandit algorithm. Since the geometric mean corresponds to the well-studied Nash
social welfare (NSW) function, this formulation quantifies the performance of a
bandit algorithm as the collective welfare it generates across rounds. NSW is
known to satisfy fairness axioms and, hence, an upper bound on Nash regret
provides a principled fairness guarantee.
We consider the stochastic linear bandits problem over a horizon of $T$
rounds and with set of arms ${X}$ in ambient dimension $d$. Furthermore, we
focus on settings in which the stochastic reward -- associated with each arm in
${X}$ -- is a non-negative, $\nu$-sub-Poisson random variable. For this
setting, we develop an algorithm that achieves a Nash regret of $O\left(
\sqrt{\frac{d\nu}{T}} \log( T |X|)\right)$. In addition, addressing linear
bandit instances in which the set of arms ${X}$ is not necessarily finite, we
obtain a Nash regret upper bound of $O\left(
\frac{d^\frac{5}{4}\nu^{\frac{1}{2}}}{\sqrt{T}} \log(T)\right)$. Since bounded
random variables are sub-Poisson, these results hold for bounded, positive
rewards. Our linear bandit algorithm is built upon the successive elimination
method with novel technical insights, including tailored concentration bounds
and the use of sampling via John ellipsoid in conjunction with the
Kiefer-Wolfowitz optimal design. | [
"Ayush Sawarni",
"Soumybrata Pal",
"Siddharth Barman"
] | 2023-10-03 12:58:10 | http://arxiv.org/abs/2310.02023v1 | http://arxiv.org/pdf/2310.02023v1 | 2310.02023v1 |
Ranking a Set of Objects using Heterogeneous Workers: QUITE an Easy Problem | We focus on the problem of ranking $N$ objects starting from a set of noisy
pairwise comparisons provided by a crowd of unequal workers, each worker being
characterized by a specific degree of reliability, which reflects her ability
to rank pairs of objects. More specifically, we assume that objects are endowed
with intrinsic qualities and that the probability with which an object is
preferred to another depends both on the difference between the qualities of
the two competitors and on the reliability of the worker. We propose QUITE, a
non-adaptive ranking algorithm that jointly estimates workers' reliabilities
and qualities of objects. Performance of QUITE is compared in different
scenarios against previously proposed algorithms. Finally, we show how QUITE
can be naturally made adaptive. | [
"Alessandro Nordio",
"Alberto tarable",
"Emilio Leonardi"
] | 2023-10-03 12:42:13 | http://arxiv.org/abs/2310.02016v1 | http://arxiv.org/pdf/2310.02016v1 | 2310.02016v1 |
Spectral operator learning for parametric PDEs without data reliance | In this paper, we introduce the Spectral Coefficient Learning via Operator
Network (SCLON), a novel operator learning-based approach for solving
parametric partial differential equations (PDEs) without the need for data
harnessing. The cornerstone of our method is the spectral methodology that
employs expansions using orthogonal functions, such as Fourier series and
Legendre polynomials, enabling accurate PDE solutions with fewer grid points.
By merging the merits of spectral methods - encompassing high accuracy,
efficiency, generalization, and the exact fulfillment of boundary conditions -
with the prowess of deep neural networks, SCLON offers a transformative
strategy. Our approach not only eliminates the need for paired input-output
training data, which typically requires extensive numerical computations, but
also effectively learns and predicts solutions of complex parametric PDEs,
ranging from singularly perturbed convection-diffusion equations to the
Navier-Stokes equations. The proposed framework demonstrates superior
performance compared to existing scientific machine learning techniques,
offering solutions for multiple instances of parametric PDEs without harnessing
data. The mathematical framework is robust and reliable, with a well-developed
loss function derived from the weak formulation, ensuring accurate
approximation of solutions while exactly satisfying boundary conditions. The
method's efficacy is further illustrated through its ability to accurately
predict intricate natural behaviors like the Kolmogorov flow and boundary
layers. In essence, our work pioneers a compelling avenue for parametric PDE
solutions, serving as a bridge between traditional numerical methodologies and
cutting-edge machine learning techniques in the realm of scientific
computation. | [
"Junho Choi",
"Taehyun Yun",
"Namjung Kim",
"Youngjoon Hong"
] | 2023-10-03 12:37:15 | http://arxiv.org/abs/2310.02013v1 | http://arxiv.org/pdf/2310.02013v1 | 2310.02013v1 |
Towards Training Without Depth Limits: Batch Normalization Without Gradient Explosion | Normalization layers are one of the key building blocks for deep neural
networks. Several theoretical studies have shown that batch normalization
improves the signal propagation, by avoiding the representations from becoming
collinear across the layers. However, results on mean-field theory of batch
normalization also conclude that this benefit comes at the expense of exploding
gradients in depth. Motivated by these two aspects of batch normalization, in
this study we pose the following question: "Can a batch-normalized network keep
the optimal signal propagation properties, but avoid exploding gradients?" We
answer this question in the affirmative by giving a particular construction of
an Multi-Layer Perceptron (MLP) with linear activations and batch-normalization
that provably has bounded gradients at any depth. Based on Weingarten calculus,
we develop a rigorous and non-asymptotic theory for this constructed MLP that
gives a precise characterization of forward signal propagation, while proving
that gradients remain bounded for linearly independent input samples, which
holds in most practical settings. Inspired by our theory, we also design an
activation shaping scheme that empirically achieves the same properties for
certain non-linear activations. | [
"Alexandru Meterez",
"Amir Joudaki",
"Francesco Orabona",
"Alexander Immer",
"Gunnar Rätsch",
"Hadi Daneshmand"
] | 2023-10-03 12:35:02 | http://arxiv.org/abs/2310.02012v1 | http://arxiv.org/pdf/2310.02012v1 | 2310.02012v1 |
Decoding Human Activities: Analyzing Wearable Accelerometer and Gyroscope Data for Activity Recognition | A person's movement or relative positioning effectively generates raw
electrical signals that can be read by computing machines to apply various
manipulative techniques for the classification of different human activities.
In this paper, a stratified multi-structural approach based on a Residual
network ensembled with Residual MobileNet is proposed, termed as FusionActNet.
The proposed method involves using carefully designed Residual blocks for
classifying the static and dynamic activities separately because they have
clear and distinct characteristics that set them apart. These networks are
trained independently, resulting in two specialized and highly accurate models.
These models excel at recognizing activities within a specific superclass by
taking advantage of the unique algorithmic benefits of architectural
adjustments. Afterward, these two ResNets are passed through a weighted
ensemble-based Residual MobileNet. Subsequently, this ensemble proficiently
discriminates between a specific static and a specific dynamic activity, which
were previously identified based on their distinct feature characteristics in
the earlier stage. The proposed model is evaluated using two publicly
accessible datasets; namely, UCI HAR and Motion-Sense. Therein, it successfully
handled the highly confusing cases of data overlap. Therefore, the proposed
approach achieves a state-of-the-art accuracy of 96.71% and 95.35% in the UCI
HAR and Motion-Sense datasets respectively. | [
"Utsab Saha",
"Sawradip Saha",
"Tahmid Kabir",
"Shaikh Anowarul Fattah",
"Mohammad Saquib"
] | 2023-10-03 12:34:31 | http://arxiv.org/abs/2310.02011v1 | http://arxiv.org/pdf/2310.02011v1 | 2310.02011v1 |
fmeffects: An R Package for Forward Marginal Effects | Forward marginal effects (FMEs) have recently been introduced as a versatile
and effective model-agnostic interpretation method. They provide comprehensible
and actionable model explanations in the form of: If we change $x$ by an amount
$h$, what is the change in predicted outcome $\widehat{y}$? We present the R
package fmeffects, the first software implementation of FMEs. The relevant
theoretical background, package functionality and handling, as well as the
software design and options for future extensions are discussed in this paper. | [
"Holger Löwe",
"Christian A. Scholbeck",
"Christian Heumann",
"Bernd Bischl",
"Giuseppe Casalicchio"
] | 2023-10-03 12:24:51 | http://arxiv.org/abs/2310.02008v1 | http://arxiv.org/pdf/2310.02008v1 | 2310.02008v1 |
MUSCLE: Multi-task Self-supervised Continual Learning to Pre-train Deep Models for X-ray Images of Multiple Body Parts | While self-supervised learning (SSL) algorithms have been widely used to
pre-train deep models, few efforts [11] have been done to improve
representation learning of X-ray image analysis with SSL pre-trained models. In
this work, we study a novel self-supervised pre-training pipeline, namely
Multi-task Self-super-vised Continual Learning (MUSCLE), for multiple medical
imaging tasks, such as classification and segmentation, using X-ray images
collected from multiple body parts, including heads, lungs, and bones.
Specifically, MUSCLE aggregates X-rays collected from multiple body parts for
MoCo-based representation learning, and adopts a well-designed continual
learning (CL) procedure to further pre-train the backbone subject various X-ray
analysis tasks jointly. Certain strategies for image pre-processing, learning
schedules, and regularization have been used to solve data heterogeneity,
overfitting, and catastrophic forgetting problems for multi-task/dataset
learning in MUSCLE.We evaluate MUSCLE using 9 real-world X-ray datasets with
various tasks, including pneumonia classification, skeletal abnormality
classification, lung segmentation, and tuberculosis (TB) detection. Comparisons
against other pre-trained models [7] confirm the proof-of-concept that
self-supervised multi-task/dataset continual pre-training could boost the
performance of X-ray image analysis. | [
"Weibin Liao",
"Haoyi Xiong",
"Qingzhong Wang",
"Yan Mo",
"Xuhong Li",
"Yi Liu",
"Zeyu Chen",
"Siyu Huang",
"Dejing Dou"
] | 2023-10-03 12:19:19 | http://arxiv.org/abs/2310.02000v1 | http://arxiv.org/pdf/2310.02000v1 | 2310.02000v1 |
Fill in the Blank: Exploring and Enhancing LLM Capabilities for Backward Reasoning in Math Word Problems | While forward reasoning (i.e. find the answer given the question) has been
explored extensively in the recent literature, backward reasoning is relatively
unexplored. We examine the backward reasoning capabilities of LLMs on Math Word
Problems (MWPs): given a mathematical question and its answer, with some
details omitted from the question, can LLMs effectively retrieve the missing
information?
In this paper, we formally define the backward reasoning task on math word
problems and modify three datasets to evaluate this task: GSM8k, SVAMP and
MultiArith. Our findings show a significant drop in the accuracy of models on
backward reasoning compared to forward reasoning across four SOTA LLMs (GPT4,
GPT3.5, PaLM-2, and LLaMa-2). Utilizing the specific format of this task, we
propose three novel techniques that improve performance: Rephrase reformulates
the given problem into a forward reasoning problem, PAL-Tools combines the idea
of Program-Aided LLMs to produce a set of equations that can be solved by an
external solver, and Check your Work exploits the availability of natural
verifier of high accuracy in the forward direction, interleaving solving and
verification steps. Finally, realizing that each of our base methods correctly
solves a different set of problems, we propose a novel Bayesian formulation for
creating an ensemble over these base methods aided by a verifier to further
boost the accuracy by a significant margin. Extensive experimentation
demonstrates that our techniques successively improve the performance of LLMs
on the backward reasoning task, with the final ensemble-based method resulting
in a substantial performance gain compared to the raw LLMs with standard
prompting techniques such as chain-of-thought. | [
"Aniruddha Deb",
"Neeva Oza",
"Sarthak Singla",
"Dinesh Khandelwal",
"Dinesh Garg",
"Parag Singla"
] | 2023-10-03 12:03:06 | http://arxiv.org/abs/2310.01991v1 | http://arxiv.org/pdf/2310.01991v1 | 2310.01991v1 |
Benign Overfitting in Two-Layer ReLU Convolutional Neural Networks for XOR Data | Modern deep learning models are usually highly over-parameterized so that
they can overfit the training data. Surprisingly, such overfitting neural
networks can usually still achieve high prediction accuracy. To study this
"benign overfitting" phenomenon, a line of recent works has theoretically
studied the learning of linear models and two-layer neural networks. However,
most of these analyses are still limited to the very simple learning problems
where the Bayes-optimal classifier is linear. In this work, we investigate a
class of XOR-type classification tasks with label-flipping noises. We show
that, under a certain condition on the sample complexity and signal-to-noise
ratio, an over-parameterized ReLU CNN trained by gradient descent can achieve
near Bayes-optimal accuracy. Moreover, we also establish a matching lower bound
result showing that when the previous condition is not satisfied, the
prediction accuracy of the obtained CNN is an absolute constant away from the
Bayes-optimal rate. Our result demonstrates that CNNs have a remarkable
capacity to efficiently learn XOR problems, even in the presence of highly
correlated features. | [
"Xuran Meng",
"Difan Zou",
"Yuan Cao"
] | 2023-10-03 11:31:37 | http://arxiv.org/abs/2310.01975v1 | http://arxiv.org/pdf/2310.01975v1 | 2310.01975v1 |
Federated Wasserstein Distance | We introduce a principled way of computing the Wasserstein distance between
two distributions in a federated manner. Namely, we show how to estimate the
Wasserstein distance between two samples stored and kept on different
devices/clients whilst a central entity/server orchestrates the computations
(again, without having access to the samples). To achieve this feat, we take
advantage of the geometric properties of the Wasserstein distance -- in
particular, the triangle inequality -- and that of the associated {\em
geodesics}: our algorithm, FedWad (for Federated Wasserstein Distance),
iteratively approximates the Wasserstein distance by manipulating and
exchanging distributions from the space of geodesics in lieu of the input
samples. In addition to establishing the convergence properties of FedWad, we
provide empirical results on federated coresets and federate optimal transport
dataset distance, that we respectively exploit for building a novel federated
model and for boosting performance of popular federated learning algorithms. | [
"Alain Rakotomamonjy",
"Kimia Nadjahi",
"Liva Ralaivola"
] | 2023-10-03 11:30:50 | http://arxiv.org/abs/2310.01973v1 | http://arxiv.org/pdf/2310.01973v1 | 2310.01973v1 |
Epidemic Learning: Boosting Decentralized Learning with Randomized Communication | We present Epidemic Learning (EL), a simple yet powerful decentralized
learning (DL) algorithm that leverages changing communication topologies to
achieve faster model convergence compared to conventional DL approaches. At
each round of EL, each node sends its model updates to a random sample of $s$
other nodes (in a system of $n$ nodes). We provide an extensive theoretical
analysis of EL, demonstrating that its changing topology culminates in superior
convergence properties compared to the state-of-the-art (static and dynamic)
topologies. Considering smooth non-convex loss functions, the number of
transient iterations for EL, i.e., the rounds required to achieve asymptotic
linear speedup, is in $\mathcal{O}(\frac{n^3}{s^2})$ which outperforms the
best-known bound $\mathcal{O}({n^3})$ by a factor of $ s^2 $, indicating the
benefit of randomized communication for DL. We empirically evaluate EL in a
96-node network and compare its performance with state-of-the-art DL
approaches. Our results illustrate that EL converges up to $ 1.6\times $
quicker than baseline DL algorithms and attains 1.8% higher accuracy for the
same communication volume. | [
"Martijn de Vos",
"Sadegh Farhadkhani",
"Rachid Guerraoui",
"Anne-Marie Kermarrec",
"Rafael Pires",
"Rishi Sharma"
] | 2023-10-03 11:28:54 | http://arxiv.org/abs/2310.01972v1 | http://arxiv.org/pdf/2310.01972v1 | 2310.01972v1 |
Beyond Labeling Oracles: What does it mean to steal ML models? | Model extraction attacks are designed to steal trained models with only query
access, as is often provided through APIs that ML-as-a-Service providers offer.
ML models are expensive to train, in part because data is hard to obtain, and a
primary incentive for model extraction is to acquire a model while incurring
less cost than training from scratch. Literature on model extraction commonly
claims or presumes that the attacker is able to save on both data acquisition
and labeling costs. We show that the attacker often does not. This is because
current attacks implicitly rely on the adversary being able to sample from the
victim model's data distribution. We thoroughly evaluate factors influencing
the success of model extraction. We discover that prior knowledge of the
attacker, i.e. access to in-distribution data, dominates other factors like the
attack policy the adversary follows to choose which queries to make to the
victim model API. Thus, an adversary looking to develop an equally capable
model with a fixed budget has little practical incentive to perform model
extraction, since for the attack to work they need to collect in-distribution
data, saving only on the cost of labeling. With low labeling costs in the
current market, the usefulness of such attacks is questionable. Ultimately, we
demonstrate that the effect of prior knowledge needs to be explicitly decoupled
from the attack policy. To this end, we propose a benchmark to evaluate attack
policy directly. | [
"Avital Shafran",
"Ilia Shumailov",
"Murat A. Erdogdu",
"Nicolas Papernot"
] | 2023-10-03 11:10:21 | http://arxiv.org/abs/2310.01959v1 | http://arxiv.org/pdf/2310.01959v1 | 2310.01959v1 |
Probabilistic Reach-Avoid for Bayesian Neural Networks | Model-based reinforcement learning seeks to simultaneously learn the dynamics
of an unknown stochastic environment and synthesise an optimal policy for
acting in it. Ensuring the safety and robustness of sequential decisions made
through a policy in such an environment is a key challenge for policies
intended for safety-critical scenarios. In this work, we investigate two
complementary problems: first, computing reach-avoid probabilities for
iterative predictions made with dynamical models, with dynamics described by
Bayesian neural network (BNN); second, synthesising control policies that are
optimal with respect to a given reach-avoid specification (reaching a "target"
state, while avoiding a set of "unsafe" states) and a learned BNN model. Our
solution leverages interval propagation and backward recursion techniques to
compute lower bounds for the probability that a policy's sequence of actions
leads to satisfying the reach-avoid specification. Such computed lower bounds
provide safety certification for the given policy and BNN model. We then
introduce control synthesis algorithms to derive policies maximizing said lower
bounds on the safety probability. We demonstrate the effectiveness of our
method on a series of control benchmarks characterized by learned BNN dynamics
models. On our most challenging benchmark, compared to purely data-driven
policies the optimal synthesis algorithm is able to provide more than a
four-fold increase in the number of certifiable states and more than a
three-fold increase in the average guaranteed reach-avoid probability. | [
"Matthew Wicker",
"Luca Laurenti",
"Andrea Patane",
"Nicola Paoletti",
"Alessandro Abate",
"Marta Kwiatkowska"
] | 2023-10-03 10:52:21 | http://arxiv.org/abs/2310.01951v1 | http://arxiv.org/pdf/2310.01951v1 | 2310.01951v1 |
OOD Aware Supervised Contrastive Learning | Out-of-Distribution (OOD) detection is a crucial problem for the safe
deployment of machine learning models identifying samples that fall outside of
the training distribution, i.e. in-distribution data (ID). Most OOD works focus
on the classification models trained with Cross Entropy (CE) and attempt to fix
its inherent issues. In this work we leverage powerful representation learned
with Supervised Contrastive (SupCon) training and propose a holistic approach
to learn a classifier robust to OOD data. We extend SupCon loss with two
additional contrast terms. The first term pushes auxiliary OOD representations
away from ID representations without imposing any constraints on similarities
among auxiliary data. The second term pushes OOD features far from the existing
class prototypes, while pushing ID representations closer to their
corresponding class prototype. When auxiliary OOD data is not available, we
propose feature mixing techniques to efficiently generate pseudo-OOD features.
Our solution is simple and efficient and acts as a natural extension of the
closed-set supervised contrastive representation learning. We compare against
different OOD detection methods on the common benchmarks and show
state-of-the-art results. | [
"Soroush Seifi",
"Daniel Olmeda Reino",
"Nikolay Chumerin",
"Rahaf Aljundi"
] | 2023-10-03 10:38:39 | http://arxiv.org/abs/2310.01942v1 | http://arxiv.org/pdf/2310.01942v1 | 2310.01942v1 |
Causal Inference with Conditional Front-Door Adjustment and Identifiable Variational Autoencoder | An essential and challenging problem in causal inference is causal effect
estimation from observational data. The problem becomes more difficult with the
presence of unobserved confounding variables. The front-door adjustment is a
practical approach for dealing with unobserved confounding variables. However,
the restriction for the standard front-door adjustment is difficult to satisfy
in practice. In this paper, we relax some of the restrictions by proposing the
concept of conditional front-door (CFD) adjustment and develop the theorem that
guarantees the causal effect identifiability of CFD adjustment. Furthermore, as
it is often impossible for a CFD variable to be given in practice, it is
desirable to learn it from data. By leveraging the ability of deep generative
models, we propose CFDiVAE to learn the representation of the CFD adjustment
variable directly from data with the identifiable Variational AutoEncoder and
formally prove the model identifiability. Extensive experiments on synthetic
datasets validate the effectiveness of CFDiVAE and its superiority over
existing methods. The experiments also show that the performance of CFDiVAE is
less sensitive to the causal strength of unobserved confounding variables. We
further apply CFDiVAE to a real-world dataset to demonstrate its potential
application. | [
"Ziqi Xu",
"Debo Cheng",
"Jiuyong Li",
"Jixue Liu",
"Lin Liu",
"Kui Yu"
] | 2023-10-03 10:24:44 | http://arxiv.org/abs/2310.01937v1 | http://arxiv.org/pdf/2310.01937v1 | 2310.01937v1 |
Navigating Cultural Chasms: Exploring and Unlocking the Cultural POV of Text-To-Image Models | Text-To-Image (TTI) models, exemplified by DALL-E and StableDiffusion, have
recently gained prominence for their remarkable zero-shot capabilities in
generating images guided by textual prompts. Language, as a conduit of culture,
plays a pivotal role in these models' multilingual capabilities, which in turn
shape their cultural agency. In this study, we explore the cultural perception
embedded in TTI models by characterizing culture across three hierarchical
tiers: cultural dimensions, cultural domains, and cultural concepts. We propose
a comprehensive suite of evaluation techniques, including intrinsic evaluations
using the CLIP space, extrinsic evaluations with a Visual-Question-Answer (VQA)
model, and human assessments, to discern TTI cultural perceptions. To
facilitate our research, we introduce the CulText2I dataset, derived from four
diverse TTI models and spanning ten languages. Our experiments reveal insights
into these models' cultural awareness, cultural distinctions, and the unlocking
of cultural features, releasing the potential for cross-cultural applications. | [
"Mor Ventura",
"Eyal Ben-David",
"Anna Korhonen",
"Roi Reichart"
] | 2023-10-03 10:13:36 | http://arxiv.org/abs/2310.01929v1 | http://arxiv.org/pdf/2310.01929v1 | 2310.01929v1 |
RoFormer for Position Aware Multiple Instance Learning in Whole Slide Image Classification | Whole slide image (WSI) classification is a critical task in computational
pathology. However, the gigapixel-size of such images remains a major challenge
for the current state of deep-learning. Current methods rely on
multiple-instance learning (MIL) models with frozen feature extractors. Given
the the high number of instances in each image, MIL methods have long assumed
independence and permutation-invariance of patches, disregarding the tissue
structure and correlation between patches. Recent works started studying this
correlation between instances but the computational workload of such a high
number of tokens remained a limiting factor. In particular, relative position
of patches remains unaddressed. We propose to apply a straightforward encoding
module, namely a RoFormer layer , relying on memory-efficient exact
self-attention and relative positional encoding. This module can perform full
self-attention with relative position encoding on patches of large and
arbitrary shaped WSIs, solving the need for correlation between instances and
spatial modeling of tissues. We demonstrate that our method outperforms
state-of-the-art MIL models on three commonly used public datasets (TCGA-NSCLC,
BRACS and Camelyon16)) on weakly supervised classification tasks. Code is
available at https://github.com/Sanofi-Public/DDS-RoFormerMIL | [
"Etienne Pochet",
"Rami Maroun",
"Roger Trullo"
] | 2023-10-03 09:59:59 | http://arxiv.org/abs/2310.01924v1 | http://arxiv.org/pdf/2310.01924v1 | 2310.01924v1 |
Improved Automatic Diabetic Retinopathy Severity Classification Using Deep Multimodal Fusion of UWF-CFP and OCTA Images | Diabetic Retinopathy (DR), a prevalent and severe complication of diabetes,
affects millions of individuals globally, underscoring the need for accurate
and timely diagnosis. Recent advancements in imaging technologies, such as
Ultra-WideField Color Fundus Photography (UWF-CFP) imaging and Optical
Coherence Tomography Angiography (OCTA), provide opportunities for the early
detection of DR but also pose significant challenges given the disparate nature
of the data they produce. This study introduces a novel multimodal approach
that leverages these imaging modalities to notably enhance DR classification.
Our approach integrates 2D UWF-CFP images and 3D high-resolution 6x6 mm$^3$
OCTA (both structure and flow) images using a fusion of ResNet50 and
3D-ResNet50 models, with Squeeze-and-Excitation (SE) blocks to amplify relevant
features. Additionally, to increase the model's generalization capabilities, a
multimodal extension of Manifold Mixup, applied to concatenated multimodal
features, is implemented. Experimental results demonstrate a remarkable
enhancement in DR classification performance with the proposed multimodal
approach compared to methods relying on a single modality only. The methodology
laid out in this work holds substantial promise for facilitating more accurate,
early detection of DR, potentially improving clinical outcomes for patients. | [
"Mostafa El Habib Daho",
"Yihao Li",
"Rachid Zeghlache",
"Yapo Cedric Atse",
"Hugo Le Boité",
"Sophie Bonnin",
"Deborah Cosette",
"Pierre Deman",
"Laurent Borderie",
"Capucine Lepicard",
"Ramin Tadayoni",
"Béatrice Cochener",
"Pierre-Henri Conze",
"Mathieu Lamard",
"Gwenolé Quellec"
] | 2023-10-03 09:35:38 | http://arxiv.org/abs/2310.01912v1 | http://arxiv.org/pdf/2310.01912v1 | 2310.01912v1 |
Unsupervised Complex Semi-Binary Matrix Factorization for Activation Sequence Recovery of Quasi-Stationary Sources | Advocating for a sustainable, resilient and human-centric industry, the three
pillars of Industry 5.0 call for an increased understanding of industrial
processes and manufacturing systems, as well as their energy sustainability.
One of the most fundamental elements of comprehension is knowing when the
systems are operated, as this is key to locating energy intensive subsystems
and operations. Such knowledge is often lacking in practice. Activation
statuses can be recovered from sensor data though. Some non-intrusive sensors
(accelerometers, current sensors, etc.) acquire mixed signals containing
information about multiple actuators at once. Despite their low cost as regards
the fleet of systems they monitor, additional signal processing is required to
extract the individual activation sequences. To that end, sparse regression
techniques can extract leading dynamics in sequential data. Notorious
dictionary learning algorithms have proven effective in this regard. This paper
considers different industrial settings in which the identification of binary
subsystem activation sequences is sought. In this context, it is assumed that
each sensor measures an extensive physical property, source signals are
periodic, quasi-stationary and independent, albeit these signals may be
correlated and their noise distribution is arbitrary. Existing methods either
restrict these assumptions, e.g., by imposing orthogonality or noise
characteristics, or lift them using additional assumptions, typically using
nonlinear transforms. | [
"Romain Delabeye",
"Martin Ghienne",
"Olivia Penas",
"Jean-Luc Dion"
] | 2023-10-03 09:29:16 | http://arxiv.org/abs/2310.02295v1 | http://arxiv.org/pdf/2310.02295v1 | 2310.02295v1 |
Beyond the Benchmark: Detecting Diverse Anomalies in Videos | Video Anomaly Detection (VAD) plays a crucial role in modern surveillance
systems, aiming to identify various anomalies in real-world situations.
However, current benchmark datasets predominantly emphasize simple,
single-frame anomalies such as novel object detection. This narrow focus
restricts the advancement of VAD models. In this research, we advocate for an
expansion of VAD investigations to encompass intricate anomalies that extend
beyond conventional benchmark boundaries. To facilitate this, we introduce two
datasets, HMDB-AD and HMDB-Violence, to challenge models with diverse
action-based anomalies. These datasets are derived from the HMDB51 action
recognition dataset. We further present Multi-Frame Anomaly Detection (MFAD), a
novel method built upon the AI-VAD framework. AI-VAD utilizes single-frame
features such as pose estimation and deep image encoding, and two-frame
features such as object velocity. They then apply a density estimation
algorithm to compute anomaly scores. To address complex multi-frame anomalies,
we add a deep video encoding features capturing long-range temporal
dependencies, and logistic regression to enhance final score calculation.
Experimental results confirm our assumptions, highlighting existing models
limitations with new anomaly types. MFAD excels in both simple and complex
anomaly detection scenarios. | [
"Yoav Arad",
"Michael Werman"
] | 2023-10-03 09:22:06 | http://arxiv.org/abs/2310.01904v1 | http://arxiv.org/pdf/2310.01904v1 | 2310.01904v1 |
FiGURe: Simple and Efficient Unsupervised Node Representations with Filter Augmentations | Unsupervised node representations learnt using contrastive learning-based
methods have shown good performance on downstream tasks. However, these methods
rely on augmentations that mimic low-pass filters, limiting their performance
on tasks requiring different eigen-spectrum parts. This paper presents a simple
filter-based augmentation method to capture different parts of the
eigen-spectrum. We show significant improvements using these augmentations.
Further, we show that sharing the same weights across these different filter
augmentations is possible, reducing the computational load. In addition,
previous works have shown that good performance on downstream tasks requires
high dimensional representations. Working with high dimensions increases the
computations, especially when multiple augmentations are involved. We mitigate
this problem and recover good performance through lower dimensional embeddings
using simple random Fourier feature projections. Our method, FiGURe achieves an
average gain of up to 4.4%, compared to the state-of-the-art unsupervised
models, across all datasets in consideration, both homophilic and heterophilic.
Our code can be found at: https://github.com/microsoft/figure. | [
"Chanakya Ekbote",
"Ajinkya Pankaj Deshpande",
"Arun Iyer",
"Ramakrishna Bairi",
"Sundararajan Sellamanickam"
] | 2023-10-03 08:54:06 | http://arxiv.org/abs/2310.01892v2 | http://arxiv.org/pdf/2310.01892v2 | 2310.01892v2 |
Effective and Parameter-Efficient Reusing Fine-Tuned Models | Many pre-trained large-scale models provided online have become highly
effective in transferring to downstream tasks. At the same time, various
task-specific models fine-tuned on these pre-trained models are available
online for public use. In practice, as collecting task-specific data is
labor-intensive and fine-tuning the large pre-trained models is computationally
expensive, one can reuse task-specific finetuned models to deal with downstream
tasks. However, using a model per task causes a heavy burden on storage and
serving. Recently, many training-free and parameter-efficient methods have been
proposed for reusing multiple fine-tuned task-specific models into a single
multi-task model. However, these methods exhibit a large accuracy gap compared
with using a fine-tuned model per task. In this paper, we propose
Parameter-Efficient methods for ReUsing (PERU) fine-tuned models. For reusing
Fully Fine-Tuned (FFT) models, we propose PERU-FFT by injecting a sparse task
vector into a merged model by magnitude pruning. For reusing LoRA fine-tuned
models, we propose PERU-LoRA use a lower-rank matrix to approximate the LoRA
matrix by singular value decomposition. Both PERUFFT and PERU-LoRA are
training-free. Extensive experiments conducted on computer vision and natural
language process tasks demonstrate the effectiveness and parameter-efficiency
of the proposed methods. The proposed PERU-FFT and PERU-LoRA outperform
existing reusing model methods by a large margin and achieve comparable
performance to using a fine-tuned model per task. | [
"Weisen Jiang",
"Baijiong Lin",
"Han Shi",
"Yu Zhang",
"Zhenguo Li",
"James T. Kwok"
] | 2023-10-03 08:39:33 | http://arxiv.org/abs/2310.01886v2 | http://arxiv.org/pdf/2310.01886v2 | 2310.01886v2 |
Synthetic CT Generation via Variant Invertible Network for All-digital Brain PET Attenuation Correction | Attenuation correction (AC) is essential for the generation of artifact-free
and quantitatively accurate positron emission tomography (PET) images. However,
AC of PET faces challenges including inter-scan motion and erroneous
transformation of structural voxel-intensities to PET attenuation-correction
factors. Nowadays, the problem of AC for quantitative PET have been solved to a
large extent after the commercial availability of devices combining PET with
computed tomography (CT). Meanwhile, considering the feasibility of a deep
learning approach for PET AC without anatomical imaging, this paper develops a
PET AC method, which uses deep learning to generate continuously valued CT
images from non-attenuation corrected PET images for AC on brain PET imaging.
Specifically, an invertible network combined with the variable augmentation
strategy that can achieve the bidirectional inference processes is proposed for
synthetic CT generation (IVNAC). To evaluate the performance of the proposed
algorithm, we conducted a comprehensive study on a total of 1440 data from 37
clinical patients using comparative algorithms (such as Cycle-GAN and Pix2pix).
Perceptual analysis and quantitative evaluations illustrate that the invertible
network for PET AC outperforms other existing AC models, which demonstrates the
potential of the proposed method and the feasibility of achieving brain PET AC
without CT. | [
"Yu Guan",
"Bohui Shen",
"Xinchong Shi",
"Xiangsong Zhang",
"Bingxuan Li",
"Qiegen Liu"
] | 2023-10-03 08:38:52 | http://arxiv.org/abs/2310.01885v1 | http://arxiv.org/pdf/2310.01885v1 | 2310.01885v1 |
Adaptive Hybrid Model for Enhanced Stock Market Predictions Using Improved VMD and Stacked Informer | This paper introduces an innovative adaptive hybrid model for stock market
predictions, leveraging the capabilities of an enhanced Variational Mode
Decomposition (VMD), Feature Engineering (FE), and stacked Informer integrated
with an adaptive loss function. Through rigorous experimentation, the proposed
model, termed Adam+GC+enhanced informer (We name it VMGCformer), demonstrates
significant proficiency in addressing the intricate dynamics and volatile
nature of stock market data. Experimental results, derived from multiple
benchmark datasets, underscore the model's superiority in terms of prediction
accuracy, responsiveness, and generalization capabilities over traditional and
other hybrid models. The research further highlights potential avenues for
optimization and introduces future directions to enhance predictive modeling,
especially for small enterprises and feature engineering. | [
"Jianan Zhang",
"Hongyi Duan"
] | 2023-10-03 08:37:21 | http://arxiv.org/abs/2310.01884v1 | http://arxiv.org/pdf/2310.01884v1 | 2310.01884v1 |
AutoCast++: Enhancing World Event Prediction with Zero-shot Ranking-based Context Retrieval | Machine-based prediction of real-world events is garnering attention due to
its potential for informed decision-making. Whereas traditional forecasting
predominantly hinges on structured data like time-series, recent breakthroughs
in language models enable predictions using unstructured text. In particular,
(Zou et al., 2022) unveils AutoCast, a new benchmark that employs news articles
for answering forecasting queries. Nevertheless, existing methods still trail
behind human performance. The cornerstone of accurate forecasting, we argue,
lies in identifying a concise, yet rich subset of news snippets from a vast
corpus. With this motivation, we introduce AutoCast++, a zero-shot
ranking-based context retrieval system, tailored to sift through expansive news
document collections for event forecasting. Our approach first re-ranks
articles based on zero-shot question-passage relevance, honing in on
semantically pertinent news. Following this, the chosen articles are subjected
to zero-shot summarization to attain succinct context. Leveraging a pre-trained
language model, we conduct both the relevance evaluation and article
summarization without needing domain-specific training. Notably, recent
articles can sometimes be at odds with preceding ones due to new facts or
unanticipated incidents, leading to fluctuating temporal dynamics. To tackle
this, our re-ranking mechanism gives preference to more recent articles, and we
further regularize the multi-passage representation learning to align with
human forecaster responses made on different dates. Empirical results
underscore marked improvements across multiple metrics, improving the
performance for multiple-choice questions (MCQ) by 48% and true/false (TF)
questions by up to 8%. | [
"Qi Yan",
"Raihan Seraj",
"Jiawei He",
"Lili Meng",
"Tristan Sylvain"
] | 2023-10-03 08:34:44 | http://arxiv.org/abs/2310.01880v1 | http://arxiv.org/pdf/2310.01880v1 | 2310.01880v1 |
Towards Stable Backdoor Purification through Feature Shift Tuning | It has been widely observed that deep neural networks (DNN) are vulnerable to
backdoor attacks where attackers could manipulate the model behavior
maliciously by tampering with a small set of training samples. Although a line
of defense methods is proposed to mitigate this threat, they either require
complicated modifications to the training process or heavily rely on the
specific model architecture, which makes them hard to deploy into real-world
applications. Therefore, in this paper, we instead start with fine-tuning, one
of the most common and easy-to-deploy backdoor defenses, through comprehensive
evaluations against diverse attack scenarios. Observations made through initial
experiments show that in contrast to the promising defensive results on high
poisoning rates, vanilla tuning methods completely fail at low poisoning rate
scenarios. Our analysis shows that with the low poisoning rate, the
entanglement between backdoor and clean features undermines the effect of
tuning-based defenses. Therefore, it is necessary to disentangle the backdoor
and clean features in order to improve backdoor purification. To address this,
we introduce Feature Shift Tuning (FST), a method for tuning-based backdoor
purification. Specifically, FST encourages feature shifts by actively deviating
the classifier weights from the originally compromised weights. Extensive
experiments demonstrate that our FST provides consistently stable performance
under different attack settings. Without complex parameter adjustments, FST
also achieves much lower tuning costs, only 10 epochs. Our codes are available
at https://github.com/AISafety-HKUST/stable_backdoor_purification. | [
"Rui Min",
"Zeyu Qin",
"Li Shen",
"Minhao Cheng"
] | 2023-10-03 08:25:32 | http://arxiv.org/abs/2310.01875v3 | http://arxiv.org/pdf/2310.01875v3 | 2310.01875v3 |
DeepDecipher: Accessing and Investigating Neuron Activation in Large Language Models | As large language models (LLMs) become more capable, there is an urgent need
for interpretable and transparent tools. Current methods are difficult to
implement, and accessible tools to analyze model internals are lacking. To
bridge this gap, we present DeepDecipher - an API and interface for probing
neurons in transformer models' MLP layers. DeepDecipher makes the outputs of
advanced interpretability techniques for LLMs readily available. The
easy-to-use interface also makes inspecting these complex models more
intuitive. This paper outlines DeepDecipher's design and capabilities. We
demonstrate how to analyze neurons, compare models, and gain insights into
model behavior. For example, we contrast DeepDecipher's functionality with
similar tools like Neuroscope and OpenAI's Neuron Explainer. DeepDecipher
enables efficient, scalable analysis of LLMs. By granting access to
state-of-the-art interpretability methods, DeepDecipher makes LLMs more
transparent, trustworthy, and safe. Researchers, engineers, and developers can
quickly diagnose issues, audit systems, and advance the field. | [
"Albert Garde",
"Esben Kran",
"Fazl Barez"
] | 2023-10-03 08:15:20 | http://arxiv.org/abs/2310.01870v1 | http://arxiv.org/pdf/2310.01870v1 | 2310.01870v1 |
Conditional Instrumental Variable Regression with Representation Learning for Causal Inference | This paper studies the challenging problem of estimating causal effects from
observational data, in the presence of unobserved confounders. The two-stage
least square (TSLS) method and its variants with a standard instrumental
variable (IV) are commonly used to eliminate confounding bias, including the
bias caused by unobserved confounders, but they rely on the linearity
assumption. Besides, the strict condition of unconfounded instruments posed on
a standard IV is too strong to be practical. To address these challenging and
practical problems of the standard IV method (linearity assumption and the
strict condition), in this paper, we use a conditional IV (CIV) to relax the
unconfounded instrument condition of standard IV and propose a non-linear CIV
regression with Confounding Balancing Representation Learning, CBRL.CIV, for
jointly eliminating the confounding bias from unobserved confounders and
balancing the observed confounders, without the linearity assumption. We
theoretically demonstrate the soundness of CBRL.CIV. Extensive experiments on
synthetic and two real-world datasets show the competitive performance of
CBRL.CIV against state-of-the-art IV-based estimators and superiority in
dealing with the non-linear situation. | [
"Debo Cheng",
"Ziqi Xu",
"Jiuyong Li",
"Lin Liu",
"Jixue Liu",
"Thuc Duy Le"
] | 2023-10-03 08:08:09 | http://arxiv.org/abs/2310.01865v1 | http://arxiv.org/pdf/2310.01865v1 | 2310.01865v1 |
High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise | High-probability analysis of stochastic first-order optimization methods
under mild assumptions on the noise has been gaining a lot of attention in
recent years. Typically, gradient clipping is one of the key algorithmic
ingredients to derive good high-probability guarantees when the noise is
heavy-tailed. However, if implemented na\"ively, clipping can spoil the
convergence of the popular methods for composite and distributed optimization
(Prox-SGD/Parallel SGD) even in the absence of any noise. Due to this reason,
many works on high-probability analysis consider only unconstrained
non-distributed problems, and the existing results for composite/distributed
problems do not include some important special cases (like strongly convex
problems) and are not optimal. To address this issue, we propose new stochastic
methods for composite and distributed optimization based on the clipping of
stochastic gradient differences and prove tight high-probability convergence
results (including nearly optimal ones) for the new methods. Using similar
ideas, we also develop new methods for composite and distributed variational
inequalities and analyze the high-probability convergence of these methods. | [
"Eduard Gorbunov",
"Abdurakhmon Sadiev",
"Marina Danilova",
"Samuel Horváth",
"Gauthier Gidel",
"Pavel Dvurechensky",
"Alexander Gasnikov",
"Peter Richtárik"
] | 2023-10-03 07:49:17 | http://arxiv.org/abs/2310.01860v1 | http://arxiv.org/pdf/2310.01860v1 | 2310.01860v1 |
Variational Gaussian approximation of the Kushner optimal filter | In estimation theory, the Kushner equation provides the evolution of the
probability density of the state of a dynamical system given continuous-time
observations. Building upon our recent work, we propose a new way to
approximate the solution of the Kushner equation through tractable variational
Gaussian approximations of two proximal losses associated with the propagation
and Bayesian update of the probability density. The first is a proximal loss
based on the Wasserstein metric and the second is a proximal loss based on the
Fisher metric. The solution to this last proximal loss is given by implicit
updates on the mean and covariance that we proposed earlier. These two
variational updates can be fused and shown to satisfy a set of stochastic
differential equations on the Gaussian's mean and covariance matrix. This
Gaussian flow is consistent with the Kalman-Bucy and Riccati flows in the
linear case and generalize them in the nonlinear one. | [
"Marc Lambert",
"Silvère Bonnabel",
"Francis Bach"
] | 2023-10-03 07:48:11 | http://arxiv.org/abs/2310.01859v1 | http://arxiv.org/pdf/2310.01859v1 | 2310.01859v1 |
Score-based Data Assimilation for a Two-Layer Quasi-Geostrophic Model | Data assimilation addresses the problem of identifying plausible state
trajectories of dynamical systems given noisy or incomplete observations. In
geosciences, it presents challenges due to the high-dimensionality of
geophysical dynamical systems, often exceeding millions of dimensions. This
work assesses the scalability of score-based data assimilation (SDA), a novel
data assimilation method, in the context of such systems. We propose
modifications to the score network architecture aimed at significantly reducing
memory consumption and execution time. We demonstrate promising results for a
two-layer quasi-geostrophic model. | [
"François Rozet",
"Gilles Louppe"
] | 2023-10-03 07:34:27 | http://arxiv.org/abs/2310.01853v1 | http://arxiv.org/pdf/2310.01853v1 | 2310.01853v1 |
Benchmarking and Improving Generator-Validator Consistency of Language Models | As of September 2023, ChatGPT correctly answers "what is 7+8" with 15, but
when asked "7+8=15, True or False" it responds with "False". This inconsistency
between generating and validating an answer is prevalent in language models
(LMs) and erodes trust. In this paper, we propose a framework for measuring the
consistency between generation and validation (which we call
generator-validator consistency, or GV-consistency), finding that even GPT-4, a
state-of-the-art LM, is GV-consistent only 76% of the time. To improve the
consistency of LMs, we propose to finetune on the filtered generator and
validator responses that are GV-consistent, and call this approach consistency
fine-tuning. We find that this approach improves GV-consistency of Alpaca-30B
from 60% to 93%, and the improvement extrapolates to unseen tasks and domains
(e.g., GV-consistency for positive style transfers extrapolates to unseen
styles like humor). In addition to improving consistency, consistency
fine-tuning improves both generator quality and validator accuracy without
using any labeled data. Evaluated across 6 tasks, including math questions,
knowledge-intensive QA, and instruction following, our method improves the
generator quality by 16% and the validator accuracy by 6.3% across all tasks. | [
"Xiang Lisa Li",
"Vaishnavi Shrivastava",
"Siyan Li",
"Tatsunori Hashimoto",
"Percy Liang"
] | 2023-10-03 07:23:22 | http://arxiv.org/abs/2310.01846v1 | http://arxiv.org/pdf/2310.01846v1 | 2310.01846v1 |
Zero-Shot Refinement of Buildings' Segmentation Models using SAM | Foundation models have excelled in various tasks but are often evaluated on
general benchmarks. The adaptation of these models for specific domains, such
as remote sensing imagery, remains an underexplored area. In remote sensing,
precise building instance segmentation is vital for applications like urban
planning. While Convolutional Neural Networks (CNNs) perform well, their
generalization can be limited. For this aim, we present a novel approach to
adapt foundation models to address existing models' generalization dropback.
Among several models, our focus centers on the Segment Anything Model (SAM), a
potent foundation model renowned for its prowess in class-agnostic image
segmentation capabilities. We start by identifying the limitations of SAM,
revealing its suboptimal performance when applied to remote sensing imagery.
Moreover, SAM does not offer recognition abilities and thus fails to classify
and tag localized objects. To address these limitations, we introduce different
prompting strategies, including integrating a pre-trained CNN as a prompt
generator. This novel approach augments SAM with recognition abilities, a first
of its kind. We evaluated our method on three remote sensing datasets,
including the WHU Buildings dataset, the Massachusetts Buildings dataset, and
the AICrowd Mapping Challenge. For out-of-distribution performance on the WHU
dataset, we achieve a 5.47% increase in IoU and a 4.81% improvement in
F1-score. For in-distribution performance on the WHU dataset, we observe a
2.72% and 1.58% increase in True-Positive-IoU and True-Positive-F1 score,
respectively. We intend to release our code repository, hoping to inspire
further exploration of foundation models for domain-specific tasks within the
remote sensing community. | [
"Ali Mayladan",
"Hasan Nasrallah",
"Hasan Moughnieh",
"Mustafa Shukor",
"Ali J. Ghandour"
] | 2023-10-03 07:19:59 | http://arxiv.org/abs/2310.01845v1 | http://arxiv.org/pdf/2310.01845v1 | 2310.01845v1 |
Extending CAM-based XAI methods for Remote Sensing Imagery Segmentation | Current AI-based methods do not provide comprehensible physical
interpretations of the utilized data, extracted features, and
predictions/inference operations. As a result, deep learning models trained
using high-resolution satellite imagery lack transparency and explainability
and can be merely seen as a black box, which limits their wide-level adoption.
Experts need help understanding the complex behavior of AI models and the
underlying decision-making process. The explainable artificial intelligence
(XAI) field is an emerging field providing means for robust, practical, and
trustworthy deployment of AI models. Several XAI techniques have been proposed
for image classification tasks, whereas the interpretation of image
segmentation remains largely unexplored. This paper offers to bridge this gap
by adapting the recent XAI classification algorithms and making them usable for
muti-class image segmentation, where we mainly focus on buildings' segmentation
from high-resolution satellite images. To benchmark and compare the performance
of the proposed approaches, we introduce a new XAI evaluation methodology and
metric based on "Entropy" to measure the model uncertainty. Conventional XAI
evaluation methods rely mainly on feeding area-of-interest regions from the
image back to the pre-trained (utility) model and then calculating the average
change in the probability of the target class. Those evaluation metrics lack
the needed robustness, and we show that using Entropy to monitor the model
uncertainty in segmenting the pixels within the target class is more suitable.
We hope this work will pave the way for additional XAI research for image
segmentation and applications in the remote sensing discipline. | [
"Abdul Karim Gizzini",
"Mustafa Shukor",
"Ali J. Ghandour"
] | 2023-10-03 07:01:23 | http://arxiv.org/abs/2310.01837v1 | http://arxiv.org/pdf/2310.01837v1 | 2310.01837v1 |
EMBERSim: A Large-Scale Databank for Boosting Similarity Search in Malware Analysis | In recent years there has been a shift from heuristics-based malware
detection towards machine learning, which proves to be more robust in the
current heavily adversarial threat landscape. While we acknowledge machine
learning to be better equipped to mine for patterns in the increasingly high
amounts of similar-looking files, we also note a remarkable scarcity of the
data available for similarity-targeted research. Moreover, we observe that the
focus in the few related works falls on quantifying similarity in malware,
often overlooking the clean data. This one-sided quantification is especially
dangerous in the context of detection bypass. We propose to address the
deficiencies in the space of similarity research on binary files, starting from
EMBER - one of the largest malware classification data sets. We enhance EMBER
with similarity information as well as malware class tags, to enable further
research in the similarity space. Our contribution is threefold: (1) we publish
EMBERSim, an augmented version of EMBER, that includes similarity-informed
tags; (2) we enrich EMBERSim with automatically determined malware class tags
using the open-source tool AVClass on VirusTotal data and (3) we describe and
share the implementation for our class scoring technique and leaf similarity
method. | [
"Dragos Georgian Corlatescu",
"Alexandru Dinu",
"Mihaela Gaman",
"Paul Sumedrea"
] | 2023-10-03 06:58:45 | http://arxiv.org/abs/2310.01835v1 | http://arxiv.org/pdf/2310.01835v1 | 2310.01835v1 |
Trainable Noise Model as an XAI evaluation method: application on Sobol for remote sensing image segmentation | eXplainable Artificial Intelligence (XAI) has emerged as an essential
requirement when dealing with mission-critical applications, ensuring
transparency and interpretability of the employed black box AI models. The
significance of XAI spans various domains, from healthcare to finance, where
understanding the decision-making process of deep learning algorithms is
essential. Most AI-based computer vision models are often black boxes; hence,
providing explainability of deep neural networks in image processing is crucial
for their wide adoption and deployment in medical image analysis, autonomous
driving, and remote sensing applications. Recently, several XAI methods for
image classification tasks have been introduced. On the contrary, image
segmentation has received comparatively less attention in the context of
explainability, although it is a fundamental task in computer vision
applications, especially in remote sensing. Only some research proposes
gradient-based XAI algorithms for image segmentation. This paper adapts the
recent gradient-free Sobol XAI method for semantic segmentation. To measure the
performance of the Sobol method for segmentation, we propose a quantitative XAI
evaluation method based on a learnable noise model. The main objective of this
model is to induce noise on the explanation maps, where higher induced noise
signifies low accuracy and vice versa. A benchmark analysis is conducted to
evaluate and compare performance of three XAI methods, including Seg-Grad-CAM,
Seg-Grad-CAM++ and Seg-Sobol using the proposed noise-based evaluation
technique. This constitutes the first attempt to run and evaluate XAI methods
using high-resolution satellite images. | [
"Hossein Shreim",
"Abdul Karim Gizzini",
"Ali J. Ghandour"
] | 2023-10-03 06:51:48 | http://arxiv.org/abs/2310.01828v1 | http://arxiv.org/pdf/2310.01828v1 | 2310.01828v1 |
Empirical Study of PEFT techniques for Winter Wheat Segmentation | Parameter Efficient Fine Tuning (PEFT) techniques have recently experienced
significant growth and have been extensively employed to adapt large vision and
language models to various domains, enabling satisfactory model performance
with minimal computational needs. Despite these advances, more research has yet
to delve into potential PEFT applications in real-life scenarios, particularly
in the critical domains of remote sensing and crop monitoring. The diversity of
climates across different regions and the need for comprehensive large-scale
datasets have posed significant obstacles to accurately identify crop types
across varying geographic locations and changing growing seasons. This study
seeks to bridge this gap by comprehensively exploring the feasibility of
cross-area and cross-year out-of-distribution generalization using the
State-of-the-Art (SOTA) wheat crop monitoring model. The aim of this work is to
explore PEFT approaches for crop monitoring. Specifically, we focus on adapting
the SOTA TSViT model to address winter wheat field segmentation, a critical
task for crop monitoring and food security. This adaptation process involves
integrating different PEFT techniques, including BigFit, LoRA, Adaptformer, and
prompt tuning. Using PEFT techniques, we achieved notable results comparable to
those achieved using full fine-tuning methods while training only a mere 0.7%
parameters of the whole TSViT architecture. The in-house labeled data-set,
referred to as the Beqaa-Lebanon dataset, comprises high-quality annotated
polygons for wheat and non-wheat classes with a total surface of 170 kmsq, over
five consecutive years. Using Sentinel-2 images, our model achieved a 84%
F1-score. We intend to publicly release the Lebanese winter wheat data set,
code repository, and model weights. | [
"Mohamad Hasan Zahweh",
"Hasan Nasrallah",
"Mustafa Shukor",
"Ghaleb Faour",
"Ali J. Ghandour"
] | 2023-10-03 06:42:28 | http://arxiv.org/abs/2310.01825v1 | http://arxiv.org/pdf/2310.01825v1 | 2310.01825v1 |
Mini-BEHAVIOR: A Procedurally Generated Benchmark for Long-horizon Decision-Making in Embodied AI | We present Mini-BEHAVIOR, a novel benchmark for embodied AI that challenges
agents to use reasoning and decision-making skills to solve complex activities
that resemble everyday human challenges. The Mini-BEHAVIOR environment is a
fast, realistic Gridworld environment that offers the benefits of rapid
prototyping and ease of use while preserving a symbolic level of physical
realism and complexity found in complex embodied AI benchmarks. We introduce
key features such as procedural generation, to enable the creation of countless
task variations and support open-ended learning. Mini-BEHAVIOR provides
implementations of various household tasks from the original BEHAVIOR
benchmark, along with starter code for data collection and reinforcement
learning agent training. In essence, Mini-BEHAVIOR offers a fast, open-ended
benchmark for evaluating decision-making and planning solutions in embodied AI.
It serves as a user-friendly entry point for research and facilitates the
evaluation and development of solutions, simplifying their assessment and
development while advancing the field of embodied AI. Code is publicly
available at https://github.com/StanfordVL/mini_behavior. | [
"Emily Jin",
"Jiaheng Hu",
"Zhuoyi Huang",
"Ruohan Zhang",
"Jiajun Wu",
"Li Fei-Fei",
"Roberto Martín-Martín"
] | 2023-10-03 06:41:18 | http://arxiv.org/abs/2310.01824v1 | http://arxiv.org/pdf/2310.01824v1 | 2310.01824v1 |
MIMO-NeRF: Fast Neural Rendering with Multi-input Multi-output Neural Radiance Fields | Neural radiance fields (NeRFs) have shown impressive results for novel view
synthesis. However, they depend on the repetitive use of a single-input
single-output multilayer perceptron (SISO MLP) that maps 3D coordinates and
view direction to the color and volume density in a sample-wise manner, which
slows the rendering. We propose a multi-input multi-output NeRF (MIMO-NeRF)
that reduces the number of MLPs running by replacing the SISO MLP with a MIMO
MLP and conducting mappings in a group-wise manner. One notable challenge with
this approach is that the color and volume density of each point can differ
according to a choice of input coordinates in a group, which can lead to some
notable ambiguity. We also propose a self-supervised learning method that
regularizes the MIMO MLP with multiple fast reformulated MLPs to alleviate this
ambiguity without using pretrained models. The results of a comprehensive
experimental evaluation including comparative and ablation studies are
presented to show that MIMO-NeRF obtains a good trade-off between speed and
quality with a reasonable training time. We then demonstrate that MIMO-NeRF is
compatible with and complementary to previous advancements in NeRFs by applying
it to two representative fast NeRFs, i.e., a NeRF with sample reduction
(DONeRF) and a NeRF with alternative representations (TensoRF). | [
"Takuhiro Kaneko"
] | 2023-10-03 06:33:05 | http://arxiv.org/abs/2310.01821v1 | http://arxiv.org/pdf/2310.01821v1 | 2310.01821v1 |
Towards Robust Fidelity for Evaluating Explainability of Graph Neural Networks | Graph Neural Networks (GNNs) are neural models that leverage the dependency
structure in graphical data via message passing among the graph nodes. GNNs
have emerged as pivotal architectures in analyzing graph-structured data, and
their expansive application in sensitive domains requires a comprehensive
understanding of their decision-making processes -- necessitating a framework
for GNN explainability. An explanation function for GNNs takes a pre-trained
GNN along with a graph as input, to produce a `sufficient statistic' subgraph
with respect to the graph label. A main challenge in studying GNN
explainability is to provide fidelity measures that evaluate the performance of
these explanation functions. This paper studies this foundational challenge,
spotlighting the inherent limitations of prevailing fidelity metrics, including
$Fid_+$, $Fid_-$, and $Fid_\Delta$. Specifically, a formal,
information-theoretic definition of explainability is introduced and it is
shown that existing metrics often fail to align with this definition across
various statistical scenarios. The reason is due to potential distribution
shifts when subgraphs are removed in computing these fidelity measures.
Subsequently, a robust class of fidelity measures are introduced, and it is
shown analytically that they are resilient to distribution shift issues and are
applicable in a wide range of scenarios. Extensive empirical analysis on both
synthetic and real datasets are provided to illustrate that the proposed
metrics are more coherent with gold standard metrics. | [
"Xu Zheng",
"Farhad Shirani",
"Tianchun Wang",
"Wei Cheng",
"Zhuomin Chen",
"Haifeng Chen",
"Hua Wei",
"Dongsheng Luo"
] | 2023-10-03 06:25:14 | http://arxiv.org/abs/2310.01820v1 | http://arxiv.org/pdf/2310.01820v1 | 2310.01820v1 |
AutoLoRa: A Parameter-Free Automated Robust Fine-Tuning Framework | Robust Fine-Tuning (RFT) is a low-cost strategy to obtain adversarial
robustness in downstream applications, without requiring a lot of computational
resources and collecting significant amounts of data. This paper uncovers an
issue with the existing RFT, where optimizing both adversarial and natural
objectives through the feature extractor (FE) yields significantly divergent
gradient directions. This divergence introduces instability in the optimization
process, thereby hindering the attainment of adversarial robustness and
rendering RFT highly sensitive to hyperparameters. To mitigate this issue, we
propose a low-rank (LoRa) branch that disentangles RFT into two distinct
components: optimizing natural objectives via the LoRa branch and adversarial
objectives via the FE. Besides, we introduce heuristic strategies for
automating the scheduling of the learning rate and the scalars of loss terms.
Extensive empirical evaluations demonstrate that our proposed automated RFT
disentangled via the LoRa branch (AutoLoRa) achieves new state-of-the-art
results across a range of downstream tasks. AutoLoRa holds significant
practical utility, as it automatically converts a pre-trained FE into an
adversarially robust model for downstream tasks without the need for searching
hyperparameters. | [
"Xilie Xu",
"Jingfeng Zhang",
"Mohan Kankanhalli"
] | 2023-10-03 06:16:03 | http://arxiv.org/abs/2310.01818v1 | http://arxiv.org/pdf/2310.01818v1 | 2310.01818v1 |
What Determines the Price of NFTs? | In the evolving landscape of digital art, Non-Fungible Tokens (NFTs) have
emerged as a groundbreaking platform, bridging the realms of art and
technology. NFTs serve as the foundational framework that has revolutionized
the market for digital art, enabling artists to showcase and monetize their
creations in unprecedented ways. NFTs combine metadata stored on the blockchain
with off-chain data, such as images, to create a novel form of digital
ownership. It is not fully understood how these factors come together to
determine NFT prices. In this study, we analyze both on-chain and off-chain
data of NFT collections trading on OpenSea to understand what influences NFT
pricing. Our results show that while text and image data of the NFTs can be
used to explain price variations within collections, the extracted features do
not generalize to new, unseen collections. Furthermore, we find that an NFT
collection's trading volume often relates to its online presence, like social
media followers and website traffic. | [
"Vivian Ziemke",
"Benjamin Estermann",
"Roger Wattenhofer",
"Ye Wang"
] | 2023-10-03 06:09:59 | http://arxiv.org/abs/2310.01815v1 | http://arxiv.org/pdf/2310.01815v1 | 2310.01815v1 |
Simulation-based Inference with the Generalized Kullback-Leibler Divergence | In Simulation-based Inference, the goal is to solve the inverse problem when
the likelihood is only known implicitly. Neural Posterior Estimation commonly
fits a normalized density estimator as a surrogate model for the posterior.
This formulation cannot easily fit unnormalized surrogates because it optimizes
the Kullback-Leibler divergence. We propose to optimize a generalized
Kullback-Leibler divergence that accounts for the normalization constant in
unnormalized distributions. The objective recovers Neural Posterior Estimation
when the model class is normalized and unifies it with Neural Ratio Estimation,
combining both into a single objective. We investigate a hybrid model that
offers the best of both worlds by learning a normalized base distribution and a
learned ratio. We also present benchmark results. | [
"Benjamin Kurt Miller",
"Marco Federici",
"Christoph Weniger",
"Patrick Forré"
] | 2023-10-03 05:42:53 | http://arxiv.org/abs/2310.01808v1 | http://arxiv.org/pdf/2310.01808v1 | 2310.01808v1 |
Discrete, compositional, and symbolic representations through attractor dynamics | Compositionality is an important feature of discrete symbolic systems, such
as language and programs, as it enables them to have infinite capacity despite
a finite symbol set. It serves as a useful abstraction for reasoning in both
cognitive science and in AI, yet the interface between continuous and symbolic
processing is often imposed by fiat at the algorithmic level, such as by means
of quantization or a softmax sampling step. In this work, we explore how
discretization could be implemented in a more neurally plausible manner through
the modeling of attractor dynamics that partition the continuous representation
space into basins that correspond to sequences of symbols. Building on
established work in attractor networks and introducing novel training methods,
we show that imposing structure in the symbolic space can produce
compositionality in the attractor-supported representation space of rich
sensory inputs. Lastly, we argue that our model exhibits the process of an
information bottleneck that is thought to play a role in conscious experience,
decomposing the rich information of a sensory input into stable components
encoding symbolic information. | [
"Andrew Nam",
"Eric Elmoznino",
"Nikolay Malkin",
"Chen Sun",
"Yoshua Bengio",
"Guillaume Lajoie"
] | 2023-10-03 05:40:56 | http://arxiv.org/abs/2310.01807v1 | http://arxiv.org/pdf/2310.01807v1 | 2310.01807v1 |
GNNX-BENCH: Unravelling the Utility of Perturbation-based GNN Explainers through In-depth Benchmarking | Numerous explainability methods have been proposed to shed light on the inner
workings of GNNs. Despite the inclusion of empirical evaluations in all the
proposed algorithms, the interrogative aspects of these evaluations lack
diversity. As a result, various facets of explainability pertaining to GNNs,
such as a comparative analysis of counterfactual reasoners, their stability to
variational factors such as different GNN architectures, noise, stochasticity
in non-convex loss surfaces, feasibility amidst domain constraints, and so
forth, have yet to be formally investigated. Motivated by this need, we present
a benchmarking study on perturbation-based explainability methods for GNNs,
aiming to systematically evaluate and compare a wide range of explainability
techniques. Among the key findings of our study, we identify the Pareto-optimal
methods that exhibit superior efficacy and stability in the presence of noise.
Nonetheless, our study reveals that all algorithms are affected by stability
issues when faced with noisy data. Furthermore, we have established that the
current generation of counterfactual explainers often fails to provide feasible
recourses due to violations of topological constraints encoded by
domain-specific considerations. Overall, this benchmarking study empowers
stakeholders in the field of GNNs with a comprehensive understanding of the
state-of-the-art explainability methods, potential research problems for
further enhancement, and the implications of their application in real-world
scenarios. | [
"Mert Kosan",
"Samidha Verma",
"Burouj Armgaan",
"Khushbu Pahwa",
"Ambuj Singh",
"Sourav Medya",
"Sayan Ranu"
] | 2023-10-03 04:42:44 | http://arxiv.org/abs/2310.01794v1 | http://arxiv.org/pdf/2310.01794v1 | 2310.01794v1 |
Can large language models provide useful feedback on research papers? A large-scale empirical analysis | Expert feedback lays the foundation of rigorous research. However, the rapid
growth of scholarly production and intricate knowledge specialization challenge
the conventional scientific feedback mechanisms. High-quality peer reviews are
increasingly difficult to obtain. Researchers who are more junior or from
under-resourced settings have especially hard times getting timely feedback.
With the breakthrough of large language models (LLM) such as GPT-4, there is
growing interest in using LLMs to generate scientific feedback on research
manuscripts. However, the utility of LLM-generated feedback has not been
systematically studied. To address this gap, we created an automated pipeline
using GPT-4 to provide comments on the full PDFs of scientific papers. We
evaluated the quality of GPT-4's feedback through two large-scale studies. We
first quantitatively compared GPT-4's generated feedback with human peer
reviewer feedback in 15 Nature family journals (3,096 papers in total) and the
ICLR machine learning conference (1,709 papers). The overlap in the points
raised by GPT-4 and by human reviewers (average overlap 30.85% for Nature
journals, 39.23% for ICLR) is comparable to the overlap between two human
reviewers (average overlap 28.58% for Nature journals, 35.25% for ICLR). The
overlap between GPT-4 and human reviewers is larger for the weaker papers. We
then conducted a prospective user study with 308 researchers from 110 US
institutions in the field of AI and computational biology to understand how
researchers perceive feedback generated by our GPT-4 system on their own
papers. Overall, more than half (57.4%) of the users found GPT-4 generated
feedback helpful/very helpful and 82.4% found it more beneficial than feedback
from at least some human reviewers. While our findings show that LLM-generated
feedback can help researchers, we also identify several limitations. | [
"Weixin Liang",
"Yuhui Zhang",
"Hancheng Cao",
"Binglu Wang",
"Daisy Ding",
"Xinyu Yang",
"Kailas Vodrahalli",
"Siyu He",
"Daniel Smith",
"Yian Yin",
"Daniel McFarland",
"James Zou"
] | 2023-10-03 04:14:17 | http://arxiv.org/abs/2310.01783v1 | http://arxiv.org/pdf/2310.01783v1 | 2310.01783v1 |
SEA: Sparse Linear Attention with Estimated Attention Mask | The transformer architecture has made breakthroughs in recent years on tasks
which require modeling pairwise relationships between sequential elements, as
is the case in natural language understanding. However, transformers struggle
with long sequences due to the quadratic complexity of the attention operation,
and previous research has aimed to lower the complexity by sparsifying or
linearly approximating the attention matrix. Yet, these approaches cannot
straightforwardly distill knowledge from a teacher's attention matrix, and
often require complete retraining from scratch. Furthermore, previous sparse
and linear approaches may also lose interpretability if they do not produce
full quadratic attention matrices. To address these challenges, we propose SEA:
Sparse linear attention with an Estimated Attention mask. SEA estimates the
attention matrix with linear complexity via kernel-based linear attention, then
creates a sparse approximation to the full attention matrix with a top-k
selection to perform a sparse attention operation. For language modeling tasks
(Wikitext2), previous linear and sparse attention methods show a roughly
two-fold worse perplexity scores over the quadratic OPT-125M baseline, while
SEA achieves an even better perplexity than OPT-125M, using roughly half as
much memory as OPT-125M. Moreover, SEA maintains an interpretable attention
matrix and can utilize knowledge distillation to lower the complexity of
existing pretrained transformers. We believe that our work will have a large
practical impact, as it opens the possibility of running large transformers on
resource-limited devices with less memory. | [
"Heejun Lee",
"Jina Kim",
"Jeffrey Willette",
"Sung Ju Hwang"
] | 2023-10-03 03:56:26 | http://arxiv.org/abs/2310.01777v1 | http://arxiv.org/pdf/2310.01777v1 | 2310.01777v1 |
A simple connection from loss flatness to compressed representations in neural networks | Deep neural networks' generalization capacity has been studied in a variety
of ways, including at least two distinct categories of approach: one based on
the shape of the loss landscape in parameter space, and the other based on the
structure of the representation manifold in feature space (that is, in the
space of unit activities). These two approaches are related, but they are
rarely studied together and explicitly connected. Here, we present a simple
analysis that makes such a connection. We show that, in the last phase of
learning of deep neural networks, compression of the volume of the manifold of
neural representations correlates with the flatness of the loss around the
minima explored by ongoing parameter optimization. We show that this is
predicted by a relatively simple mathematical relationship: loss flatness
implies compression of neural representations. Our results build closely on
prior work of \citet{ma_linear_2021}, which shows how flatness (i.e., small
eigenvalues of the loss Hessian) develops in late phases of learning and lead
to robustness to perturbations in network inputs. Moreover, we show there is no
similarly direct connection between local dimensionality and sharpness,
suggesting that this property may be controlled by different mechanisms than
volume and hence may play a complementary role in neural representations.
Overall, we advance a dual perspective on generalization in neural networks in
both parameter and feature space. | [
"Shirui Chen",
"Stefano Recanatesi",
"Eric Shea-Brown"
] | 2023-10-03 03:36:29 | http://arxiv.org/abs/2310.01770v1 | http://arxiv.org/pdf/2310.01770v1 | 2310.01770v1 |
How Over-Parameterization Slows Down Gradient Descent in Matrix Sensing: The Curses of Symmetry and Initialization | This paper rigorously shows how over-parameterization changes the convergence
behaviors of gradient descent (GD) for the matrix sensing problem, where the
goal is to recover an unknown low-rank ground-truth matrix from near-isotropic
linear measurements. First, we consider the symmetric setting with the
symmetric parameterization where $M^* \in \mathbb{R}^{n \times n}$ is a
positive semi-definite unknown matrix of rank $r \ll n$, and one uses a
symmetric parameterization $XX^\top$ to learn $M^*$. Here $X \in \mathbb{R}^{n
\times k}$ with $k > r$ is the factor matrix. We give a novel $\Omega (1/T^2)$
lower bound of randomly initialized GD for the over-parameterized case ($k >r$)
where $T$ is the number of iterations. This is in stark contrast to the
exact-parameterization scenario ($k=r$) where the convergence rate is $\exp
(-\Omega (T))$. Next, we study asymmetric setting where $M^* \in
\mathbb{R}^{n_1 \times n_2}$ is the unknown matrix of rank $r \ll
\min\{n_1,n_2\}$, and one uses an asymmetric parameterization $FG^\top$ to
learn $M^*$ where $F \in \mathbb{R}^{n_1 \times k}$ and $G \in \mathbb{R}^{n_2
\times k}$. Building on prior work, we give a global exact convergence result
of randomly initialized GD for the exact-parameterization case ($k=r$) with an
$\exp (-\Omega(T))$ rate. Furthermore, we give the first global exact
convergence result for the over-parameterization case ($k>r$) with an
$\exp(-\Omega(\alpha^2 T))$ rate where $\alpha$ is the initialization scale.
This linear convergence result in the over-parameterization case is especially
significant because one can apply the asymmetric parameterization to the
symmetric setting to speed up from $\Omega (1/T^2)$ to linear convergence. On
the other hand, we propose a novel method that only modifies one step of GD and
obtains a convergence rate independent of $\alpha$, recovering the rate in the
exact-parameterization case. | [
"Nuoya Xiong",
"Lijun Ding",
"Simon S. Du"
] | 2023-10-03 03:34:22 | http://arxiv.org/abs/2310.01769v2 | http://arxiv.org/pdf/2310.01769v2 | 2310.01769v2 |
Backdiff: a diffusion model for generalized transferable protein backmapping | Coarse-grained (CG) models play a crucial role in the study of protein
structures, protein thermodynamic properties, and protein conformation
dynamics. Due to the information loss in the coarse-graining process,
backmapping from CG to all-atom configurations is essential in many protein
design and drug discovery applications when detailed atomic representations are
needed for in-depth studies. Despite recent progress in data-driven backmapping
approaches, devising a backmapping method that can be universally applied
across various CG models and proteins remains unresolved. In this work, we
propose BackDiff, a new generative model designed to achieve generalization and
reliability in the protein backmapping problem. BackDiff leverages the
conditional score-based diffusion model with geometric representations. Since
different CG models can contain different coarse-grained sites which include
selected atoms (CG atoms) and simple CG auxiliary functions of atomistic
coordinates (CG auxiliary variables), we design a self-supervised training
framework to adapt to different CG atoms, and constrain the diffusion sampling
paths with arbitrary CG auxiliary variables as conditions. Our method
facilitates end-to-end training and allows efficient sampling across different
proteins and diverse CG models without the need for retraining. Comprehensive
experiments over multiple popular CG models demonstrate BackDiff's superior
performance to existing state-of-the-art approaches, and generalization and
flexibility that these approaches cannot achieve. A pretrained BackDiff model
can offer a convenient yet reliable plug-and-play solution for protein
researchers, enabling them to investigate further from their own CG models. | [
"Yikai Liu",
"Ming Chen",
"Guang Lin"
] | 2023-10-03 03:32:07 | http://arxiv.org/abs/2310.01768v1 | http://arxiv.org/pdf/2310.01768v1 | 2310.01768v1 |
Exploring Counterfactual Alignment Loss towards Human-centered AI | Deep neural networks have demonstrated impressive accuracy in supervised
learning tasks. However, their lack of transparency makes it hard for humans to
trust their results, especially in safe-critic domains such as healthcare. To
address this issue, recent explanation-guided learning approaches proposed to
align the gradient-based attention map to image regions annotated by human
experts, thereby obtaining an intrinsically human-centered model. However, the
attention map these methods are based on may fail to causally attribute the
model predictions, thus compromising their validity for alignment. To address
this issue, we propose a novel human-centered framework based on counterfactual
generation. In particular, we utilize the counterfactual generation's ability
for causal attribution to introduce a novel loss called the CounterFactual
Alignment (CF-Align) loss. This loss guarantees that the features attributed by
the counterfactual generation for the classifier align with the human
annotations. To optimize the proposed loss that entails a counterfactual
generation with an implicit function form, we leverage the implicit function
theorem for backpropagation. Our method is architecture-agnostic and, therefore
can be applied to any neural network. We demonstrate the effectiveness of our
method on a lung cancer diagnosis dataset, showcasing faithful alignment to
humans. | [
"Mingzhou Liu",
"Xinwei Sun",
"Ching-Wen Lee",
"Yu Qiao",
"Yizhou Wang"
] | 2023-10-03 03:20:07 | http://arxiv.org/abs/2310.01766v1 | http://arxiv.org/pdf/2310.01766v1 | 2310.01766v1 |
Data Cleaning and Machine Learning: A Systematic Literature Review | Context: Machine Learning (ML) is integrated into a growing number of systems
for various applications. Because the performance of an ML model is highly
dependent on the quality of the data it has been trained on, there is a growing
interest in approaches to detect and repair data errors (i.e., data cleaning).
Researchers are also exploring how ML can be used for data cleaning; hence
creating a dual relationship between ML and data cleaning. To the best of our
knowledge, there is no study that comprehensively reviews this relationship.
Objective: This paper's objectives are twofold. First, it aims to summarize the
latest approaches for data cleaning for ML and ML for data cleaning. Second, it
provides future work recommendations. Method: We conduct a systematic
literature review of the papers published between 2016 and 2022 inclusively. We
identify different types of data cleaning activities with and for ML: feature
cleaning, label cleaning, entity matching, outlier detection, imputation, and
holistic data cleaning. Results: We summarize the content of 101 papers
covering various data cleaning activities and provide 24 future work
recommendations. Our review highlights many promising data cleaning techniques
that can be further extended. Conclusion: We believe that our review of the
literature will help the community develop better approaches to clean data. | [
"Pierre-Olivier Côté",
"Amin Nikanjam",
"Nafisa Ahmed",
"Dmytro Humeniuk",
"Foutse Khomh"
] | 2023-10-03 03:13:23 | http://arxiv.org/abs/2310.01765v1 | http://arxiv.org/pdf/2310.01765v1 | 2310.01765v1 |
Sampling Multimodal Distributions with the Vanilla Score: Benefits of Data-Based Initialization | There is a long history, as well as a recent explosion of interest, in
statistical and generative modeling approaches based on score functions --
derivatives of the log-likelihood of a distribution. In seminal works,
Hyv\"arinen proposed vanilla score matching as a way to learn distributions
from data by computing an estimate of the score function of the underlying
ground truth, and established connections between this method and established
techniques like Contrastive Divergence and Pseudolikelihood estimation. It is
by now well-known that vanilla score matching has significant difficulties
learning multimodal distributions. Although there are various ways to overcome
this difficulty, the following question has remained unanswered -- is there a
natural way to sample multimodal distributions using just the vanilla score?
Inspired by a long line of related experimental works, we prove that the
Langevin diffusion with early stopping, initialized at the empirical
distribution, and run on a score function estimated from data successfully
generates natural multimodal distributions (mixtures of log-concave
distributions). | [
"Frederic Koehler",
"Thuy-Duong Vuong"
] | 2023-10-03 03:06:59 | http://arxiv.org/abs/2310.01762v1 | http://arxiv.org/pdf/2310.01762v1 | 2310.01762v1 |
Linearization of ReLU Activation Function for Neural Network-Embedded Optimization:Optimal Day-Ahead Energy Scheduling | Neural networks have been widely applied in the power system area. They can
be used for better predicting input information and modeling system performance
with increased accuracy. In some applications such as battery degradation
neural network-based microgrid day-ahead energy scheduling, the input features
of the trained learning model are variables to be solved in optimization models
that enforce limits on the output of the same learning model. This will create
a neural network-embedded optimization problem; the use of nonlinear activation
functions in the neural network will make such problems extremely hard to solve
if not unsolvable. To address this emerging challenge, this paper investigated
different methods for linearizing the nonlinear activation functions with a
particular focus on the widely used rectified linear unit (ReLU) function. Four
linearization methods tailored for the ReLU activation function are developed,
analyzed and compared in this paper. Each method employs a set of linear
constraints to replace the ReLU function, effectively linearizing the
optimization problem, which can overcome the computational challenges
associated with the nonlinearity of the neural network model. These proposed
linearization methods provide valuable tools for effectively solving
optimization problems that integrate neural network models with ReLU activation
functions. | [
"Cunzhi Zhao",
"Xingpeng Li"
] | 2023-10-03 02:47:38 | http://arxiv.org/abs/2310.01758v1 | http://arxiv.org/pdf/2310.01758v1 | 2310.01758v1 |
Improved Algorithms for Adversarial Bandits with Unbounded Losses | We consider the Adversarial Multi-Armed Bandits (MAB) problem with unbounded
losses, where the algorithms have no prior knowledge on the sizes of the
losses. We present UMAB-NN and UMAB-G, two algorithms for non-negative and
general unbounded loss respectively. For non-negative unbounded loss, UMAB-NN
achieves the first adaptive and scale free regret bound without uniform
exploration. Built up on that, we further develop UMAB-G that can learn from
arbitrary unbounded loss. Our analysis reveals the asymmetry between positive
and negative losses in the MAB problem and provide additional insights. We also
accompany our theoretical findings with extensive empirical evaluations,
showing that our algorithms consistently out-performs all existing algorithms
that handles unbounded losses. | [
"Mingyu Chen",
"Xuezhou Zhang"
] | 2023-10-03 02:44:31 | http://arxiv.org/abs/2310.01756v1 | http://arxiv.org/pdf/2310.01756v1 | 2310.01756v1 |
CausalTime: Realistically Generated Time-series for Benchmarking of Causal Discovery | Time-series causal discovery (TSCD) is a fundamental problem of machine
learning. However, existing synthetic datasets cannot properly evaluate or
predict the algorithms' performance on real data. This study introduces the
CausalTime pipeline to generate time-series that highly resemble the real data
and with ground truth causal graphs for quantitative performance evaluation.
The pipeline starts from real observations in a specific scenario and produces
a matching benchmark dataset. Firstly, we harness deep neural networks along
with normalizing flow to accurately capture realistic dynamics. Secondly, we
extract hypothesized causal graphs by performing importance analysis on the
neural network or leveraging prior knowledge. Thirdly, we derive the ground
truth causal graphs by splitting the causal model into causal term, residual
term, and noise term. Lastly, using the fitted network and the derived causal
graph, we generate corresponding versatile time-series proper for algorithm
assessment. In the experiments, we validate the fidelity of the generated data
through qualitative and quantitative experiments, followed by a benchmarking of
existing TSCD algorithms using these generated datasets. CausalTime offers a
feasible solution to evaluating TSCD algorithms in real applications and can be
generalized to a wide range of fields. For easy use of the proposed approach,
we also provide a user-friendly website, hosted on www.causaltime.cc. | [
"Yuxiao Cheng",
"Ziqian Wang",
"Tingxiong Xiao",
"Qin Zhong",
"Jinli Suo",
"Kunlun He"
] | 2023-10-03 02:29:19 | http://arxiv.org/abs/2310.01753v1 | http://arxiv.org/pdf/2310.01753v1 | 2310.01753v1 |
5G Network Slicing: Analysis of Multiple Machine Learning Classifiers | The division of one physical 5G communications infrastructure into several
virtual network slices with distinct characteristics such as bandwidth,
latency, reliability, security, and service quality is known as 5G network
slicing. Each slice is a separate logical network that meets the requirements
of specific services or use cases, such as virtual reality, gaming, autonomous
vehicles, or industrial automation. The network slice can be adjusted
dynamically to meet the changing demands of the service, resulting in a more
cost-effective and efficient approach to delivering diverse services and
applications over a shared infrastructure. This paper assesses various machine
learning techniques, including the logistic regression model, linear
discriminant model, k-nearest neighbor's model, decision tree model, random
forest model, SVC BernoulliNB model, and GaussianNB model, to investigate the
accuracy and precision of each model on detecting network slices. The report
also gives an overview of 5G network slicing. | [
"Mirsad Malkoc",
"Hisham A. Kholidy"
] | 2023-10-03 02:16:50 | http://arxiv.org/abs/2310.01747v1 | http://arxiv.org/pdf/2310.01747v1 | 2310.01747v1 |
Randomized Dimension Reduction with Statistical Guarantees | Large models and enormous data are essential driving forces of the
unprecedented successes achieved by modern algorithms, especially in scientific
computing and machine learning. Nevertheless, the growing dimensionality and
model complexity, as well as the non-negligible workload of data
pre-processing, also bring formidable costs to such successes in both
computation and data aggregation. As the deceleration of Moore's Law slackens
the cost reduction of computation from the hardware level, fast heuristics for
expensive classical routines and efficient algorithms for exploiting limited
data are increasingly indispensable for pushing the limit of algorithm potency.
This thesis explores some of such algorithms for fast execution and efficient
data utilization.
From the computational efficiency perspective, we design and analyze fast
randomized low-rank decomposition algorithms for large matrices based on
"matrix sketching", which can be regarded as a dimension reduction strategy in
the data space. These include the randomized pivoting-based interpolative and
CUR decomposition discussed in Chapter 2 and the randomized subspace
approximations discussed in Chapter 3.
From the sample efficiency perspective, we focus on learning algorithms with
various incorporations of data augmentation that improve generalization and
distributional robustness provably. Specifically, Chapter 4 presents a sample
complexity analysis for data augmentation consistency regularization where we
view sample efficiency from the lens of dimension reduction in the function
space. Then in Chapter 5, we introduce an adaptively weighted data augmentation
consistency regularization algorithm for distributionally robust optimization
with applications in medical image segmentation. | [
"Yijun Dong"
] | 2023-10-03 02:01:39 | http://arxiv.org/abs/2310.01739v1 | http://arxiv.org/pdf/2310.01739v1 | 2310.01739v1 |
Blending Imitation and Reinforcement Learning for Robust Policy Improvement | While reinforcement learning (RL) has shown promising performance, its sample
complexity continues to be a substantial hurdle, restricting its broader
application across a variety of domains. Imitation learning (IL) utilizes
oracles to improve sample efficiency, yet it is often constrained by the
quality of the oracles deployed. which actively interleaves between IL and RL
based on an online estimate of their performance. RPI draws on the strengths of
IL, using oracle queries to facilitate exploration, an aspect that is notably
challenging in sparse-reward RL, particularly during the early stages of
learning. As learning unfolds, RPI gradually transitions to RL, effectively
treating the learned policy as an improved oracle. This algorithm is capable of
learning from and improving upon a diverse set of black-box oracles. Integral
to RPI are Robust Active Policy Selection (RAPS) and Robust Policy Gradient
(RPG), both of which reason over whether to perform state-wise imitation from
the oracles or learn from its own value function when the learner's performance
surpasses that of the oracles in a specific state. Empirical evaluations and
theoretical analysis validate that RPI excels in comparison to existing
state-of-the-art methodologies, demonstrating superior performance across
various benchmark domains. | [
"Xuefeng Liu",
"Takuma Yoneda",
"Rick L. Stevens",
"Matthew R. Walter",
"Yuxin Chen"
] | 2023-10-03 01:55:54 | http://arxiv.org/abs/2310.01737v2 | http://arxiv.org/pdf/2310.01737v2 | 2310.01737v2 |
Nugget: Neural Agglomerative Embeddings of Text | Embedding text sequences is a widespread requirement in modern language
understanding. Existing approaches focus largely on constant-size
representations. This is problematic, as the amount of information contained in
text often varies with the length of the input. We propose a solution called
Nugget, which encodes language into a representation based on a dynamically
selected subset of input tokens. These nuggets are learned through tasks like
autoencoding and machine translation, and intuitively segment language into
meaningful units. We demonstrate Nugget outperforms related approaches in tasks
involving semantic comparison. Finally, we illustrate these compact units allow
for expanding the contextual window of a language model (LM), suggesting new
future LMs that can condition on significantly larger amounts of content. | [
"Guanghui Qin",
"Benjamin Van Durme"
] | 2023-10-03 01:47:49 | http://arxiv.org/abs/2310.01732v1 | http://arxiv.org/pdf/2310.01732v1 | 2310.01732v1 |
Time-LLM: Time Series Forecasting by Reprogramming Large Language Models | Time series forecasting holds significant importance in many real-world
dynamic systems and has been extensively studied. Unlike natural language
process (NLP) and computer vision (CV), where a single large model can tackle
multiple tasks, models for time series forecasting are often specialized,
necessitating distinct designs for different tasks and applications. While
pre-trained foundation models have made impressive strides in NLP and CV, their
development in time series domains has been constrained by data sparsity.
Recent studies have revealed that large language models (LLMs) possess robust
pattern recognition and reasoning abilities over complex sequences of tokens.
However, the challenge remains in effectively aligning the modalities of time
series data and natural language to leverage these capabilities. In this work,
we present Time-LLM, a reprogramming framework to repurpose LLMs for general
time series forecasting with the backbone language models kept intact. We begin
by reprogramming the input time series with text prototypes before feeding it
into the frozen LLM to align the two modalities. To augment the LLM's ability
to reason with time series data, we propose Prompt-as-Prefix (PaP), which
enriches the input context and directs the transformation of reprogrammed input
patches. The transformed time series patches from the LLM are finally projected
to obtain the forecasts. Our comprehensive evaluations demonstrate that
Time-LLM is a powerful time series learner that outperforms state-of-the-art,
specialized forecasting models. Moreover, Time-LLM excels in both few-shot and
zero-shot learning scenarios. | [
"Ming Jin",
"Shiyu Wang",
"Lintao Ma",
"Zhixuan Chu",
"James Y. Zhang",
"Xiaoming Shi",
"Pin-Yu Chen",
"Yuxuan Liang",
"Yuan-Fang Li",
"Shirui Pan",
"Qingsong Wen"
] | 2023-10-03 01:31:25 | http://arxiv.org/abs/2310.01728v1 | http://arxiv.org/pdf/2310.01728v1 | 2310.01728v1 |
Large Language Models for Test-Free Fault Localization | Fault Localization (FL) aims to automatically localize buggy lines of code, a
key first step in many manual and automatic debugging tasks. Previous FL
techniques assume the provision of input tests, and often require extensive
program analysis, program instrumentation, or data preprocessing. Prior work on
deep learning for APR struggles to learn from small datasets and produces
limited results on real-world programs. Inspired by the ability of large
language models (LLMs) of code to adapt to new tasks based on very few
examples, we investigate the applicability of LLMs to line level fault
localization. Specifically, we propose to overcome the left-to-right nature of
LLMs by fine-tuning a small set of bidirectional adapter layers on top of the
representations learned by LLMs to produce LLMAO, the first language model
based fault localization approach that locates buggy lines of code without any
test coverage information. We fine-tune LLMs with 350 million, 6 billion, and
16 billion parameters on small, manually curated corpora of buggy programs such
as the Defects4J corpus. We observe that our technique achieves substantially
more confidence in fault localization when built on the larger models, with bug
localization performance scaling consistently with the LLM size. Our empirical
evaluation shows that LLMAO improves the Top-1 results over the
state-of-the-art machine learning fault localization (MLFL) baselines by
2.3%-54.4%, and Top-5 results by 14.4%-35.6%. LLMAO is also the first FL
technique trained using a language model architecture that can detect security
vulnerabilities down to the code line level. | [
"Aidan Z. H. Yang",
"Ruben Martins",
"Claire Le Goues",
"Vincent J. Hellendoorn"
] | 2023-10-03 01:26:39 | http://arxiv.org/abs/2310.01726v1 | http://arxiv.org/pdf/2310.01726v1 | 2310.01726v1 |
PrACTiS: Perceiver-Attentional Copulas for Time Series | Transformers incorporating copula structures have demonstrated remarkable
performance in time series prediction. However, their heavy reliance on
self-attention mechanisms demands substantial computational resources, thus
limiting their practical utility across a wide range of tasks. In this work, we
present a model that combines the perceiver architecture with a copula
structure to enhance time-series forecasting. By leveraging the perceiver as
the encoder, we efficiently transform complex, high-dimensional, multimodal
data into a compact latent space, thereby significantly reducing computational
demands. To further reduce complexity, we introduce midpoint inference and
local attention mechanisms, enabling the model to capture dependencies within
imputed samples effectively. Subsequently, we deploy the copula-based attention
and output variance testing mechanism to capture the joint distribution of
missing data, while simultaneously mitigating error propagation during
prediction. Our experimental results on the unimodal and multimodal benchmarks
showcase a consistent 20\% improvement over the state-of-the-art methods, while
utilizing less than half of available memory resources. | [
"Cat P. Le",
"Chris Cannella",
"Ali Hasan",
"Yuting Ng",
"Vahid Tarokh"
] | 2023-10-03 01:13:17 | http://arxiv.org/abs/2310.01720v1 | http://arxiv.org/pdf/2310.01720v1 | 2310.01720v1 |
Ensemble Distillation for Unsupervised Constituency Parsing | We investigate the unsupervised constituency parsing task, which organizes
words and phrases of a sentence into a hierarchical structure without using
linguistically annotated data. We observe that existing unsupervised parsers
capture differing aspects of parsing structures, which can be leveraged to
enhance unsupervised parsing performance. To this end, we propose a notion of
"tree averaging," based on which we further propose a novel ensemble method for
unsupervised parsing. To improve inference efficiency, we further distill the
ensemble knowledge into a student model; such an ensemble-then-distill process
is an effective approach to mitigate the over-smoothing problem existing in
common multi-teacher distilling methods. Experiments show that our method
surpasses all previous approaches, consistently demonstrating its effectiveness
and robustness across various runs, with different ensemble components, and
under domain-shift conditions. | [
"Behzad Shayegh",
"Yanshuai Cao",
"Xiaodan Zhu",
"Jackie C. K. Cheung",
"Lili Mou"
] | 2023-10-03 01:02:44 | http://arxiv.org/abs/2310.01717v1 | http://arxiv.org/pdf/2310.01717v1 | 2310.01717v1 |
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