title
stringlengths 9
208
| abstract
stringlengths 280
2.36k
| authors
sequence | published
stringlengths 19
19
| url
stringlengths 33
33
| pdf_url
stringlengths 33
33
| arxiv_id
stringlengths 12
12
|
|---|---|---|---|---|---|---|
Large Language Models as Analogical Reasoners | Chain-of-thought (CoT) prompting for language models demonstrates impressive
performance across reasoning tasks, but typically needs labeled exemplars of
the reasoning process. In this work, we introduce a new prompting approach,
Analogical Prompting, designed to automatically guide the reasoning process of
large language models. Inspired by analogical reasoning, a cognitive process in
which humans draw from relevant past experiences to tackle new problems, our
approach prompts language models to self-generate relevant exemplars or
knowledge in the context, before proceeding to solve the given problem. This
method presents several advantages: it obviates the need for labeling or
retrieving exemplars, offering generality and convenience; it can also tailor
the generated exemplars and knowledge to each problem, offering adaptability.
Experimental results show that our approach outperforms 0-shot CoT and manual
few-shot CoT in a variety of reasoning tasks, including math problem solving in
GSM8K and MATH, code generation in Codeforces, and other reasoning tasks in
BIG-Bench. | [
"Michihiro Yasunaga",
"Xinyun Chen",
"Yujia Li",
"Panupong Pasupat",
"Jure Leskovec",
"Percy Liang",
"Ed H. Chi",
"Denny Zhou"
] | 2023-10-03 00:57:26 | http://arxiv.org/abs/2310.01714v2 | http://arxiv.org/pdf/2310.01714v2 | 2310.01714v2 |
Generative Autoencoding of Dropout Patterns | We propose a generative model termed Deciphering Autoencoders. In this model,
we assign a unique random dropout pattern to each data point in the training
dataset and then train an autoencoder to reconstruct the corresponding data
point using this pattern as information to be encoded. Since the training of
Deciphering Autoencoders relies solely on reconstruction error, it offers more
stable training than other generative models. Despite its simplicity,
Deciphering Autoencoders show comparable sampling quality to DCGAN on the
CIFAR-10 dataset. | [
"Shunta Maeda"
] | 2023-10-03 00:54:13 | http://arxiv.org/abs/2310.01712v1 | http://arxiv.org/pdf/2310.01712v1 | 2310.01712v1 |
On Representation Complexity of Model-based and Model-free Reinforcement Learning | We study the representation complexity of model-based and model-free
reinforcement learning (RL) in the context of circuit complexity. We prove
theoretically that there exists a broad class of MDPs such that their
underlying transition and reward functions can be represented by constant depth
circuits with polynomial size, while the optimal $Q$-function suffers an
exponential circuit complexity in constant-depth circuits. By drawing attention
to the approximation errors and building connections to complexity theory, our
theory provides unique insights into why model-based algorithms usually enjoy
better sample complexity than model-free algorithms from a novel representation
complexity perspective: in some cases, the ground-truth rule (model) of the
environment is simple to represent, while other quantities, such as
$Q$-function, appear complex. We empirically corroborate our theory by
comparing the approximation error of the transition kernel, reward function,
and optimal $Q$-function in various Mujoco environments, which demonstrates
that the approximation errors of the transition kernel and reward function are
consistently lower than those of the optimal $Q$-function. To the best of our
knowledge, this work is the first to study the circuit complexity of RL, which
also provides a rigorous framework for future research. | [
"Hanlin Zhu",
"Baihe Huang",
"Stuart Russell"
] | 2023-10-03 00:01:58 | http://arxiv.org/abs/2310.01706v1 | http://arxiv.org/pdf/2310.01706v1 | 2310.01706v1 |
Transformers are efficient hierarchical chemical graph learners | Transformers, adapted from natural language processing, are emerging as a
leading approach for graph representation learning. Contemporary graph
transformers often treat nodes or edges as separate tokens. This approach leads
to computational challenges for even moderately-sized graphs due to the
quadratic scaling of self-attention complexity with token count. In this paper,
we introduce SubFormer, a graph transformer that operates on subgraphs that
aggregate information by a message-passing mechanism. This approach reduces the
number of tokens and enhances learning long-range interactions. We demonstrate
SubFormer on benchmarks for predicting molecular properties from chemical
structures and show that it is competitive with state-of-the-art graph
transformers at a fraction of the computational cost, with training times on
the order of minutes on a consumer-grade graphics card. We interpret the
attention weights in terms of chemical structures. We show that SubFormer
exhibits limited over-smoothing and avoids over-squashing, which is prevalent
in traditional graph neural networks. | [
"Zihan Pengmei",
"Zimu Li",
"Chih-chan Tien",
"Risi Kondor",
"Aaron R. Dinner"
] | 2023-10-02 23:57:04 | http://arxiv.org/abs/2310.01704v1 | http://arxiv.org/pdf/2310.01704v1 | 2310.01704v1 |
Robustifying State-space Models for Long Sequences via Approximate Diagonalization | State-space models (SSMs) have recently emerged as a framework for learning
long-range sequence tasks. An example is the structured state-space sequence
(S4) layer, which uses the diagonal-plus-low-rank structure of the HiPPO
initialization framework. However, the complicated structure of the S4 layer
poses challenges; and, in an effort to address these challenges, models such as
S4D and S5 have considered a purely diagonal structure. This choice simplifies
the implementation, improves computational efficiency, and allows channel
communication. However, diagonalizing the HiPPO framework is itself an
ill-posed problem. In this paper, we propose a general solution for this and
related ill-posed diagonalization problems in machine learning. We introduce a
generic, backward-stable "perturb-then-diagonalize" (PTD) methodology, which is
based on the pseudospectral theory of non-normal operators, and which may be
interpreted as the approximate diagonalization of the non-normal matrices
defining SSMs. Based on this, we introduce the S4-PTD and S5-PTD models.
Through theoretical analysis of the transfer functions of different
initialization schemes, we demonstrate that the S4-PTD/S5-PTD initialization
strongly converges to the HiPPO framework, while the S4D/S5 initialization only
achieves weak convergences. As a result, our new models show resilience to
Fourier-mode noise-perturbed inputs, a crucial property not achieved by the
S4D/S5 models. In addition to improved robustness, our S5-PTD model averages
87.6% accuracy on the Long-Range Arena benchmark, demonstrating that the PTD
methodology helps to improve the accuracy of deep learning models. | [
"Annan Yu",
"Arnur Nigmetov",
"Dmitriy Morozov",
"Michael W. Mahoney",
"N. Benjamin Erichson"
] | 2023-10-02 23:36:13 | http://arxiv.org/abs/2310.01698v1 | http://arxiv.org/pdf/2310.01698v1 | 2310.01698v1 |
LoFT: Local Proxy Fine-tuning For Improving Transferability Of Adversarial Attacks Against Large Language Model | It has been shown that Large Language Model (LLM) alignments can be
circumvented by appending specially crafted attack suffixes with harmful
queries to elicit harmful responses. To conduct attacks against private target
models whose characterization is unknown, public models can be used as proxies
to fashion the attack, with successful attacks being transferred from public
proxies to private target models. The success rate of attack depends on how
closely the proxy model approximates the private model. We hypothesize that for
attacks to be transferrable, it is sufficient if the proxy can approximate the
target model in the neighborhood of the harmful query. Therefore, in this
paper, we propose \emph{Local Fine-Tuning (LoFT)}, \textit{i.e.}, fine-tuning
proxy models on similar queries that lie in the lexico-semantic neighborhood of
harmful queries to decrease the divergence between the proxy and target models.
First, we demonstrate three approaches to prompt private target models to
obtain similar queries given harmful queries. Next, we obtain data for local
fine-tuning by eliciting responses from target models for the generated similar
queries. Then, we optimize attack suffixes to generate attack prompts and
evaluate the impact of our local fine-tuning on the attack's success rate.
Experiments show that local fine-tuning of proxy models improves attack
transferability and increases attack success rate by $39\%$, $7\%$, and $0.5\%$
(absolute) on target models ChatGPT, GPT-4, and Claude respectively. | [
"Muhammad Ahmed Shah",
"Roshan Sharma",
"Hira Dhamyal",
"Raphael Olivier",
"Ankit Shah",
"Joseph Konan",
"Dareen Alharthi",
"Hazim T Bukhari",
"Massa Baali",
"Soham Deshmukh",
"Michael Kuhlmann",
"Bhiksha Raj",
"Rita Singh"
] | 2023-10-02 23:29:23 | http://arxiv.org/abs/2310.04445v2 | http://arxiv.org/pdf/2310.04445v2 | 2310.04445v2 |
DANI: Fast Diffusion Aware Network Inference with Preserving Topological Structure Property | The fast growth of social networks and their data access limitations in
recent years has led to increasing difficulty in obtaining the complete
topology of these networks. However, diffusion information over these networks
is available, and many algorithms have been proposed to infer the underlying
networks using this information. The previously proposed algorithms only focus
on inferring more links and ignore preserving the critical topological
characteristics of the underlying social networks. In this paper, we propose a
novel method called DANI to infer the underlying network while preserving its
structural properties. It is based on the Markov transition matrix derived from
time series cascades, as well as the node-node similarity that can be observed
in the cascade behavior from a structural point of view. In addition, the
presented method has linear time complexity (increases linearly with the number
of nodes, number of cascades, and square of the average length of cascades),
and its distributed version in the MapReduce framework is also scalable. We
applied the proposed approach to both real and synthetic networks. The
experimental results showed that DANI has higher accuracy and lower run time
while maintaining structural properties, including modular structure, degree
distribution, connected components, density, and clustering coefficients, than
well-known network inference methods. | [
"Maryam Ramezani",
"Aryan Ahadinia",
"Erfan Farhadi",
"Hamid R. Rabiee"
] | 2023-10-02 23:23:00 | http://arxiv.org/abs/2310.01696v1 | http://arxiv.org/pdf/2310.01696v1 | 2310.01696v1 |
Forecasting Tropical Cyclones with Cascaded Diffusion Models | As cyclones become more intense due to climate change, the rise of AI-based
modelling provides a more affordable and accessible approach compared to
traditional methods based on mathematical models. This work leverages diffusion
models to forecast cyclone trajectories and precipitation patterns by
integrating satellite imaging, remote sensing, and atmospheric data, employing
a cascaded approach that incorporates forecasting, super-resolution, and
precipitation modelling, with training on a dataset of 51 cyclones from six
major basins. Experiments demonstrate that the final forecasts from the
cascaded models show accurate predictions up to a 36-hour rollout, with SSIM
and PSNR values exceeding 0.5 and 20 dB, respectively, for all three tasks.
This work also highlights the promising efficiency of AI methods such as
diffusion models for high-performance needs, such as cyclone forecasting, while
remaining computationally affordable, making them ideal for highly vulnerable
regions with critical forecasting needs and financial limitations. Code
accessible at \url{https://github.com/nathzi1505/forecast-diffmodels}. | [
"Pritthijit Nath",
"Pancham Shukla",
"César Quilodrán-Casas"
] | 2023-10-02 23:09:59 | http://arxiv.org/abs/2310.01690v2 | http://arxiv.org/pdf/2310.01690v2 | 2310.01690v2 |
From Stability to Chaos: Analyzing Gradient Descent Dynamics in Quadratic Regression | We conduct a comprehensive investigation into the dynamics of gradient
descent using large-order constant step-sizes in the context of quadratic
regression models. Within this framework, we reveal that the dynamics can be
encapsulated by a specific cubic map, naturally parameterized by the step-size.
Through a fine-grained bifurcation analysis concerning the step-size parameter,
we delineate five distinct training phases: (1) monotonic, (2) catapult, (3)
periodic, (4) chaotic, and (5) divergent, precisely demarcating the boundaries
of each phase. As illustrations, we provide examples involving phase retrieval
and two-layer neural networks employing quadratic activation functions and
constant outer-layers, utilizing orthogonal training data. Our simulations
indicate that these five phases also manifest with generic non-orthogonal data.
We also empirically investigate the generalization performance when training in
the various non-monotonic (and non-divergent) phases. In particular, we observe
that performing an ergodic trajectory averaging stabilizes the test error in
non-monotonic (and non-divergent) phases. | [
"Xuxing Chen",
"Krishnakumar Balasubramanian",
"Promit Ghosal",
"Bhavya Agrawalla"
] | 2023-10-02 22:59:17 | http://arxiv.org/abs/2310.01687v1 | http://arxiv.org/pdf/2310.01687v1 | 2310.01687v1 |
A Framework for Interpretability in Machine Learning for Medical Imaging | Interpretability for machine learning models in medical imaging (MLMI) is an
important direction of research. However, there is a general sense of murkiness
in what interpretability means. Why does the need for interpretability in MLMI
arise? What goals does one actually seek to address when interpretability is
needed? To answer these questions, we identify a need to formalize the goals
and elements of interpretability in MLMI. By reasoning about real-world tasks
and goals common in both medical image analysis and its intersection with
machine learning, we identify four core elements of interpretability:
localization, visual recognizability, physical attribution, and transparency.
Overall, this paper formalizes interpretability needs in the context of medical
imaging, and our applied perspective clarifies concrete MLMI-specific goals and
considerations in order to guide method design and improve real-world usage.
Our goal is to provide practical and didactic information for model designers
and practitioners, inspire developers of models in the medical imaging field to
reason more deeply about what interpretability is achieving, and suggest future
directions of interpretability research. | [
"Alan Q. Wang",
"Batuhan K. Karaman",
"Heejong Kim",
"Jacob Rosenthal",
"Rachit Saluja",
"Sean I. Young",
"Mert R. Sabuncu"
] | 2023-10-02 22:46:49 | http://arxiv.org/abs/2310.01685v1 | http://arxiv.org/pdf/2310.01685v1 | 2310.01685v1 |
Designing User-Centric Behavioral Interventions to Prevent Dysglycemia with Novel Counterfactual Explanations | Maintaining normal blood glucose levels through lifestyle behaviors is
central to maintaining health and preventing disease. Frequent exposure to
dysglycemia (i.e., abnormal glucose events such as hyperlycemia and
hypoglycemia) leads to chronic complications including diabetes, kidney disease
and need for dialysis, myocardial infarction, stroke, amputation, and death.
Therefore, a tool capable of predicting dysglycemia and offering users
actionable feedback about how to make changes in their diet, exercise, and
medication to prevent abnormal glycemic events could have significant societal
impacts. Counterfactual explanations can provide insights into why a model made
a particular prediction by generating hypothetical instances that are similar
to the original input but lead to a different prediction outcome. Therefore,
counterfactuals can be viewed as a means to design AI-driven health
interventions to prevent adverse health outcomes such as dysglycemia. In this
paper, we design GlyCoach, a framework for generating counterfactual
explanations for glucose control. Leveraging insights from adversarial
learning, GlyCoach characterizes the decision boundary for high-dimensional
health data and performs a grid search to generate actionable interventions.
GlyCoach is unique in integrating prior knowledge about user preferences of
plausible explanations into the process of counterfactual generation. We
evaluate GlyCoach extensively using two real-world datasets and external
simulators from prior studies that predict glucose response. GlyCoach achieves
87\% sensitivity in the simulation-aided validation, surpassing the
state-of-the-art techniques for generating counterfactual explanations by at
least $10\%$. Besides, counterfactuals from GlyCoach exhibit a $32\%$ improved
normalized distance compared to previous research. | [
"Asiful Arefeen",
"Hassan Ghasemzadeh"
] | 2023-10-02 22:42:52 | http://arxiv.org/abs/2310.01684v1 | http://arxiv.org/pdf/2310.01684v1 | 2310.01684v1 |
Commutative Width and Depth Scaling in Deep Neural Networks | This paper is the second in the series Commutative Scaling of Width and Depth
(WD) about commutativity of infinite width and depth limits in deep neural
networks. Our aim is to understand the behaviour of neural functions (functions
that depend on a neural network model) as width and depth go to infinity (in
some sense), and eventually identify settings under which commutativity holds,
i.e. the neural function tends to the same limit no matter how width and depth
limits are taken. In this paper, we formally introduce and define the
commutativity framework, and discuss its implications on neural network design
and scaling. We study commutativity for the neural covariance kernel which
reflects how network layers separate data. Our findings extend previous results
established in [55] by showing that taking the width and depth to infinity in a
deep neural network with skip connections, when branches are suitably scaled to
avoid exploding behaviour, result in the same covariance structure no matter
how that limit is taken. This has a number of theoretical and practical
implications that we discuss in the paper. The proof techniques in this paper
are novel and rely on tools that are more accessible to readers who are not
familiar with stochastic calculus (used in the proofs of WD(I))). | [
"Soufiane Hayou"
] | 2023-10-02 22:39:09 | http://arxiv.org/abs/2310.01683v1 | http://arxiv.org/pdf/2310.01683v1 | 2310.01683v1 |
What's the Magic Word? A Control Theory of LLM Prompting | Prompt engineering is effective and important in the deployment of LLMs but
is poorly understood mathematically. Here, we formalize prompt engineering as
an optimal control problem on LLMs -- where the prompt is considered a control
variable for modulating the output distribution of the LLM. Within this
framework, we ask a simple question: given a sequence of tokens, does there
always exist a prompt we can prepend that will steer the LLM toward accurately
predicting the final token? We call such an optimal prompt the magic word since
prepending the prompt causes the LLM to output the correct answer. If magic
words exist, can we find them? If so, what are their properties? We offer
analytic analysis on the controllability of the self-attention head where we
prove a bound on controllability as a function of the singular values of its
weight matrices. We take inspiration from control theory to propose a metric
called $k-\epsilon$ controllability to characterize LLM steerability. We
compute the $k-\epsilon$ controllability of a panel of large language models,
including Falcon-7b, Llama-7b, and Falcon-40b on 5000 WikiText causal language
modeling tasks. Remarkably, we find that magic words of 10 tokens or less exist
for over 97% of WikiText instances surveyed for each model. | [
"Aman Bhargava",
"Cameron Witkowski",
"Manav Shah",
"Matt Thomson"
] | 2023-10-02 22:35:40 | http://arxiv.org/abs/2310.04444v2 | http://arxiv.org/pdf/2310.04444v2 | 2310.04444v2 |
Estimating and Implementing Conventional Fairness Metrics With Probabilistic Protected Features | The vast majority of techniques to train fair models require access to the
protected attribute (e.g., race, gender), either at train time or in
production. However, in many important applications this protected attribute is
largely unavailable. In this paper, we develop methods for measuring and
reducing fairness violations in a setting with limited access to protected
attribute labels. Specifically, we assume access to protected attribute labels
on a small subset of the dataset of interest, but only probabilistic estimates
of protected attribute labels (e.g., via Bayesian Improved Surname Geocoding)
for the rest of the dataset. With this setting in mind, we propose a method to
estimate bounds on common fairness metrics for an existing model, as well as a
method for training a model to limit fairness violations by solving a
constrained non-convex optimization problem. Unlike similar existing
approaches, our methods take advantage of contextual information --
specifically, the relationships between a model's predictions and the
probabilistic prediction of protected attributes, given the true protected
attribute, and vice versa -- to provide tighter bounds on the true disparity.
We provide an empirical illustration of our methods using voting data. First,
we show our measurement method can bound the true disparity up to 5.5x tighter
than previous methods in these applications. Then, we demonstrate that our
training technique effectively reduces disparity while incurring lesser
fairness-accuracy trade-offs than other fair optimization methods with limited
access to protected attributes. | [
"Hadi Elzayn",
"Emily Black",
"Patrick Vossler",
"Nathanael Jo",
"Jacob Goldin",
"Daniel E. Ho"
] | 2023-10-02 22:30:25 | http://arxiv.org/abs/2310.01679v1 | http://arxiv.org/pdf/2310.01679v1 | 2310.01679v1 |
Score dynamics: scaling molecular dynamics with picosecond timesteps via conditional diffusion model | We propose score dynamics (SD), a general framework for learning effective
evolution operators for atomistic as well as coarse-grained dynamics from
molecular-dynamics (MD) simulations. SD is centered around scores, or
derivatives of the transition log-probability with respect to the dynamical
degrees of freedom. The latter play the same role as force fields in MD but are
used in denoising diffusion probability models to generate discrete transitions
of the dynamical variables in an SD timestep, which can be orders of magnitude
larger than a typical MD timestep. In this work, we construct graph neural
network based score dynamics models of realistic molecular systems that are
evolved with 1~ps timesteps. We demonstrate the efficacy of score dynamics with
case studies of alanine dipeptide and short alkanes in aqueous solution. Both
equilibrium predictions derived from the stationary distributions of the
conditional probability and kinetic predictions for the transition rates and
transition paths are in good agreement with MD at about 8-18 fold wall-clock
speedup. Open challenges and possible future remedies to improve score dynamics
are also discussed. | [
"Tim Hsu",
"Babak Sadigh",
"Vasily Bulatov",
"Fei Zhou"
] | 2023-10-02 22:29:45 | http://arxiv.org/abs/2310.01678v1 | http://arxiv.org/pdf/2310.01678v1 | 2310.01678v1 |
Locality-Aware Graph-Rewiring in GNNs | Graph Neural Networks (GNNs) are popular models for machine learning on
graphs that typically follow the message-passing paradigm, whereby the feature
of a node is updated recursively upon aggregating information over its
neighbors. While exchanging messages over the input graph endows GNNs with a
strong inductive bias, it can also make GNNs susceptible to over-squashing,
thereby preventing them from capturing long-range interactions in the given
graph. To rectify this issue, graph rewiring techniques have been proposed as a
means of improving information flow by altering the graph connectivity. In this
work, we identify three desiderata for graph-rewiring: (i) reduce
over-squashing, (ii) respect the locality of the graph, and (iii) preserve the
sparsity of the graph. We highlight fundamental trade-offs that occur between
spatial and spectral rewiring techniques; while the former often satisfy (i)
and (ii) but not (iii), the latter generally satisfy (i) and (iii) at the
expense of (ii). We propose a novel rewiring framework that satisfies all of
(i)--(iii) through a locality-aware sequence of rewiring operations. We then
discuss a specific instance of such rewiring framework and validate its
effectiveness on several real-world benchmarks, showing that it either matches
or significantly outperforms existing rewiring approaches. | [
"Federico Barbero",
"Ameya Velingker",
"Amin Saberi",
"Michael Bronstein",
"Francesco Di Giovanni"
] | 2023-10-02 21:59:44 | http://arxiv.org/abs/2310.01668v1 | http://arxiv.org/pdf/2310.01668v1 | 2310.01668v1 |
Artemis: HE-Aware Training for Efficient Privacy-Preserving Machine Learning | Privacy-Preserving ML (PPML) based on Homomorphic Encryption (HE) is a
promising foundational privacy technology. Making it more practical requires
lowering its computational cost, especially, in handling modern large deep
neural networks. Model compression via pruning is highly effective in
conventional plaintext ML but cannot be effectively applied to HE-PPML as is.
We propose Artemis, a highly effective DNN pruning technique for HE-based
inference. We judiciously investigate two HE-aware pruning strategies
(positional and diagonal) to reduce the number of Rotation operations, which
dominate compute time in HE convolution. We find that Pareto-optimal solutions
are based fully on diagonal pruning. Artemis' benefits come from coupling DNN
training, driven by a novel group Lasso regularization objective, with pruning
to maximize HE-specific cost reduction (dominated by the Rotation operations).
We show that Artemis improves on prior HE-oriented pruning and can achieve a
1.2-6x improvement when targeting modern convolutional models (ResNet18 and
ResNet18) across three datasets. | [
"Yeonsoo Jeon",
"Mattan Erez",
"Michael Orshansky"
] | 2023-10-02 21:53:24 | http://arxiv.org/abs/2310.01664v1 | http://arxiv.org/pdf/2310.01664v1 | 2310.01664v1 |
Home Electricity Data Generator (HEDGE): An open-access tool for the generation of electric vehicle, residential demand, and PV generation profiles | In this paper, we present the Home Electricity Data Generator (HEDGE), an
open-access tool for the random generation of realistic residential energy
data. HEDGE generates realistic daily profiles of residential PV generation,
household electric loads, and electric vehicle consumption and at-home
availability, based on real-life UK datasets. The lack of usable data is a
major hurdle for research on residential distributed energy resources
characterisation and coordination, especially when using data-driven methods
such as machine learning-based forecasting and reinforcement learning-based
control. A key issue is that while large data banks are available, they are not
in a usable format, and numerous subsequent days of data for a given single
home are unavailable. We fill these gaps with the open-access HEDGE tool which
generates data sequences of energy data for several days in a way that is
consistent for single homes, both in terms of profile magnitude and behavioural
clusters. From raw datasets, pre-processing steps are conducted, including
filling in incomplete data sequences and clustering profiles into behaviour
clusters. Generative adversarial networks (GANs) are then trained to generate
realistic synthetic data representative of each behaviour groups consistent
with real-life behavioural and physical patterns. | [
"Flora Charbonnier",
"Thomas Morstyn",
"Malcolm McCulloch"
] | 2023-10-02 21:51:42 | http://arxiv.org/abs/2310.01661v1 | http://arxiv.org/pdf/2310.01661v1 | 2310.01661v1 |
REMEDI: REinforcement learning-driven adaptive MEtabolism modeling of primary sclerosing cholangitis DIsease progression | Primary sclerosing cholangitis (PSC) is a rare disease wherein altered bile
acid metabolism contributes to sustained liver injury. This paper introduces
REMEDI, a framework that captures bile acid dynamics and the body's adaptive
response during PSC progression that can assist in exploring treatments. REMEDI
merges a differential equation (DE)-based mechanistic model that describes bile
acid metabolism with reinforcement learning (RL) to emulate the body's
adaptations to PSC continuously. An objective of adaptation is to maintain
homeostasis by regulating enzymes involved in bile acid metabolism. These
enzymes correspond to the parameters of the DEs. REMEDI leverages RL to
approximate adaptations in PSC, treating homeostasis as a reward signal and the
adjustment of the DE parameters as the corresponding actions. On real-world
data, REMEDI generated bile acid dynamics and parameter adjustments consistent
with published findings. Also, our results support discussions in the
literature that early administration of drugs that suppress bile acid synthesis
may be effective in PSC treatment. | [
"Chang Hu",
"Krishnakant V. Saboo",
"Ahmad H. Ali",
"Brian D. Juran",
"Konstantinos N. Lazaridis",
"Ravishankar K. Iyer"
] | 2023-10-02 21:46:01 | http://arxiv.org/abs/2310.01426v1 | http://arxiv.org/pdf/2310.01426v1 | 2310.01426v1 |
PolySketchFormer: Fast Transformers via Sketches for Polynomial Kernels | The quadratic complexity of attention in transformer architectures remains a
big bottleneck in scaling up large foundation models for long context. In fact,
recent theoretical results show the hardness of approximating the output of
softmax attention mechanism in sub-quadratic time assuming Strong Exponential
Time Hypothesis. In this paper, we show how to break this theoretical barrier
by replacing softmax with a polynomial function and polynomial sketching. In
particular we show that sketches for Polynomial Kernel from the randomized
numerical linear algebra literature can be used to approximate the polynomial
attention which leads to a significantly faster attention mechanism without
assuming any sparse structure for the attention matrix that has been done in
many previous works.
In addition, we propose an efficient block-based algorithm that lets us apply
the causal mask to the attention matrix without explicitly realizing the $n
\times n$ attention matrix and compute the output of the polynomial attention
mechanism in time linear in the context length. The block-based algorithm gives
significant speedups over the \emph{cumulative sum} algorithm used by Performer
to apply the causal mask to the attention matrix. These observations help us
design \emph{PolySketchFormer}, a practical linear-time transformer
architecture for language modeling with provable guarantees.
We validate our design empirically by training language models with long
context lengths. We first show that the eval perplexities of our models are
comparable to that of models trained with softmax attention. We then show that
for large context lengths our training times are significantly faster than
FlashAttention. | [
"Praneeth Kacham",
"Vahab Mirrokni",
"Peilin Zhong"
] | 2023-10-02 21:39:04 | http://arxiv.org/abs/2310.01655v1 | http://arxiv.org/pdf/2310.01655v1 | 2310.01655v1 |
Fool Your (Vision and) Language Model With Embarrassingly Simple Permutations | Large language and vision-language models are rapidly being deployed in
practice thanks to their impressive capabilities in instruction following,
in-context learning, and so on. This raises an urgent need to carefully analyse
their robustness so that stakeholders can understand if and when such models
are trustworthy enough to be relied upon in any given application. In this
paper, we highlight a specific vulnerability in popular models, namely
permutation sensitivity in multiple-choice question answering (MCQA).
Specifically, we show empirically that popular models are vulnerable to
adversarial permutation in answer sets for multiple-choice prompting, which is
surprising as models should ideally be as invariant to prompt permutation as
humans are. These vulnerabilities persist across various model sizes, and exist
in very recent language and vision-language models. Code is available at
\url{https://github.com/ys-zong/FoolyourVLLMs}. | [
"Yongshuo Zong",
"Tingyang Yu",
"Bingchen Zhao",
"Ruchika Chavhan",
"Timothy Hospedales"
] | 2023-10-02 21:27:57 | http://arxiv.org/abs/2310.01651v1 | http://arxiv.org/pdf/2310.01651v1 | 2310.01651v1 |
CoDBench: A Critical Evaluation of Data-driven Models for Continuous Dynamical Systems | Continuous dynamical systems, characterized by differential equations, are
ubiquitously used to model several important problems: plasma dynamics, flow
through porous media, weather forecasting, and epidemic dynamics. Recently, a
wide range of data-driven models has been used successfully to model these
systems. However, in contrast to established fields like computer vision,
limited studies are available analyzing the strengths and potential
applications of different classes of these models that could steer
decision-making in scientific machine learning. Here, we introduce CodBench, an
exhaustive benchmarking suite comprising 11 state-of-the-art data-driven models
for solving differential equations. Specifically, we comprehensively evaluate 4
distinct categories of models, viz., feed forward neural networks, deep
operator regression models, frequency-based neural operators, and transformer
architectures against 8 widely applicable benchmark datasets encompassing
challenges from fluid and solid mechanics. We conduct extensive experiments,
assessing the operators' capabilities in learning, zero-shot super-resolution,
data efficiency, robustness to noise, and computational efficiency.
Interestingly, our findings highlight that current operators struggle with the
newer mechanics datasets, motivating the need for more robust neural operators.
All the datasets and codes will be shared in an easy-to-use fashion for the
scientific community. We hope this resource will be an impetus for accelerated
progress and exploration in modeling dynamical systems. | [
"Priyanshu Burark",
"Karn Tiwari",
"Meer Mehran Rashid",
"Prathosh A P",
"N M Anoop Krishnan"
] | 2023-10-02 21:27:54 | http://arxiv.org/abs/2310.01650v1 | http://arxiv.org/pdf/2310.01650v1 | 2310.01650v1 |
On Training Derivative-Constrained Neural Networks | We refer to the setting where the (partial) derivatives of a neural network's
(NN's) predictions with respect to its inputs are used as additional training
signal as a derivative-constrained (DC) NN. This situation is common in
physics-informed settings in the natural sciences. We propose an integrated
RELU (IReLU) activation function to improve training of DC NNs. We also
investigate denormalization and label rescaling to help stabilize DC training.
We evaluate our methods on physics-informed settings including quantum
chemistry and Scientific Machine Learning (SciML) tasks. We demonstrate that
existing architectures with IReLU activations combined with denormalization and
label rescaling better incorporate training signal provided by derivative
constraints. | [
"KaiChieh Lo",
"Daniel Huang"
] | 2023-10-02 21:23:31 | http://arxiv.org/abs/2310.01649v2 | http://arxiv.org/pdf/2310.01649v2 | 2310.01649v2 |
Equivariant Adaptation of Large Pre-Trained Models | Equivariant networks are specifically designed to ensure consistent behavior
with respect to a set of input transformations, leading to higher sample
efficiency and more accurate and robust predictions. However, redesigning each
component of prevalent deep neural network architectures to achieve chosen
equivariance is a difficult problem and can result in a computationally
expensive network during both training and inference. A recently proposed
alternative towards equivariance that removes the architectural constraints is
to use a simple canonicalization network that transforms the input to a
canonical form before feeding it to an unconstrained prediction network. We
show here that this approach can effectively be used to make a large
pre-trained network equivariant. However, we observe that the produced
canonical orientations can be misaligned with those of the training
distribution, hindering performance. Using dataset-dependent priors to inform
the canonicalization function, we are able to make large pre-trained models
equivariant while maintaining their performance. This significantly improves
the robustness of these models to deterministic transformations of the data,
such as rotations. We believe this equivariant adaptation of large pre-trained
models can help their domain-specific applications with known symmetry priors. | [
"Arnab Kumar Mondal",
"Siba Smarak Panigrahi",
"Sékou-Oumar Kaba",
"Sai Rajeswar",
"Siamak Ravanbakhsh"
] | 2023-10-02 21:21:28 | http://arxiv.org/abs/2310.01647v1 | http://arxiv.org/pdf/2310.01647v1 | 2310.01647v1 |
Deep Insights into Noisy Pseudo Labeling on Graph Data | Pseudo labeling (PL) is a wide-applied strategy to enlarge the labeled
dataset by self-annotating the potential samples during the training process.
Several works have shown that it can improve the graph learning model
performance in general. However, we notice that the incorrect labels can be
fatal to the graph training process. Inappropriate PL may result in the
performance degrading, especially on graph data where the noise can propagate.
Surprisingly, the corresponding error is seldom theoretically analyzed in the
literature. In this paper, we aim to give deep insights of PL on graph learning
models. We first present the error analysis of PL strategy by showing that the
error is bounded by the confidence of PL threshold and consistency of
multi-view prediction. Then, we theoretically illustrate the effect of PL on
convergence property. Based on the analysis, we propose a cautious pseudo
labeling methodology in which we pseudo label the samples with highest
confidence and multi-view consistency. Finally, extensive experiments
demonstrate that the proposed strategy improves graph learning process and
outperforms other PL strategies on link prediction and node classification
tasks. | [
"Botao Wang",
"Jia Li",
"Yang Liu",
"Jiashun Cheng",
"Yu Rong",
"Wenjia Wang",
"Fugee Tsung"
] | 2023-10-02 20:57:11 | http://arxiv.org/abs/2310.01634v1 | http://arxiv.org/pdf/2310.01634v1 | 2310.01634v1 |
Imitation Learning from Observation through Optimal Transport | Imitation Learning from Observation (ILfO) is a setting in which a learner
tries to imitate the behavior of an expert, using only observational data and
without the direct guidance of demonstrated actions. In this paper, we
re-examine the use of optimal transport for IL, in which a reward is generated
based on the Wasserstein distance between the state trajectories of the learner
and expert. We show that existing methods can be simplified to generate a
reward function without requiring learned models or adversarial learning.
Unlike many other state-of-the-art methods, our approach can be integrated with
any RL algorithm, and is amenable to ILfO. We demonstrate the effectiveness of
this simple approach on a variety of continuous control tasks and find that it
surpasses the state of the art in the IlfO setting, achieving expert-level
performance across a range of evaluation domains even when observing only a
single expert trajectory without actions. | [
"Wei-Di Chang",
"Scott Fujimoto",
"David Meger",
"Gregory Dudek"
] | 2023-10-02 20:53:20 | http://arxiv.org/abs/2310.01632v1 | http://arxiv.org/pdf/2310.01632v1 | 2310.01632v1 |
Operator Learning Meets Numerical Analysis: Improving Neural Networks through Iterative Methods | Deep neural networks, despite their success in numerous applications, often
function without established theoretical foundations. In this paper, we bridge
this gap by drawing parallels between deep learning and classical numerical
analysis. By framing neural networks as operators with fixed points
representing desired solutions, we develop a theoretical framework grounded in
iterative methods for operator equations. Under defined conditions, we present
convergence proofs based on fixed point theory. We demonstrate that popular
architectures, such as diffusion models and AlphaFold, inherently employ
iterative operator learning. Empirical assessments highlight that performing
iterations through network operators improves performance. We also introduce an
iterative graph neural network, PIGN, that further demonstrates benefits of
iterations. Our work aims to enhance the understanding of deep learning by
merging insights from numerical analysis, potentially guiding the design of
future networks with clearer theoretical underpinnings and improved
performance. | [
"Emanuele Zappala",
"Daniel Levine",
"Sizhuang He",
"Syed Rizvi",
"Sacha Levy",
"David van Dijk"
] | 2023-10-02 20:25:36 | http://arxiv.org/abs/2310.01618v1 | http://arxiv.org/pdf/2310.01618v1 | 2310.01618v1 |
Sample-Efficiency in Multi-Batch Reinforcement Learning: The Need for Dimension-Dependent Adaptivity | We theoretically explore the relationship between sample-efficiency and
adaptivity in reinforcement learning. An algorithm is sample-efficient if it
uses a number of queries $n$ to the environment that is polynomial in the
dimension $d$ of the problem. Adaptivity refers to the frequency at which
queries are sent and feedback is processed to update the querying strategy. To
investigate this interplay, we employ a learning framework that allows sending
queries in $K$ batches, with feedback being processed and queries updated after
each batch. This model encompasses the whole adaptivity spectrum, ranging from
non-adaptive 'offline' ($K=1$) to fully adaptive ($K=n$) scenarios, and regimes
in between. For the problems of policy evaluation and best-policy
identification under $d$-dimensional linear function approximation, we
establish $\Omega(\log \log d)$ lower bounds on the number of batches $K$
required for sample-efficient algorithms with $n = O(poly(d))$ queries. Our
results show that just having adaptivity ($K>1$) does not necessarily guarantee
sample-efficiency. Notably, the adaptivity-boundary for sample-efficiency is
not between offline reinforcement learning ($K=1$), where sample-efficiency was
known to not be possible, and adaptive settings. Instead, the boundary lies
between different regimes of adaptivity and depends on the problem dimension. | [
"Emmeran Johnson",
"Ciara Pike-Burke",
"Patrick Rebeschini"
] | 2023-10-02 20:14:01 | http://arxiv.org/abs/2310.01616v1 | http://arxiv.org/pdf/2310.01616v1 | 2310.01616v1 |
Intractability of Learning the Discrete Logarithm with Gradient-Based Methods | The discrete logarithm problem is a fundamental challenge in number theory
with significant implications for cryptographic protocols. In this paper, we
investigate the limitations of gradient-based methods for learning the parity
bit of the discrete logarithm in finite cyclic groups of prime order. Our main
result, supported by theoretical analysis and empirical verification, reveals
the concentration of the gradient of the loss function around a fixed point,
independent of the logarithm's base used. This concentration property leads to
a restricted ability to learn the parity bit efficiently using gradient-based
methods, irrespective of the complexity of the network architecture being
trained.
Our proof relies on Boas-Bellman inequality in inner product spaces and it
involves establishing approximate orthogonality of discrete logarithm's parity
bit functions through the spectral norm of certain matrices. Empirical
experiments using a neural network-based approach further verify the
limitations of gradient-based learning, demonstrating the decreasing success
rate in predicting the parity bit as the group order increases. | [
"Rustem Takhanov",
"Maxat Tezekbayev",
"Artur Pak",
"Arman Bolatov",
"Zhibek Kadyrsizova",
"Zhenisbek Assylbekov"
] | 2023-10-02 20:01:12 | http://arxiv.org/abs/2310.01611v1 | http://arxiv.org/pdf/2310.01611v1 | 2310.01611v1 |
Adversarial Contextual Bandits Go Kernelized | We study a generalization of the problem of online learning in adversarial
linear contextual bandits by incorporating loss functions that belong to a
reproducing kernel Hilbert space, which allows for a more flexible modeling of
complex decision-making scenarios. We propose a computationally efficient
algorithm that makes use of a new optimistically biased estimator for the loss
functions and achieves near-optimal regret guarantees under a variety of
eigenvalue decay assumptions made on the underlying kernel. Specifically, under
the assumption of polynomial eigendecay with exponent $c>1$, the regret is
$\widetilde{O}(KT^{\frac{1}{2}(1+\frac{1}{c})})$, where $T$ denotes the number
of rounds and $K$ the number of actions. Furthermore, when the eigendecay
follows an exponential pattern, we achieve an even tighter regret bound of
$\widetilde{O}(\sqrt{T})$. These rates match the lower bounds in all special
cases where lower bounds are known at all, and match the best known upper
bounds available for the more well-studied stochastic counterpart of our
problem. | [
"Gergely Neu",
"Julia Olkhovskaya",
"Sattar Vakili"
] | 2023-10-02 19:59:39 | http://arxiv.org/abs/2310.01609v1 | http://arxiv.org/pdf/2310.01609v1 | 2310.01609v1 |
Solving the Quadratic Assignment Problem using Deep Reinforcement Learning | The Quadratic Assignment Problem (QAP) is an NP-hard problem which has proven
particularly challenging to solve: unlike other combinatorial problems like the
traveling salesman problem (TSP), which can be solved to optimality for
instances with hundreds or even thousands of locations using advanced integer
programming techniques, no methods are known to exactly solve QAP instances of
size greater than 30. Solving the QAP is nevertheless important because of its
many critical applications, such as electronic wiring design and facility
layout selection. We propose a method to solve the original Koopmans-Beckman
formulation of the QAP using deep reinforcement learning. Our approach relies
on a novel double pointer network, which alternates between selecting a
location in which to place the next facility and a facility to place in the
previous location. We train our model using A2C on a large dataset of synthetic
instances, producing solutions with no instance-specific retraining necessary.
Out of sample, our solutions are on average within 7.5% of a high-quality local
search baseline, and even outperform it on 1.2% of instances. | [
"Puneet S. Bagga",
"Arthur Delarue"
] | 2023-10-02 19:55:15 | http://arxiv.org/abs/2310.01604v1 | http://arxiv.org/pdf/2310.01604v1 | 2310.01604v1 |
Pool-Based Active Learning with Proper Topological Regions | Machine learning methods usually rely on large sample size to have good
performance, while it is difficult to provide labeled set in many applications.
Pool-based active learning methods are there to detect, among a set of
unlabeled data, the ones that are the most relevant for the training. We
propose in this paper a meta-approach for pool-based active learning strategies
in the context of multi-class classification tasks based on Proper Topological
Regions. PTR, based on topological data analysis (TDA), are relevant regions
used to sample cold-start points or within the active learning scheme. The
proposed method is illustrated empirically on various benchmark datasets, being
competitive to the classical methods from the literature. | [
"Lies Hadjadj",
"Emilie Devijver",
"Remi Molinier",
"Massih-Reza Amini"
] | 2023-10-02 19:42:33 | http://arxiv.org/abs/2310.01597v1 | http://arxiv.org/pdf/2310.01597v1 | 2310.01597v1 |
Prescribed Fire Modeling using Knowledge-Guided Machine Learning for Land Management | In recent years, the increasing threat of devastating wildfires has
underscored the need for effective prescribed fire management. Process-based
computer simulations have traditionally been employed to plan prescribed fires
for wildfire prevention. However, even simplified process models like QUIC-Fire
are too compute-intensive to be used for real-time decision-making, especially
when weather conditions change rapidly. Traditional ML methods used for fire
modeling offer computational speedup but struggle with physically inconsistent
predictions, biased predictions due to class imbalance, biased estimates for
fire spread metrics (e.g., burned area, rate of spread), and generalizability
in out-of-distribution wind conditions. This paper introduces a novel machine
learning (ML) framework that enables rapid emulation of prescribed fires while
addressing these concerns. By incorporating domain knowledge, the proposed
method helps reduce physical inconsistencies in fuel density estimates in
data-scarce scenarios. To overcome the majority class bias in predictions, we
leverage pre-existing source domain data to augment training data and learn the
spread of fire more effectively. Finally, we overcome the problem of biased
estimation of fire spread metrics by incorporating a hierarchical modeling
structure to capture the interdependence in fuel density and burned area.
Notably, improvement in fire metric (e.g., burned area) estimates offered by
our framework makes it useful for fire managers, who often rely on these fire
metric estimates to make decisions about prescribed burn management.
Furthermore, our framework exhibits better generalization capabilities than the
other ML-based fire modeling methods across diverse wind conditions and
ignition patterns. | [
"Somya Sharma Chatterjee",
"Kelly Lindsay",
"Neel Chatterjee",
"Rohan Patil",
"Ilkay Altintas De Callafon",
"Michael Steinbach",
"Daniel Giron",
"Mai H. Nguyen",
"Vipin Kumar"
] | 2023-10-02 19:38:04 | http://arxiv.org/abs/2310.01593v1 | http://arxiv.org/pdf/2310.01593v1 | 2310.01593v1 |
An Investigation of Representation and Allocation Harms in Contrastive Learning | The effect of underrepresentation on the performance of minority groups is
known to be a serious problem in supervised learning settings; however, it has
been underexplored so far in the context of self-supervised learning (SSL). In
this paper, we demonstrate that contrastive learning (CL), a popular variant of
SSL, tends to collapse representations of minority groups with certain majority
groups. We refer to this phenomenon as representation harm and demonstrate it
on image and text datasets using the corresponding popular CL methods.
Furthermore, our causal mediation analysis of allocation harm on a downstream
classification task reveals that representation harm is partly responsible for
it, thus emphasizing the importance of studying and mitigating representation
harm. Finally, we provide a theoretical explanation for representation harm
using a stochastic block model that leads to a representational neural collapse
in a contrastive learning setting. | [
"Subha Maity",
"Mayank Agarwal",
"Mikhail Yurochkin",
"Yuekai Sun"
] | 2023-10-02 19:25:37 | http://arxiv.org/abs/2310.01583v1 | http://arxiv.org/pdf/2310.01583v1 | 2310.01583v1 |
On the Safety of Open-Sourced Large Language Models: Does Alignment Really Prevent Them From Being Misused? | Large Language Models (LLMs) have achieved unprecedented performance in
Natural Language Generation (NLG) tasks. However, many existing studies have
shown that they could be misused to generate undesired content. In response,
before releasing LLMs for public access, model developers usually align those
language models through Supervised Fine-Tuning (SFT) or Reinforcement Learning
with Human Feedback (RLHF). Consequently, those aligned large language models
refuse to generate undesired content when facing potentially harmful/unethical
requests. A natural question is "could alignment really prevent those
open-sourced large language models from being misused to generate undesired
content?''. In this work, we provide a negative answer to this question. In
particular, we show those open-sourced, aligned large language models could be
easily misguided to generate undesired content without heavy computations or
careful prompt designs. Our key idea is to directly manipulate the generation
process of open-sourced LLMs to misguide it to generate undesired content
including harmful or biased information and even private data. We evaluate our
method on 4 open-sourced LLMs accessible publicly and our finding highlights
the need for more advanced mitigation strategies for open-sourced LLMs. | [
"Hangfan Zhang",
"Zhimeng Guo",
"Huaisheng Zhu",
"Bochuan Cao",
"Lu Lin",
"Jinyuan Jia",
"Jinghui Chen",
"Dinghao Wu"
] | 2023-10-02 19:22:01 | http://arxiv.org/abs/2310.01581v1 | http://arxiv.org/pdf/2310.01581v1 | 2310.01581v1 |
Contraction Properties of the Global Workspace Primitive | To push forward the important emerging research field surrounding multi-area
recurrent neural networks (RNNs), we expand theoretically and empirically on
the provably stable RNNs of RNNs introduced by Kozachkov et al. in "RNNs of
RNNs: Recursive Construction of Stable Assemblies of Recurrent Neural
Networks". We prove relaxed stability conditions for salient special cases of
this architecture, most notably for a global workspace modular structure. We
then demonstrate empirical success for Global Workspace Sparse Combo Nets with
a small number of trainable parameters, not only through strong overall test
performance but also greater resilience to removal of individual subnetworks.
These empirical results for the global workspace inter-area topology are
contingent on stability preservation, highlighting the relevance of our
theoretical work for enabling modular RNN success. Further, by exploring
sparsity in the connectivity structure between different subnetwork modules
more broadly, we improve the state of the art performance for stable RNNs on
benchmark sequence processing tasks, thus underscoring the general utility of
specialized graph structures for multi-area RNNs. | [
"Michaela Ennis",
"Leo Kozachkov",
"Jean-Jacques Slotine"
] | 2023-10-02 19:04:41 | http://arxiv.org/abs/2310.01571v1 | http://arxiv.org/pdf/2310.01571v1 | 2310.01571v1 |
Iterative Option Discovery for Planning, by Planning | Discovering useful temporal abstractions, in the form of options, is widely
thought to be key to applying reinforcement learning and planning to
increasingly complex domains. Building on the empirical success of the Expert
Iteration approach to policy learning used in AlphaZero, we propose Option
Iteration, an analogous approach to option discovery. Rather than learning a
single strong policy that is trained to match the search results everywhere,
Option Iteration learns a set of option policies trained such that for each
state encountered, at least one policy in the set matches the search results
for some horizon into the future. Intuitively, this may be significantly easier
as it allows the algorithm to hedge its bets compared to learning a single
globally strong policy, which may have complex dependencies on the details of
the current state. Having learned such a set of locally strong policies, we can
use them to guide the search algorithm resulting in a virtuous cycle where
better options lead to better search results which allows for training of
better options. We demonstrate experimentally that planning using options
learned with Option Iteration leads to a significant benefit in challenging
planning environments compared to an analogous planning algorithm operating in
the space of primitive actions and learning a single rollout policy with Expert
Iteration. | [
"Kenny Young",
"Richard S. Sutton"
] | 2023-10-02 19:03:30 | http://arxiv.org/abs/2310.01569v1 | http://arxiv.org/pdf/2310.01569v1 | 2310.01569v1 |
Causality-informed Rapid Post-hurricane Building Damage Detection in Large Scale from InSAR Imagery | Timely and accurate assessment of hurricane-induced building damage is
crucial for effective post-hurricane response and recovery efforts. Recently,
remote sensing technologies provide large-scale optical or Interferometric
Synthetic Aperture Radar (InSAR) imagery data immediately after a disastrous
event, which can be readily used to conduct rapid building damage assessment.
Compared to optical satellite imageries, the Synthetic Aperture Radar can
penetrate cloud cover and provide more complete spatial coverage of damaged
zones in various weather conditions. However, these InSAR imageries often
contain highly noisy and mixed signals induced by co-occurring or co-located
building damage, flood, flood/wind-induced vegetation changes, as well as
anthropogenic activities, making it challenging to extract accurate building
damage information. In this paper, we introduced an approach for rapid
post-hurricane building damage detection from InSAR imagery. This approach
encoded complex causal dependencies among wind, flood, building damage, and
InSAR imagery using a holistic causal Bayesian network. Based on the causal
Bayesian network, we further jointly inferred the large-scale unobserved
building damage by fusing the information from InSAR imagery with prior
physical models of flood and wind, without the need for ground truth labels.
Furthermore, we validated our estimation results in a real-world devastating
hurricane -- the 2022 Hurricane Ian. We gathered and annotated building damage
ground truth data in Lee County, Florida, and compared the introduced method's
estimation results with the ground truth and benchmarked it against
state-of-the-art models to assess the effectiveness of our proposed method.
Results show that our method achieves rapid and accurate detection of building
damage, with significantly reduced processing time compared to traditional
manual inspection methods. | [
"Chenguang Wang",
"Yepeng Liu",
"Xiaojian Zhang",
"Xuechun Li",
"Vladimir Paramygin",
"Arthriya Subgranon",
"Peter Sheng",
"Xilei Zhao",
"Susu Xu"
] | 2023-10-02 18:56:05 | http://arxiv.org/abs/2310.01565v1 | http://arxiv.org/pdf/2310.01565v1 | 2310.01565v1 |
SmartPlay : A Benchmark for LLMs as Intelligent Agents | Recent large language models (LLMs) have demonstrated great potential toward
intelligent agents and next-gen automation, but there currently lacks a
systematic benchmark for evaluating LLMs' abilities as agents. We introduce
SmartPlay: both a challenging benchmark and a methodology for evaluating LLMs
as agents. SmartPlay consists of 6 different games, including
Rock-Paper-Scissors, Tower of Hanoi, Minecraft. Each game features a unique
setting, providing up to 20 evaluation settings and infinite environment
variations. Each game in SmartPlay uniquely challenges a subset of 9 important
capabilities of an intelligent LLM agent, including reasoning with object
dependencies, planning ahead, spatial reasoning, learning from history, and
understanding randomness. The distinction between the set of capabilities each
game test allows us to analyze each capability separately. SmartPlay serves not
only as a rigorous testing ground for evaluating the overall performance of LLM
agents but also as a road-map for identifying gaps in current methodologies. We
release our benchmark at github.com/microsoft/SmartPlay | [
"Yue Wu",
"Xuan Tang",
"Tom M. Mitchell",
"Yuanzhi Li"
] | 2023-10-02 18:52:11 | http://arxiv.org/abs/2310.01557v2 | http://arxiv.org/pdf/2310.01557v2 | 2310.01557v2 |
Harnessing the Power of Choices in Decision Tree Learning | We propose a simple generalization of standard and empirically successful
decision tree learning algorithms such as ID3, C4.5, and CART. These
algorithms, which have been central to machine learning for decades, are greedy
in nature: they grow a decision tree by iteratively splitting on the best
attribute. Our algorithm, Top-$k$, considers the $k$ best attributes as
possible splits instead of just the single best attribute. We demonstrate,
theoretically and empirically, the power of this simple generalization. We
first prove a {\sl greediness hierarchy theorem} showing that for every $k \in
\mathbb{N}$, Top-$(k+1)$ can be dramatically more powerful than Top-$k$: there
are data distributions for which the former achieves accuracy $1-\varepsilon$,
whereas the latter only achieves accuracy $\frac1{2}+\varepsilon$. We then
show, through extensive experiments, that Top-$k$ outperforms the two main
approaches to decision tree learning: classic greedy algorithms and more recent
"optimal decision tree" algorithms. On one hand, Top-$k$ consistently enjoys
significant accuracy gains over greedy algorithms across a wide range of
benchmarks. On the other hand, Top-$k$ is markedly more scalable than optimal
decision tree algorithms and is able to handle dataset and feature set sizes
that remain far beyond the reach of these algorithms. | [
"Guy Blanc",
"Jane Lange",
"Chirag Pabbaraju",
"Colin Sullivan",
"Li-Yang Tan",
"Mo Tiwari"
] | 2023-10-02 18:45:46 | http://arxiv.org/abs/2310.01551v1 | http://arxiv.org/pdf/2310.01551v1 | 2310.01551v1 |
On the near-optimality of betting confidence sets for bounded means | Constructing nonasymptotic confidence intervals (CIs) for the mean of a
univariate distribution from independent and identically distributed (i.i.d.)
observations is a fundamental task in statistics. For bounded observations, a
classical nonparametric approach proceeds by inverting standard concentration
bounds, such as Hoeffding's or Bernstein's inequalities. Recently, an
alternative betting-based approach for defining CIs and their time-uniform
variants called confidence sequences (CSs), has been shown to be empirically
superior to the classical methods. In this paper, we provide theoretical
justification for this improved empirical performance of betting CIs and CSs.
Our main contributions are as follows: (i) We first compare CIs using the
values of their first-order asymptotic widths (scaled by $\sqrt{n}$), and show
that the betting CI of Waudby-Smith and Ramdas (2023) has a smaller limiting
width than existing empirical Bernstein (EB)-CIs. (ii) Next, we establish two
lower bounds that characterize the minimum width achievable by any method for
constructing CIs/CSs in terms of certain inverse information projections. (iii)
Finally, we show that the betting CI and CS match the fundamental limits,
modulo an additive logarithmic term and a multiplicative constant. Overall
these results imply that the betting CI~(and CS) admit stronger theoretical
guarantees than the existing state-of-the-art EB-CI~(and CS); both in the
asymptotic and finite-sample regimes. | [
"Shubhanshu Shekhar",
"Aaditya Ramdas"
] | 2023-10-02 18:42:23 | http://arxiv.org/abs/2310.01547v1 | http://arxiv.org/pdf/2310.01547v1 | 2310.01547v1 |
Fusing Models with Complementary Expertise | Training AI models that generalize across tasks and domains has long been
among the open problems driving AI research. The emergence of Foundation Models
made it easier to obtain expert models for a given task, but the heterogeneity
of data that may be encountered at test time often means that any single expert
is insufficient. We consider the Fusion of Experts (FoE) problem of fusing
outputs of expert models with complementary knowledge of the data distribution
and formulate it as an instance of supervised learning. Our method is
applicable to both discriminative and generative tasks and leads to significant
performance improvements in image and text classification, text summarization,
multiple-choice QA, and automatic evaluation of generated text. We also extend
our method to the "frugal" setting where it is desired to reduce the number of
expert model evaluations at test time. | [
"Hongyi Wang",
"Felipe Maia Polo",
"Yuekai Sun",
"Souvik Kundu",
"Eric Xing",
"Mikhail Yurochkin"
] | 2023-10-02 18:31:35 | http://arxiv.org/abs/2310.01542v1 | http://arxiv.org/pdf/2310.01542v1 | 2310.01542v1 |
Adversarial Client Detection via Non-parametric Subspace Monitoring in the Internet of Federated Things | The Internet of Federated Things (IoFT) represents a network of
interconnected systems with federated learning as the backbone, facilitating
collaborative knowledge acquisition while ensuring data privacy for individual
systems. The wide adoption of IoFT, however, is hindered by security concerns,
particularly the susceptibility of federated learning networks to adversarial
attacks. In this paper, we propose an effective non-parametric approach FedRR,
which leverages the low-rank features of the transmitted parameter updates
generated by federated learning to address the adversarial attack problem.
Besides, our proposed method is capable of accurately detecting adversarial
clients and controlling the false alarm rate under the scenario with no attack
occurring. Experiments based on digit recognition using the MNIST datasets
validated the advantages of our approach. | [
"Xianjian Xie",
"Xiaochen Xian",
"Dan Li",
"Andi Wang"
] | 2023-10-02 18:25:02 | http://arxiv.org/abs/2310.01537v1 | http://arxiv.org/pdf/2310.01537v1 | 2310.01537v1 |
Nowcasting day-ahead marginal emissions using multi-headed CNNs and deep generative models | Nowcasting day-ahead marginal emissions factors is increasingly important for
power systems with high flexibility and penetration of distributed energy
resources. With a significant share of firm generation from natural gas and
coal power plants, forecasting day-ahead emissions in the current energy system
has been widely studied. In contrast, as we shift to an energy system
characterized by flexible power markets, dispatchable sources, and competing
low-cost generation such as large-scale battery or hydrogen storage, system
operators will be able to choose from a mix of different generation as well as
emission pathways. To fully develop the emissions implications of a given
dispatch schedule, we need a near real-time workflow with two layers. The first
layer is a market model that continuously solves a security-constrained
economic dispatch model. The second layer determines the marginal emissions
based on the output of the market model, which is the subject of this paper. We
propose using multi-headed convolutional neural networks to generate day-ahead
forecasts of marginal and average emissions for a given independent system
operator. | [
"Dhruv Suri",
"Anela Arifi",
"Ines Azevedo"
] | 2023-10-02 18:14:55 | http://arxiv.org/abs/2310.01524v1 | http://arxiv.org/pdf/2310.01524v1 | 2310.01524v1 |
The Benefit of Noise-Injection for Dynamic Gray-Box Model Creation | Gray-box models offer significant benefit over black-box approaches for
equipment emulator development for equipment since their integration of physics
provides more confidence in the model outside of the training domain. However,
challenges such as model nonlinearity, unmodeled dynamics, and local minima
introduce uncertainties into grey-box creation that contemporary approaches
have failed to overcome, leading to their under-performance compared with
black-box models. This paper seeks to address these uncertainties by injecting
noise into the training dataset. This noise injection enriches the dataset and
provides a measure of robustness against such uncertainties. A dynamic model
for a water-to-water heat exchanger has been used as a demonstration case for
this approach and tested using a pair of real devices with live data streaming.
Compared to the unprocessed signal data, the application of noise injection
resulted in a significant reduction in modeling error (root mean square error),
decreasing from 0.68 to 0.27{\deg}C. This improvement amounts to a 60%
enhancement when assessed on the training set, and improvements of 50% and 45%
when validated against the test and validation sets, respectively. | [
"Mohamed Kandil",
"J. J. McArthur"
] | 2023-10-02 18:10:21 | http://arxiv.org/abs/2310.01517v1 | http://arxiv.org/pdf/2310.01517v1 | 2310.01517v1 |
Tensor Ring Optimized Quantum-Enhanced Tensor Neural Networks | Quantum machine learning researchers often rely on incorporating Tensor
Networks (TN) into Deep Neural Networks (DNN) and variational optimization.
However, the standard optimization techniques used for training the contracted
trainable weights of each model layer suffer from the correlations and
entanglement structure between the model parameters on classical
implementations. To address this issue, a multi-layer design of a Tensor Ring
optimized variational Quantum learning classifier (Quan-TR) comprising
cascading entangling gates replacing the fully connected (dense) layers of a TN
is proposed, and it is referred to as Tensor Ring optimized Quantum-enhanced
tensor neural Networks (TR-QNet). TR-QNet parameters are optimized through the
stochastic gradient descent algorithm on qubit measurements. The proposed
TR-QNet is assessed on three distinct datasets, namely Iris, MNIST, and
CIFAR-10, to demonstrate the enhanced precision achieved for binary
classification. On quantum simulations, the proposed TR-QNet achieves promising
accuracy of $94.5\%$, $86.16\%$, and $83.54\%$ on the Iris, MNIST, and CIFAR-10
datasets, respectively. Benchmark studies have been conducted on
state-of-the-art quantum and classical implementations of TN models to show the
efficacy of the proposed TR-QNet. Moreover, the scalability of TR-QNet
highlights its potential for exhibiting in deep learning applications on a
large scale. The PyTorch implementation of TR-QNet is available on
Github:https://github.com/konar1987/TR-QNet/ | [
"Debanjan Konar",
"Dheeraj Peddireddy",
"Vaneet Aggarwal",
"Bijaya K. Panigrahi"
] | 2023-10-02 18:07:10 | http://arxiv.org/abs/2310.01515v1 | http://arxiv.org/pdf/2310.01515v1 | 2310.01515v1 |
CODA: Temporal Domain Generalization via Concept Drift Simulator | In real-world applications, machine learning models often become obsolete due
to shifts in the joint distribution arising from underlying temporal trends, a
phenomenon known as the "concept drift". Existing works propose model-specific
strategies to achieve temporal generalization in the near-future domain.
However, the diverse characteristics of real-world datasets necessitate
customized prediction model architectures. To this end, there is an urgent
demand for a model-agnostic temporal domain generalization approach that
maintains generality across diverse data modalities and architectures. In this
work, we aim to address the concept drift problem from a data-centric
perspective to bypass considering the interaction between data and model.
Developing such a framework presents non-trivial challenges: (i) existing
generative models struggle to generate out-of-distribution future data, and
(ii) precisely capturing the temporal trends of joint distribution along
chronological source domains is computationally infeasible. To tackle the
challenges, we propose the COncept Drift simulAtor (CODA) framework
incorporating a predicted feature correlation matrix to simulate future data
for model training. Specifically, CODA leverages feature correlations to
represent data characteristics at specific time points, thereby circumventing
the daunting computational costs. Experimental results demonstrate that using
CODA-generated data as training input effectively achieves temporal domain
generalization across different model architectures. | [
"Chia-Yuan Chang",
"Yu-Neng Chuang",
"Zhimeng Jiang",
"Kwei-Herng Lai",
"Anxiao Jiang",
"Na Zou"
] | 2023-10-02 18:04:34 | http://arxiv.org/abs/2310.01508v1 | http://arxiv.org/pdf/2310.01508v1 | 2310.01508v1 |
Generalized Animal Imitator: Agile Locomotion with Versatile Motion Prior | The agility of animals, particularly in complex activities such as running,
turning, jumping, and backflipping, stands as an exemplar for robotic system
design. Transferring this suite of behaviors to legged robotic systems
introduces essential inquiries: How can a robot be trained to learn multiple
locomotion behaviors simultaneously? How can the robot execute these tasks with
a smooth transition? And what strategies allow for the integrated application
of these skills? This paper introduces the Versatile Instructable Motion prior
(VIM) - a Reinforcement Learning framework designed to incorporate a range of
agile locomotion tasks suitable for advanced robotic applications. Our
framework enables legged robots to learn diverse agile low-level skills by
imitating animal motions and manually designed motions with Functionality
reward and Stylization reward. While the Functionality reward guides the
robot's ability to adopt varied skills, the Stylization reward ensures
performance alignment with reference motions. Our evaluations of the VIM
framework span both simulation environments and real-world deployment. To our
understanding, this is the first work that allows a robot to concurrently learn
diverse agile locomotion tasks using a singular controller. Further details and
supportive media can be found at our project site:
https://rchalyang.github.io/VIM . | [
"Ruihan Yang",
"Zhuoqun Chen",
"Jianhan Ma",
"Chongyi Zheng",
"Yiyu Chen",
"Quan Nguyen",
"Xiaolong Wang"
] | 2023-10-02 17:59:24 | http://arxiv.org/abs/2310.01408v1 | http://arxiv.org/pdf/2310.01408v1 | 2310.01408v1 |
Conditional Diffusion Distillation | Generative diffusion models provide strong priors for text-to-image
generation and thereby serve as a foundation for conditional generation tasks
such as image editing, restoration, and super-resolution. However, one major
limitation of diffusion models is their slow sampling time. To address this
challenge, we present a novel conditional distillation method designed to
supplement the diffusion priors with the help of image conditions, allowing for
conditional sampling with very few steps. We directly distill the unconditional
pre-training in a single stage through joint-learning, largely simplifying the
previous two-stage procedures that involve both distillation and conditional
finetuning separately. Furthermore, our method enables a new
parameter-efficient distillation mechanism that distills each task with only a
small number of additional parameters combined with the shared frozen
unconditional backbone. Experiments across multiple tasks including
super-resolution, image editing, and depth-to-image generation demonstrate that
our method outperforms existing distillation techniques for the same sampling
time. Notably, our method is the first distillation strategy that can match the
performance of the much slower fine-tuned conditional diffusion models. | [
"Kangfu Mei",
"Mauricio Delbracio",
"Hossein Talebi",
"Zhengzhong Tu",
"Vishal M. Patel",
"Peyman Milanfar"
] | 2023-10-02 17:59:18 | http://arxiv.org/abs/2310.01407v1 | http://arxiv.org/pdf/2310.01407v1 | 2310.01407v1 |
Representation Engineering: A Top-Down Approach to AI Transparency | In this paper, we identify and characterize the emerging area of
representation engineering (RepE), an approach to enhancing the transparency of
AI systems that draws on insights from cognitive neuroscience. RepE places
population-level representations, rather than neurons or circuits, at the
center of analysis, equipping us with novel methods for monitoring and
manipulating high-level cognitive phenomena in deep neural networks (DNNs). We
provide baselines and an initial analysis of RepE techniques, showing that they
offer simple yet effective solutions for improving our understanding and
control of large language models. We showcase how these methods can provide
traction on a wide range of safety-relevant problems, including honesty,
harmlessness, power-seeking, and more, demonstrating the promise of top-down
transparency research. We hope that this work catalyzes further exploration of
RepE and fosters advancements in the transparency and safety of AI systems. | [
"Andy Zou",
"Long Phan",
"Sarah Chen",
"James Campbell",
"Phillip Guo",
"Richard Ren",
"Alexander Pan",
"Xuwang Yin",
"Mantas Mazeika",
"Ann-Kathrin Dombrowski",
"Shashwat Goel",
"Nathaniel Li",
"Michael J. Byun",
"Zifan Wang",
"Alex Mallen",
"Steven Basart",
"Sanmi Koyejo",
"Dawn Song",
"Matt Fredrikson",
"J. Zico Kolter",
"Dan Hendrycks"
] | 2023-10-02 17:59:07 | http://arxiv.org/abs/2310.01405v3 | http://arxiv.org/pdf/2310.01405v3 | 2310.01405v3 |
H-InDex: Visual Reinforcement Learning with Hand-Informed Representations for Dexterous Manipulation | Human hands possess remarkable dexterity and have long served as a source of
inspiration for robotic manipulation. In this work, we propose a human
$\textbf{H}$and$\textbf{-In}$formed visual representation learning framework to
solve difficult $\textbf{Dex}$terous manipulation tasks ($\textbf{H-InDex}$)
with reinforcement learning. Our framework consists of three stages: (i)
pre-training representations with 3D human hand pose estimation, (ii) offline
adapting representations with self-supervised keypoint detection, and (iii)
reinforcement learning with exponential moving average BatchNorm. The last two
stages only modify $0.36\%$ parameters of the pre-trained representation in
total, ensuring the knowledge from pre-training is maintained to the full
extent. We empirically study 12 challenging dexterous manipulation tasks and
find that H-InDex largely surpasses strong baseline methods and the recent
visual foundation models for motor control. Code is available at
https://yanjieze.com/H-InDex . | [
"Yanjie Ze",
"Yuyao Liu",
"Ruizhe Shi",
"Jiaxin Qin",
"Zhecheng Yuan",
"Jiashun Wang",
"Huazhe Xu"
] | 2023-10-02 17:59:03 | http://arxiv.org/abs/2310.01404v2 | http://arxiv.org/pdf/2310.01404v2 | 2310.01404v2 |
Sequential Data Generation with Groupwise Diffusion Process | We present the Groupwise Diffusion Model (GDM), which divides data into
multiple groups and diffuses one group at one time interval in the forward
diffusion process. GDM generates data sequentially from one group at one time
interval, leading to several interesting properties. First, as an extension of
diffusion models, GDM generalizes certain forms of autoregressive models and
cascaded diffusion models. As a unified framework, GDM allows us to investigate
design choices that have been overlooked in previous works, such as
data-grouping strategy and order of generation. Furthermore, since one group of
the initial noise affects only a certain group of the generated data, latent
space now possesses group-wise interpretable meaning. We can further extend GDM
to the frequency domain where the forward process sequentially diffuses each
group of frequency components. Dividing the frequency bands of the data as
groups allows the latent variables to become a hierarchical representation
where individual groups encode data at different levels of abstraction. We
demonstrate several applications of such representation including
disentanglement of semantic attributes, image editing, and generating
variations. | [
"Sangyun Lee",
"Gayoung Lee",
"Hyunsu Kim",
"Junho Kim",
"Youngjung Uh"
] | 2023-10-02 17:58:47 | http://arxiv.org/abs/2310.01400v1 | http://arxiv.org/pdf/2310.01400v1 | 2310.01400v1 |
A Learning Based Scheme for Fair Timeliness in Sparse Gossip Networks | We consider a gossip network, consisting of $n$ nodes, which tracks the
information at a source. The source updates its information with a Poisson
arrival process and also sends updates to the nodes in the network. The nodes
themselves can exchange information among themselves to become as timely as
possible. However, the network structure is sparse and irregular, i.e., not
every node is connected to every other node in the network, rather, the order
of connectivity is low, and varies across different nodes. This asymmetry of
the network implies that the nodes in the network do not perform equally in
terms of timelines. Due to the gossiping nature of the network, some nodes are
able to track the source very timely, whereas, some nodes fall behind versions
quite often. In this work, we investigate how the rate-constrained source
should distribute its update rate across the network to maintain fairness
regarding timeliness, i.e., the overall worst case performance of the network
can be minimized. Due to the continuous search space for optimum rate
allocation, we formulate this problem as a continuum-armed bandit problem and
employ Gaussian process based Bayesian optimization to meet a trade-off between
exploration and exploitation sequentially. | [
"Purbesh Mitra",
"Sennur Ulukus"
] | 2023-10-02 17:55:17 | http://arxiv.org/abs/2310.01396v1 | http://arxiv.org/pdf/2310.01396v1 | 2310.01396v1 |
Compressing LLMs: The Truth is Rarely Pure and Never Simple | Despite their remarkable achievements, modern Large Language Models (LLMs)
encounter exorbitant computational and memory footprints. Recently, several
works have shown significant success in training-free and data-free compression
(pruning and quantization) of LLMs achieving 50-60% sparsity and reducing the
bit-width down to 3 or 4 bits per weight, with negligible perplexity
degradation over the uncompressed baseline. As recent research efforts are
focused on developing increasingly sophisticated compression methods, our work
takes a step back, and re-evaluates the effectiveness of existing SoTA
compression methods, which rely on a fairly simple and widely questioned
metric, perplexity (even for dense LLMs). We introduce Knowledge-Intensive
Compressed LLM BenchmarK (LLM-KICK), a collection of carefully-curated tasks to
re-define the evaluation protocol for compressed LLMs, which have significant
alignment with their dense counterparts, and perplexity fail to capture subtle
change in their true capabilities. LLM-KICK unveils many favorable merits and
unfortunate plights of current SoTA compression methods: all pruning methods
suffer significant performance degradation, sometimes at trivial sparsity
ratios (e.g., 25-30%), and fail for N:M sparsity on knowledge-intensive tasks;
current quantization methods are more successful than pruning; yet, pruned LLMs
even at $\geq 50$% sparsity are robust in-context retrieval and summarization
systems; among others. LLM-KICK is designed to holistically access compressed
LLMs' ability for language understanding, reasoning, generation, in-context
retrieval, in-context summarization, etc. We hope our study can foster the
development of better LLM compression methods. All our related codes are planed
to be open-sourced. | [
"Ajay Jaiswal",
"Zhe Gan",
"Xianzhi Du",
"Bowen Zhang",
"Zhangyang Wang",
"Yinfei Yang"
] | 2023-10-02 17:42:37 | http://arxiv.org/abs/2310.01382v1 | http://arxiv.org/pdf/2310.01382v1 | 2310.01382v1 |
Pessimistic Nonlinear Least-Squares Value Iteration for Offline Reinforcement Learning | Offline reinforcement learning (RL), where the agent aims to learn the
optimal policy based on the data collected by a behavior policy, has attracted
increasing attention in recent years. While offline RL with linear function
approximation has been extensively studied with optimal results achieved under
certain assumptions, many works shift their interest to offline RL with
non-linear function approximation. However, limited works on offline RL with
non-linear function approximation have instance-dependent regret guarantees. In
this paper, we propose an oracle-efficient algorithm, dubbed Pessimistic
Nonlinear Least-Square Value Iteration (PNLSVI), for offline RL with non-linear
function approximation. Our algorithmic design comprises three innovative
components: (1) a variance-based weighted regression scheme that can be applied
to a wide range of function classes, (2) a subroutine for variance estimation,
and (3) a planning phase that utilizes a pessimistic value iteration approach.
Our algorithm enjoys a regret bound that has a tight dependency on the function
class complexity and achieves minimax optimal instance-dependent regret when
specialized to linear function approximation. Our work extends the previous
instance-dependent results within simpler function classes, such as linear and
differentiable function to a more general framework. | [
"Qiwei Di",
"Heyang Zhao",
"Jiafan He",
"Quanquan Gu"
] | 2023-10-02 17:42:01 | http://arxiv.org/abs/2310.01380v1 | http://arxiv.org/pdf/2310.01380v1 | 2310.01380v1 |
UltraFeedback: Boosting Language Models with High-quality Feedback | Reinforcement learning from human feedback (RLHF) has become a pivot
technique in aligning large language models (LLMs) with human preferences. In
RLHF practice, preference data plays a crucial role in bridging human
proclivity and LLMs. However, the scarcity of diverse, naturalistic datasets of
human preferences on LLM outputs at scale poses a great challenge to RLHF as
well as feedback learning research within the open-source community. Current
preference datasets, either proprietary or limited in size and prompt variety,
result in limited RLHF adoption in open-source models and hinder further
exploration. In this study, we propose ULTRAFEEDBACK, a large-scale,
high-quality, and diversified preference dataset designed to overcome these
limitations and foster RLHF development. To create ULTRAFEEDBACK, we compile a
diverse array of instructions and models from multiple sources to produce
comparative data. We meticulously devise annotation instructions and employ
GPT-4 to offer detailed feedback in both numerical and textual forms.
ULTRAFEEDBACK establishes a reproducible and expandable preference data
construction pipeline, serving as a solid foundation for future RLHF and
feedback learning research. Utilizing ULTRAFEEDBACK, we train various models to
demonstrate its effectiveness, including the reward model UltraRM, chat
language model UltraLM-13B-PPO, and critique model UltraCM. Experimental
results indicate that our models outperform existing open-source models,
achieving top performance across multiple benchmarks. Our data and models are
available at https://github.com/thunlp/UltraFeedback. | [
"Ganqu Cui",
"Lifan Yuan",
"Ning Ding",
"Guanming Yao",
"Wei Zhu",
"Yuan Ni",
"Guotong Xie",
"Zhiyuan Liu",
"Maosong Sun"
] | 2023-10-02 17:40:01 | http://arxiv.org/abs/2310.01377v1 | http://arxiv.org/pdf/2310.01377v1 | 2310.01377v1 |
Window-based Model Averaging Improves Generalization in Heterogeneous Federated Learning | Federated Learning (FL) aims to learn a global model from distributed users
while protecting their privacy. However, when data are distributed
heterogeneously the learning process becomes noisy, unstable, and biased
towards the last seen clients' data, slowing down convergence. To address these
issues and improve the robustness and generalization capabilities of the global
model, we propose WIMA (Window-based Model Averaging). WIMA aggregates global
models from different rounds using a window-based approach, effectively
capturing knowledge from multiple users and reducing the bias from the last
ones. By adopting a windowed view on the rounds, WIMA can be applied from the
initial stages of training. Importantly, our method introduces no additional
communication or client-side computation overhead. Our experiments demonstrate
the robustness of WIMA against distribution shifts and bad client sampling,
resulting in smoother and more stable learning trends. Additionally, WIMA can
be easily integrated with state-of-the-art algorithms. We extensively evaluate
our approach on standard FL benchmarks, demonstrating its effectiveness. | [
"Debora Caldarola",
"Barbara Caputo",
"Marco Ciccone"
] | 2023-10-02 17:30:14 | http://arxiv.org/abs/2310.01366v2 | http://arxiv.org/pdf/2310.01366v2 | 2310.01366v2 |
Elephant Neural Networks: Born to Be a Continual Learner | Catastrophic forgetting remains a significant challenge to continual learning
for decades. While recent works have proposed effective methods to mitigate
this problem, they mainly focus on the algorithmic side. Meanwhile, we do not
fully understand what architectural properties of neural networks lead to
catastrophic forgetting. This study aims to fill this gap by studying the role
of activation functions in the training dynamics of neural networks and their
impact on catastrophic forgetting. Our study reveals that, besides sparse
representations, the gradient sparsity of activation functions also plays an
important role in reducing forgetting. Based on this insight, we propose a new
class of activation functions, elephant activation functions, that can generate
both sparse representations and sparse gradients. We show that by simply
replacing classical activation functions with elephant activation functions, we
can significantly improve the resilience of neural networks to catastrophic
forgetting. Our method has broad applicability and benefits for continual
learning in regression, class incremental learning, and reinforcement learning
tasks. Specifically, we achieves excellent performance on Split MNIST dataset
in just one single pass, without using replay buffer, task boundary
information, or pre-training. | [
"Qingfeng Lan",
"A. Rupam Mahmood"
] | 2023-10-02 17:27:39 | http://arxiv.org/abs/2310.01365v1 | http://arxiv.org/pdf/2310.01365v1 | 2310.01365v1 |
Fleet Policy Learning via Weight Merging and An Application to Robotic Tool-Use | Fleets of robots ingest massive amounts of streaming data generated by
interacting with their environments, far more than those that can be stored or
transmitted with ease. At the same time, we hope that teams of robots can
co-acquire diverse skills through their experiences in varied settings. How can
we enable such fleet-level learning without having to transmit or centralize
fleet-scale data? In this paper, we investigate distributed learning of
policies as a potential solution. To efficiently merge policies in the
distributed setting, we propose fleet-merge, an instantiation of distributed
learning that accounts for the symmetries that can arise in learning policies
that are parameterized by recurrent neural networks. We show that fleet-merge
consolidates the behavior of policies trained on 50 tasks in the Meta-World
environment, with the merged policy achieving good performance on nearly all
training tasks at test time. Moreover, we introduce a novel robotic tool-use
benchmark, fleet-tools, for fleet policy learning in compositional and
contact-rich robot manipulation tasks, which might be of broader interest, and
validate the efficacy of fleet-merge on the benchmark. | [
"Lirui Wang",
"Kaiqing Zhang",
"Allan Zhou",
"Max Simchowitz",
"Russ Tedrake"
] | 2023-10-02 17:23:51 | http://arxiv.org/abs/2310.01362v1 | http://arxiv.org/pdf/2310.01362v1 | 2310.01362v1 |
GenSim: Generating Robotic Simulation Tasks via Large Language Models | Collecting large amounts of real-world interaction data to train general
robotic policies is often prohibitively expensive, thus motivating the use of
simulation data. However, existing methods for data generation have generally
focused on scene-level diversity (e.g., object instances and poses) rather than
task-level diversity, due to the human effort required to come up with and
verify novel tasks. This has made it challenging for policies trained on
simulation data to demonstrate significant task-level generalization. In this
paper, we propose to automatically generate rich simulation environments and
expert demonstrations by exploiting a large language models' (LLM) grounding
and coding ability. Our approach, dubbed GenSim, has two modes: goal-directed
generation, wherein a target task is given to the LLM and the LLM proposes a
task curriculum to solve the target task, and exploratory generation, wherein
the LLM bootstraps from previous tasks and iteratively proposes novel tasks
that would be helpful in solving more complex tasks. We use GPT4 to expand the
existing benchmark by ten times to over 100 tasks, on which we conduct
supervised finetuning and evaluate several LLMs including finetuned GPTs and
Code Llama on code generation for robotic simulation tasks. Furthermore, we
observe that LLMs-generated simulation programs can enhance task-level
generalization significantly when used for multitask policy training. We
further find that with minimal sim-to-real adaptation, the multitask policies
pretrained on GPT4-generated simulation tasks exhibit stronger transfer to
unseen long-horizon tasks in the real world and outperform baselines by 25%.
See the project website (https://liruiw.github.io/gensim) for code, demos, and
videos. | [
"Lirui Wang",
"Yiyang Ling",
"Zhecheng Yuan",
"Mohit Shridhar",
"Chen Bao",
"Yuzhe Qin",
"Bailin Wang",
"Huazhe Xu",
"Xiaolong Wang"
] | 2023-10-02 17:23:48 | http://arxiv.org/abs/2310.01361v1 | http://arxiv.org/pdf/2310.01361v1 | 2310.01361v1 |
A peridynamic-informed deep learning model for brittle damage prediction | In this study, a novel approach that combines the principles of peridynamic
(PD) theory with PINN is presented to predict quasi-static damage and crack
propagation in brittle materials. To achieve high prediction accuracy and
convergence rate, the linearized PD governing equation is enforced in the
PINN's residual-based loss function. The proposed PD-INN is able to learn and
capture intricate displacement patterns associated with different geometrical
parameters, such as pre-crack position and length. Several enhancements like
cyclical annealing schedule and deformation gradient aware optimization
technique are proposed to ensure the model would not get stuck in its trivial
solution. The model's performance assessment is conducted by monitoring the
behavior of loss function throughout the training process. The PD-INN
predictions are also validated through several benchmark cases with the results
obtained from high-fidelity techniques such as PD direct numerical method and
Extended-Finite Element Method. Our results show the ability of the nonlocal
PD-INN to predict damage and crack propagation accurately and efficiently. | [
"Roozbeh Eghbalpoor",
"Azadeh Sheidaei"
] | 2023-10-02 17:12:20 | http://arxiv.org/abs/2310.01350v1 | http://arxiv.org/pdf/2310.01350v1 | 2310.01350v1 |
L2MAC: Large Language Model Automatic Computer for Unbounded Code Generation | Transformer-based large language models (LLMs) are constrained by the fixed
context window of the underlying transformer architecture, hindering their
ability to produce long and logically consistent code. Memory-augmented LLMs
are a promising solution, but current approaches cannot handle long code
generation tasks since they (1) only focus on reading memory and reduce its
evolution to the concatenation of new memories or (2) use very specialized
memories that cannot adapt to other domains. This paper presents L2MAC, the
first practical LLM-based stored-program automatic computer for long and
consistent code generation. Its memory has two components: the instruction
registry, which is populated with a prompt program to solve the user-given
task, and a file store, which will contain the final and intermediate outputs.
Each instruction is executed by a separate LLM instance, whose context is
managed by a control unit capable of precise memory reading and writing to
ensure effective interaction with the file store. These components enable L2MAC
to generate virtually unbounded code structures, bypassing the constraints of
the finite context window while producing code that fulfills complex
user-specified requirements. We empirically show that L2MAC succeeds in
generating large code bases for system design tasks where other coding methods
fall short in implementing user requirements and provide insight into the
reasons for this performance gap. | [
"Samuel Holt",
"Max Ruiz Luyten",
"Mihaela van der Schaar"
] | 2023-10-02 16:55:19 | http://arxiv.org/abs/2310.02003v1 | http://arxiv.org/pdf/2310.02003v1 | 2310.02003v1 |
Merge, Then Compress: Demystify Efficient SMoE with Hints from Its Routing Policy | Sparsely activated Mixture-of-Experts (SMoE) has shown promise to scale up
the learning capacity of neural networks, however, they have issues like (a)
High Memory Usage, due to duplication of the network layers into multiple
copies as experts; and (b) Redundancy in Experts, as common learning-based
routing policies suffer from representational collapse. Therefore, vanilla SMoE
models are memory inefficient and non-scalable, especially for
resource-constrained downstream scenarios. In this paper, we ask: Can we craft
a compact SMoE model by consolidating expert information? What is the best
recipe to merge multiple experts into fewer but more knowledgeable experts? Our
pilot investigation reveals that conventional model merging methods fail to be
effective in such expert merging for SMoE. The potential reasons are: (1)
redundant information overshadows critical experts; (2) appropriate neuron
permutation for each expert is missing to bring all of them in alignment. To
address this, we propose M-SMoE, which leverages routing statistics to guide
expert merging. Specifically, it starts with neuron permutation alignment for
experts; then, dominant experts and their "group members" are formed; lastly,
every expert group is merged into a single expert by utilizing each expert's
activation frequency as their weight for merging, thus diminishing the impact
of insignificant experts. Moreover, we observed that our proposed merging
promotes a low dimensionality in the merged expert's weight space, naturally
paving the way for additional compression. Hence, our final method, MC-SMoE
(i.e., Merge, then Compress SMoE), further decomposes the merged experts into
low-rank and structural sparse alternatives. Extensive experiments across 8
benchmarks validate the effectiveness of MC-SMoE. For instance, our MC-SMoE
achieves up to 80% memory and a 20% FLOPs reduction, with virtually no loss in
performance. | [
"Pingzhi Li",
"Zhenyu Zhang",
"Prateek Yadav",
"Yi-Lin Sung",
"Yu Cheng",
"Mohit Bansal",
"Tianlong Chen"
] | 2023-10-02 16:51:32 | http://arxiv.org/abs/2310.01334v1 | http://arxiv.org/pdf/2310.01334v1 | 2310.01334v1 |
The Optimal use of Segmentation for Sampling Calorimeters | One of the key design choices of any sampling calorimeter is how fine to make
the longitudinal and transverse segmentation. To inform this choice, we study
the impact of calorimeter segmentation on energy reconstruction. To ensure that
the trends are due entirely to hardware and not to a sub-optimal use of
segmentation, we deploy deep neural networks to perform the reconstruction.
These networks make use of all available information by representing the
calorimeter as a point cloud. To demonstrate our approach, we simulate a
detector similar to the forward calorimeter system intended for use in the ePIC
detector, which will operate at the upcoming Electron Ion Collider. We find
that for the energy estimation of isolated charged pion showers, relatively
fine longitudinal segmentation is key to achieving an energy resolution that is
better than 10% across the full phase space. These results provide a valuable
benchmark for ongoing EIC detector optimizations and may also inform future
studies involving high-granularity calorimeters in other experiments at various
facilities. | [
"Fernando Torales Acosta",
"Bishnu Karki",
"Piyush Karande",
"Aaron Angerami",
"Miguel Arratia",
"Kenneth Barish",
"Ryan Milton",
"Sebastián Morán",
"Benjamin Nachman",
"Anshuman Sinha"
] | 2023-10-02 16:46:22 | http://arxiv.org/abs/2310.04442v1 | http://arxiv.org/pdf/2310.04442v1 | 2310.04442v1 |
TACTiS-2: Better, Faster, Simpler Attentional Copulas for Multivariate Time Series | We introduce a new model for multivariate probabilistic time series
prediction, designed to flexibly address a range of tasks including
forecasting, interpolation, and their combinations. Building on copula theory,
we propose a simplified objective for the recently-introduced transformer-based
attentional copulas (TACTiS), wherein the number of distributional parameters
now scales linearly with the number of variables instead of factorially. The
new objective requires the introduction of a training curriculum, which goes
hand-in-hand with necessary changes to the original architecture. We show that
the resulting model has significantly better training dynamics and achieves
state-of-the-art performance across diverse real-world forecasting tasks, while
maintaining the flexibility of prior work, such as seamless handling of
unaligned and unevenly-sampled time series. | [
"Arjun Ashok",
"Étienne Marcotte",
"Valentina Zantedeschi",
"Nicolas Chapados",
"Alexandre Drouin"
] | 2023-10-02 16:45:19 | http://arxiv.org/abs/2310.01327v1 | http://arxiv.org/pdf/2310.01327v1 | 2310.01327v1 |
Optimal Estimator for Linear Regression with Shuffled Labels | This paper considers the task of linear regression with shuffled labels,
i.e., $\mathbf Y = \mathbf \Pi \mathbf X \mathbf B + \mathbf W$, where $\mathbf
Y \in \mathbb R^{n\times m}, \mathbf Pi \in \mathbb R^{n\times n}, \mathbf X\in
\mathbb R^{n\times p}, \mathbf B \in \mathbb R^{p\times m}$, and $\mathbf W\in
\mathbb R^{n\times m}$, respectively, represent the sensing results, (unknown
or missing) corresponding information, sensing matrix, signal of interest, and
additive sensing noise. Given the observation $\mathbf Y$ and sensing matrix
$\mathbf X$, we propose a one-step estimator to reconstruct $(\mathbf \Pi,
\mathbf B)$. From the computational perspective, our estimator's complexity is
$O(n^3 + np^2m)$, which is no greater than the maximum complexity of a linear
assignment algorithm (e.g., $O(n^3)$) and a least square algorithm (e.g.,
$O(np^2 m)$). From the statistical perspective, we divide the minimum $snr$
requirement into four regimes, e.g., unknown, hard, medium, and easy regimes;
and present sufficient conditions for the correct permutation recovery under
each regime: $(i)$ $snr \geq \Omega(1)$ in the easy regime; $(ii)$ $snr \geq
\Omega(\log n)$ in the medium regime; and $(iii)$ $snr \geq \Omega((\log
n)^{c_0}\cdot n^{{c_1}/{srank(\mathbf B)}})$ in the hard regime ($c_0, c_1$ are
some positive constants and $srank(\mathbf B)$ denotes the stable rank of
$\mathbf B$). In the end, we also provide numerical experiments to confirm the
above claims. | [
"Hang Zhang",
"Ping Li"
] | 2023-10-02 16:44:47 | http://arxiv.org/abs/2310.01326v1 | http://arxiv.org/pdf/2310.01326v1 | 2310.01326v1 |
Avalon's Game of Thoughts: Battle Against Deception through Recursive Contemplation | Recent breakthroughs in large language models (LLMs) have brought remarkable
success in the field of LLM-as-Agent. Nevertheless, a prevalent assumption is
that the information processed by LLMs is consistently honest, neglecting the
pervasive deceptive or misleading information in human society and AI-generated
content. This oversight makes LLMs susceptible to malicious manipulations,
potentially resulting in detrimental outcomes. This study utilizes the
intricate Avalon game as a testbed to explore LLMs' potential in deceptive
environments. Avalon, full of misinformation and requiring sophisticated logic,
manifests as a "Game-of-Thoughts". Inspired by the efficacy of humans'
recursive thinking and perspective-taking in the Avalon game, we introduce a
novel framework, Recursive Contemplation (ReCon), to enhance LLMs' ability to
identify and counteract deceptive information. ReCon combines formulation and
refinement contemplation processes; formulation contemplation produces initial
thoughts and speech, while refinement contemplation further polishes them.
Additionally, we incorporate first-order and second-order perspective
transitions into these processes respectively. Specifically, the first-order
allows an LLM agent to infer others' mental states, and the second-order
involves understanding how others perceive the agent's mental state. After
integrating ReCon with different LLMs, extensive experiment results from the
Avalon game indicate its efficacy in aiding LLMs to discern and maneuver around
deceptive information without extra fine-tuning and data. Finally, we offer a
possible explanation for the efficacy of ReCon and explore the current
limitations of LLMs in terms of safety, reasoning, speaking style, and format,
potentially furnishing insights for subsequent research. | [
"Shenzhi Wang",
"Chang Liu",
"Zilong Zheng",
"Siyuan Qi",
"Shuo Chen",
"Qisen Yang",
"Andrew Zhao",
"Chaofei Wang",
"Shiji Song",
"Gao Huang"
] | 2023-10-02 16:27:36 | http://arxiv.org/abs/2310.01320v2 | http://arxiv.org/pdf/2310.01320v2 | 2310.01320v2 |
On the Generalization of Training-based ChatGPT Detection Methods | ChatGPT is one of the most popular language models which achieve amazing
performance on various natural language tasks. Consequently, there is also an
urgent need to detect the texts generated ChatGPT from human written. One of
the extensively studied methods trains classification models to distinguish
both. However, existing studies also demonstrate that the trained models may
suffer from distribution shifts (during test), i.e., they are ineffective to
predict the generated texts from unseen language tasks or topics. In this work,
we aim to have a comprehensive investigation on these methods' generalization
behaviors under distribution shift caused by a wide range of factors, including
prompts, text lengths, topics, and language tasks. To achieve this goal, we
first collect a new dataset with human and ChatGPT texts, and then we conduct
extensive studies on the collected dataset. Our studies unveil insightful
findings which provide guidance for developing future methodologies or data
collection strategies for ChatGPT detection. | [
"Han Xu",
"Jie Ren",
"Pengfei He",
"Shenglai Zeng",
"Yingqian Cui",
"Amy Liu",
"Hui Liu",
"Jiliang Tang"
] | 2023-10-02 16:13:08 | http://arxiv.org/abs/2310.01307v2 | http://arxiv.org/pdf/2310.01307v2 | 2310.01307v2 |
Coupling public and private gradient provably helps optimization | The success of large neural networks is crucially determined by the
availability of data. It has been observed that training only on a small amount
of public data, or privately on the abundant private data can lead to
undesirable degradation of accuracy. In this work, we leverage both private and
public data to improve the optimization, by coupling their gradients via a
weighted linear combination. We formulate an optimal solution for the optimal
weight in the convex setting to indicate that the weighting coefficient should
be hyperparameter-dependent. Then, we prove the acceleration in the convergence
of non-convex loss and the effects of hyper-parameters such as privacy budget,
number of iterations, batch size, and model size on the choice of the weighting
coefficient. We support our analysis with empirical experiments across language
and vision benchmarks, and provide a guideline for choosing the optimal weight
of the gradient coupling. | [
"Ruixuan Liu",
"Zhiqi Bu",
"Yu-xiang Wang",
"Sheng Zha",
"George Karypis"
] | 2023-10-02 16:08:18 | http://arxiv.org/abs/2310.01304v1 | http://arxiv.org/pdf/2310.01304v1 | 2310.01304v1 |
Efficient Remote Sensing Segmentation With Generative Adversarial Transformer | Most deep learning methods that achieve high segmentation accuracy require
deep network architectures that are too heavy and complex to run on embedded
devices with limited storage and memory space. To address this issue, this
paper proposes an efficient Generative Adversarial Transfomer (GATrans) for
achieving high-precision semantic segmentation while maintaining an extremely
efficient size. The framework utilizes a Global Transformer Network (GTNet) as
the generator, efficiently extracting multi-level features through residual
connections. GTNet employs global transformer blocks with progressively linear
computational complexity to reassign global features based on a learnable
similarity function. To focus on object-level and pixel-level information, the
GATrans optimizes the objective function by combining structural similarity
losses. We validate the effectiveness of our approach through extensive
experiments on the Vaihingen dataset, achieving an average F1 score of 90.17%
and an overall accuracy of 91.92%. | [
"Luyi Qiu",
"Dayu Yu",
"Xiaofeng Zhang",
"Chenxiao Zhang"
] | 2023-10-02 15:46:59 | http://arxiv.org/abs/2310.01292v1 | http://arxiv.org/pdf/2310.01292v1 | 2310.01292v1 |
Automated regime detection in multidimensional time series data using sliced Wasserstein k-means clustering | Recent work has proposed Wasserstein k-means (Wk-means) clustering as a
powerful method to identify regimes in time series data, and one-dimensional
asset returns in particular. In this paper, we begin by studying in detail the
behaviour of the Wasserstein k-means clustering algorithm applied to synthetic
one-dimensional time series data. We study the dynamics of the algorithm and
investigate how varying different hyperparameters impacts the performance of
the clustering algorithm for different random initialisations. We compute
simple metrics that we find are useful in identifying high-quality clusterings.
Then, we extend the technique of Wasserstein k-means clustering to
multidimensional time series data by approximating the multidimensional
Wasserstein distance as a sliced Wasserstein distance, resulting in a method we
call `sliced Wasserstein k-means (sWk-means) clustering'. We apply the
sWk-means clustering method to the problem of automated regime detection in
multidimensional time series data, using synthetic data to demonstrate the
validity of the approach. Finally, we show that the sWk-means method is
effective in identifying distinct market regimes in real multidimensional
financial time series, using publicly available foreign exchange spot rate data
as a case study. We conclude with remarks about some limitations of our
approach and potential complementary or alternative approaches. | [
"Qinmeng Luan",
"James Hamp"
] | 2023-10-02 15:37:56 | http://arxiv.org/abs/2310.01285v1 | http://arxiv.org/pdf/2310.01285v1 | 2310.01285v1 |
A Comparison of Mesh-Free Differentiable Programming and Data-Driven Strategies for Optimal Control under PDE Constraints | The field of Optimal Control under Partial Differential Equations (PDE)
constraints is rapidly changing under the influence of Deep Learning and the
accompanying automatic differentiation libraries. Novel techniques like
Physics-Informed Neural Networks (PINNs) and Differentiable Programming (DP)
are to be contrasted with established numerical schemes like Direct-Adjoint
Looping (DAL). We present a comprehensive comparison of DAL, PINN, and DP using
a general-purpose mesh-free differentiable PDE solver based on Radial Basis
Functions. Under Laplace and Navier-Stokes equations, we found DP to be
extremely effective as it produces the most accurate gradients; thriving even
when DAL fails and PINNs struggle. Additionally, we provide a detailed
benchmark highlighting the limited conditions under which any of those methods
can be efficiently used. Our work provides a guide to Optimal Control
practitioners and connects them further to the Deep Learning community. | [
"Roussel Desmond Nzoyem",
"David A. W. Barton",
"Tom Deakin"
] | 2023-10-02 15:30:12 | http://arxiv.org/abs/2310.02286v1 | http://arxiv.org/pdf/2310.02286v1 | 2310.02286v1 |
Cooperative Graph Neural Networks | Graph neural networks are popular architectures for graph machine learning,
based on iterative computation of node representations of an input graph
through a series of invariant transformations. A large class of graph neural
networks follow a standard message-passing paradigm: at every layer, each node
state is updated based on an aggregate of messages from its neighborhood. In
this work, we propose a novel framework for training graph neural networks,
where every node is viewed as a player that can choose to either 'listen',
'broadcast', 'listen and broadcast', or to 'isolate'. The standard message
propagation scheme can then be viewed as a special case of this framework where
every node 'listens and broadcasts' to all neighbors. Our approach offers a
more flexible and dynamic message-passing paradigm, where each node can
determine its own strategy based on their state, effectively exploring the
graph topology while learning. We provide a theoretical analysis of the new
message-passing scheme which is further supported by an extensive empirical
analysis on a synthetic dataset and on real-world datasets. | [
"Ben Finkelshtein",
"Xingyue Huang",
"Michael Bronstein",
"İsmail İlkan Ceylan"
] | 2023-10-02 15:08:52 | http://arxiv.org/abs/2310.01267v1 | http://arxiv.org/pdf/2310.01267v1 | 2310.01267v1 |
Non-Exchangeable Conformal Risk Control | Split conformal prediction has recently sparked great interest due to its
ability to provide formally guaranteed uncertainty sets or intervals for
predictions made by black-box neural models, ensuring a predefined probability
of containing the actual ground truth. While the original formulation assumes
data exchangeability, some extensions handle non-exchangeable data, which is
often the case in many real-world scenarios. In parallel, some progress has
been made in conformal methods that provide statistical guarantees for a
broader range of objectives, such as bounding the best F1-score or minimizing
the false negative rate in expectation. In this paper, we leverage and extend
these two lines of work by proposing non-exchangeable conformal risk control,
which allows controlling the expected value of any monotone loss function when
the data is not exchangeable. Our framework is flexible, makes very few
assumptions, and allows weighting the data based on its statistical similarity
with the test examples; a careful choice of weights may result on tighter
bounds, making our framework useful in the presence of change points, time
series, or other forms of distribution drift. Experiments with both synthetic
and real world data show the usefulness of our method. | [
"António Farinhas",
"Chrysoula Zerva",
"Dennis Ulmer",
"André F. T. Martins"
] | 2023-10-02 15:00:19 | http://arxiv.org/abs/2310.01262v1 | http://arxiv.org/pdf/2310.01262v1 | 2310.01262v1 |
Faster and Accurate Neural Networks with Semantic Inference | Deep neural networks (DNN) usually come with a significant computational
burden. While approaches such as structured pruning and mobile-specific DNNs
have been proposed, they incur drastic accuracy loss. In this paper we leverage
the intrinsic redundancy in latent representations to reduce the computational
load with limited loss in performance. We show that semantically similar inputs
share many filters, especially in the earlier layers. Thus, semantically
similar classes can be clustered to create cluster-specific subgraphs. To this
end, we propose a new framework called Semantic Inference (SINF). In short,
SINF (i) identifies the semantic cluster the object belongs to using a small
additional classifier and (ii) executes the subgraph extracted from the base
DNN related to that semantic cluster for inference. To extract each
cluster-specific subgraph, we propose a new approach named Discriminative
Capability Score (DCS) that finds the subgraph with the capability to
discriminate among the members of a specific semantic cluster. DCS is
independent from SINF and can be applied to any DNN. We benchmark the
performance of DCS on the VGG16, VGG19, and ResNet50 DNNs trained on the
CIFAR100 dataset against 6 state-of-the-art pruning approaches. Our results
show that (i) SINF reduces the inference time of VGG19, VGG16, and ResNet50
respectively by up to 35%, 29% and 15% with only 0.17%, 3.75%, and 6.75%
accuracy loss (ii) DCS achieves respectively up to 3.65%, 4.25%, and 2.36%
better accuracy with VGG16, VGG19, and ResNet50 with respect to existing
discriminative scores (iii) when used as a pruning criterion, DCS achieves up
to 8.13% accuracy gain with 5.82% less parameters than the existing state of
the art work published at ICLR 2023 (iv) when considering per-cluster accuracy,
SINF performs on average 5.73%, 8.38% and 6.36% better than the base VGG16,
VGG19, and ResNet50. | [
"Sazzad Sayyed",
"Jonathan Ashdown",
"Francesco Restuccia"
] | 2023-10-02 14:51:10 | http://arxiv.org/abs/2310.01259v2 | http://arxiv.org/pdf/2310.01259v2 | 2310.01259v2 |
MobileNVC: Real-time 1080p Neural Video Compression on a Mobile Device | Neural video codecs have recently become competitive with standard codecs
such as HEVC in the low-delay setting. However, most neural codecs are large
floating-point networks that use pixel-dense warping operations for temporal
modeling, making them too computationally expensive for deployment on mobile
devices. Recent work has demonstrated that running a neural decoder in real
time on mobile is feasible, but shows this only for 720p RGB video, while the
YUV420 format is more commonly used in production. This work presents the first
neural video codec that decodes 1080p YUV420 video in real time on a mobile
device. Our codec relies on two major contributions. First, we design an
efficient codec that uses a block-based motion compensation algorithm available
on the warping core of the mobile accelerator, and we show how to quantize this
model to integer precision. Second, we implement a fast decoder pipeline that
concurrently runs neural network components on the neural signal processor,
parallel entropy coding on the mobile GPU, and warping on the warping core. Our
codec outperforms the previous on-device codec by a large margin with up to 48
% BD-rate savings, while reducing the MAC count on the receiver side by 10x. We
perform a careful ablation to demonstrate the effect of the introduced motion
compensation scheme, and ablate the effect of model quantization. | [
"Ties van Rozendaal",
"Tushar Singhal",
"Hoang Le",
"Guillaume Sautiere",
"Amir Said",
"Krishna Buska",
"Anjuman Raha",
"Dimitris Kalatzis",
"Hitarth Mehta",
"Frank Mayer",
"Liang Zhang",
"Markus Nagel",
"Auke Wiggers"
] | 2023-10-02 14:50:14 | http://arxiv.org/abs/2310.01258v1 | http://arxiv.org/pdf/2310.01258v1 | 2310.01258v1 |
Pre-training Contextual Location Embeddings in Personal Trajectories via Efficient Hierarchical Location Representations | Pre-training the embedding of a location generated from human mobility data
has become a popular method for location based services. In practice, modeling
the location embedding is too expensive, due to the large number of locations
to be trained in situations with fine-grained resolution or extensive target
regions. Previous studies have handled less than ten thousand distinct
locations, which is insufficient in the real-world applications. To tackle this
problem, we propose a Geo-Tokenizer, designed to efficiently reduce the number
of locations to be trained by representing a location as a combination of
several grids at different scales. In the Geo-Tokenizer, a grid at a larger
scale shares the common set of grids at smaller scales, which is a key factor
in reducing the size of the location vocabulary. The sequences of locations
preprocessed with the Geo-Tokenizer are utilized by a causal location embedding
model to capture the temporal dependencies of locations. This model dynamically
calculates the embedding vector of a target location, which varies depending on
its trajectory. In addition, to efficiently pre-train the location embedding
model, we propose the Hierarchical Auto-regressive Location Model objective to
effectively train decomposed locations in the Geo-Tokenizer. We conducted
experiments on two real-world user trajectory datasets using our pre-trained
location model. The experimental results show that our model significantly
improves the performance of downstream tasks with fewer model parameters
compared to existing location embedding methods. | [
"Chung Park",
"Taesan Kim",
"Junui Hong",
"Minsung Choi",
"Jaegul Choo"
] | 2023-10-02 14:40:24 | http://arxiv.org/abs/2310.01252v1 | http://arxiv.org/pdf/2310.01252v1 | 2310.01252v1 |
Generating 3D Brain Tumor Regions in MRI using Vector-Quantization Generative Adversarial Networks | Medical image analysis has significantly benefited from advancements in deep
learning, particularly in the application of Generative Adversarial Networks
(GANs) for generating realistic and diverse images that can augment training
datasets. However, the effectiveness of such approaches is often limited by the
amount of available data in clinical settings. Additionally, the common
GAN-based approach is to generate entire image volumes, rather than solely the
region of interest (ROI). Research on deep learning-based brain tumor
classification using MRI has shown that it is easier to classify the tumor ROIs
compared to the entire image volumes. In this work, we present a novel
framework that uses vector-quantization GAN and a transformer incorporating
masked token modeling to generate high-resolution and diverse 3D brain tumor
ROIs that can be directly used as augmented data for the classification of
brain tumor ROI. We apply our method to two imbalanced datasets where we
augment the minority class: (1) the Multimodal Brain Tumor Segmentation
Challenge (BraTS) 2019 dataset to generate new low-grade glioma (LGG) ROIs to
balance with high-grade glioma (HGG) class; (2) the internal pediatric LGG
(pLGG) dataset tumor ROIs with BRAF V600E Mutation genetic marker to balance
with BRAF Fusion genetic marker class. We show that the proposed method
outperforms various baseline models in both qualitative and quantitative
measurements. The generated data was used to balance the data in the brain
tumor types classification task. Using the augmented data, our approach
surpasses baseline models by 6.4% in AUC on the BraTS 2019 dataset and 4.3% in
AUC on our internal pLGG dataset. The results indicate the generated tumor ROIs
can effectively address the imbalanced data problem. Our proposed method has
the potential to facilitate an accurate diagnosis of rare brain tumors using
MRI scans. | [
"Meng Zhou",
"Matthias W Wagner",
"Uri Tabori",
"Cynthia Hawkins",
"Birgit B Ertl-Wagner",
"Farzad Khalvati"
] | 2023-10-02 14:39:10 | http://arxiv.org/abs/2310.01251v1 | http://arxiv.org/pdf/2310.01251v1 | 2310.01251v1 |
Self-supervised Learning for Anomaly Detection in Computational Workflows | Anomaly detection is the task of identifying abnormal behavior of a system.
Anomaly detection in computational workflows is of special interest because of
its wide implications in various domains such as cybersecurity, finance, and
social networks. However, anomaly detection in computational workflows~(often
modeled as graphs) is a relatively unexplored problem and poses distinct
challenges. For instance, when anomaly detection is performed on graph data,
the complex interdependency of nodes and edges, the heterogeneity of node
attributes, and edge types must be accounted for. Although the use of graph
neural networks can help capture complex inter-dependencies, the scarcity of
labeled anomalous examples from workflow executions is still a significant
challenge. To address this problem, we introduce an autoencoder-driven
self-supervised learning~(SSL) approach that learns a summary statistic from
unlabeled workflow data and estimates the normal behavior of the computational
workflow in the latent space. In this approach, we combine generative and
contrastive learning objectives to detect outliers in the summary statistics.
We demonstrate that by estimating the distribution of normal behavior in the
latent space, we can outperform state-of-the-art anomaly detection methods on
our benchmark datasets. | [
"Hongwei Jin",
"Krishnan Raghavan",
"George Papadimitriou",
"Cong Wang",
"Anirban Mandal",
"Ewa Deelman",
"Prasanna Balaprakash"
] | 2023-10-02 14:31:56 | http://arxiv.org/abs/2310.01247v1 | http://arxiv.org/pdf/2310.01247v1 | 2310.01247v1 |
Mirror Diffusion Models for Constrained and Watermarked Generation | Modern successes of diffusion models in learning complex, high-dimensional
data distributions are attributed, in part, to their capability to construct
diffusion processes with analytic transition kernels and score functions. The
tractability results in a simulation-free framework with stable regression
losses, from which reversed, generative processes can be learned at scale.
However, when data is confined to a constrained set as opposed to a standard
Euclidean space, these desirable characteristics appear to be lost based on
prior attempts. In this work, we propose Mirror Diffusion Models (MDM), a new
class of diffusion models that generate data on convex constrained sets without
losing any tractability. This is achieved by learning diffusion processes in a
dual space constructed from a mirror map, which, crucially, is a standard
Euclidean space. We derive efficient computation of mirror maps for popular
constrained sets, such as simplices and $\ell_2$-balls, showing significantly
improved performance of MDM over existing methods. For safety and privacy
purposes, we also explore constrained sets as a new mechanism to embed
invisible but quantitative information (i.e., watermarks) in generated data,
for which MDM serves as a compelling approach. Our work brings new algorithmic
opportunities for learning tractable diffusion on complex domains. | [
"Guan-Horng Liu",
"Tianrong Chen",
"Evangelos A. Theodorou",
"Molei Tao"
] | 2023-10-02 14:26:31 | http://arxiv.org/abs/2310.01236v1 | http://arxiv.org/pdf/2310.01236v1 | 2310.01236v1 |
Modality-aware Transformer for Time series Forecasting | Time series forecasting presents a significant challenge, particularly when
its accuracy relies on external data sources rather than solely on historical
values. This issue is prevalent in the financial sector, where the future
behavior of time series is often intricately linked to information derived from
various textual reports and a multitude of economic indicators. In practice,
the key challenge lies in constructing a reliable time series forecasting model
capable of harnessing data from diverse sources and extracting valuable
insights to predict the target time series accurately. In this work, we tackle
this challenging problem and introduce a novel multimodal transformer-based
model named the Modality-aware Transformer. Our model excels in exploring the
power of both categorical text and numerical timeseries to forecast the target
time series effectively while providing insights through its neural attention
mechanism. To achieve this, we develop feature-level attention layers that
encourage the model to focus on the most relevant features within each data
modality. By incorporating the proposed feature-level attention, we develop a
novel Intra-modal multi-head attention (MHA), Inter-modal MHA and
Modality-target MHA in a way that both feature and temporal attentions are
incorporated in MHAs. This enables the MHAs to generate temporal attentions
with consideration of modality and feature importance which leads to more
informative embeddings. The proposed modality-aware structure enables the model
to effectively exploit information within each modality as well as foster
cross-modal understanding. Our extensive experiments on financial datasets
demonstrate that Modality-aware Transformer outperforms existing methods,
offering a novel and practical solution to the complex challenges of
multi-modality time series forecasting. | [
"Hajar Emami",
"Xuan-Hong Dang",
"Yousaf Shah",
"Petros Zerfos"
] | 2023-10-02 14:22:41 | http://arxiv.org/abs/2310.01232v1 | http://arxiv.org/pdf/2310.01232v1 | 2310.01232v1 |
Reconstructing Atmospheric Parameters of Exoplanets Using Deep Learning | Exploring exoplanets has transformed our understanding of the universe by
revealing many planetary systems that defy our current understanding. To study
their atmospheres, spectroscopic observations are used to infer essential
atmospheric properties that are not directly measurable. Estimating atmospheric
parameters that best fit the observed spectrum within a specified atmospheric
model is a complex problem that is difficult to model. In this paper, we
present a multi-target probabilistic regression approach that combines deep
learning and inverse modeling techniques within a multimodal architecture to
extract atmospheric parameters from exoplanets. Our methodology overcomes
computational limitations and outperforms previous approaches, enabling
efficient analysis of exoplanetary atmospheres. This research contributes to
advancements in the field of exoplanet research and offers valuable insights
for future studies. | [
"Flavio Giobergia",
"Alkis Koudounas",
"Elena Baralis"
] | 2023-10-02 14:16:04 | http://arxiv.org/abs/2310.01227v1 | http://arxiv.org/pdf/2310.01227v1 | 2310.01227v1 |
A path-norm toolkit for modern networks: consequences, promises and challenges | This work introduces the first toolkit around path-norms that is fully able
to encompass general DAG ReLU networks with biases, skip connections and any
operation based on the extraction of order statistics: max pooling, GroupSort
etc. This toolkit notably allows us to establish generalization bounds for
modern neural networks that are not only the most widely applicable path-norm
based ones, but also recover or beat the sharpest known bounds of this type.
These extended path-norms further enjoy the usual benefits of path-norms: ease
of computation, invariance under the symmetries of the network, and improved
sharpness on feedforward networks compared to the product of operators' norms,
another complexity measure most commonly used.
The versatility of the toolkit and its ease of implementation allow us to
challenge the concrete promises of path-norm-based generalization bounds, by
numerically evaluating the sharpest known bounds for ResNets on ImageNet. | [
"Antoine Gonon",
"Nicolas Brisebarre",
"Elisa Riccietti",
"Rémi Gribonval"
] | 2023-10-02 14:12:53 | http://arxiv.org/abs/2310.01225v2 | http://arxiv.org/pdf/2310.01225v2 | 2310.01225v2 |
Revisiting Mobility Modeling with Graph: A Graph Transformer Model for Next Point-of-Interest Recommendation | Next Point-of-Interest (POI) recommendation plays a crucial role in urban
mobility applications. Recently, POI recommendation models based on Graph
Neural Networks (GNN) have been extensively studied and achieved, however, the
effective incorporation of both spatial and temporal information into such
GNN-based models remains challenging. Extracting distinct fine-grained features
unique to each piece of information is difficult since temporal information
often includes spatial information, as users tend to visit nearby POIs. To
address the challenge, we propose \textbf{\underline{Mob}}ility
\textbf{\underline{G}}raph \textbf{\underline{T}}ransformer (MobGT) that
enables us to fully leverage graphs to capture both the spatial and temporal
features in users' mobility patterns. MobGT combines individual spatial and
temporal graph encoders to capture unique features and global user-location
relations. Additionally, it incorporates a mobility encoder based on Graph
Transformer to extract higher-order information between POIs. To address the
long-tailed problem in spatial-temporal data, MobGT introduces a novel loss
function, Tail Loss. Experimental results demonstrate that MobGT outperforms
state-of-the-art models on various datasets and metrics, achieving 24\%
improvement on average. Our codes are available at
\url{https://github.com/Yukayo/MobGT}. | [
"Xiaohang Xu",
"Toyotaro Suzumura",
"Jiawei Yong",
"Masatoshi Hanai",
"Chuang Yang",
"Hiroki Kanezashi",
"Renhe Jiang",
"Shintaro Fukushima"
] | 2023-10-02 14:11:16 | http://arxiv.org/abs/2310.01224v1 | http://arxiv.org/pdf/2310.01224v1 | 2310.01224v1 |
PASTA: PArallel Spatio-Temporal Attention with spatial auto-correlation gating for fine-grained crowd flow prediction | Understanding the movement patterns of objects (e.g., humans and vehicles) in
a city is essential for many applications, including city planning and
management. This paper proposes a method for predicting future city-wide crowd
flows by modeling the spatio-temporal patterns of historical crowd flows in
fine-grained city-wide maps. We introduce a novel neural network named PArallel
Spatio-Temporal Attention with spatial auto-correlation gating (PASTA) that
effectively captures the irregular spatio-temporal patterns of fine-grained
maps. The novel components in our approach include spatial auto-correlation
gating, multi-scale residual block, and temporal attention gating module. The
spatial auto-correlation gating employs the concept of spatial statistics to
identify irregular spatial regions. The multi-scale residual block is
responsible for handling multiple range spatial dependencies in the
fine-grained map, and the temporal attention gating filters out irrelevant
temporal information for the prediction. The experimental results demonstrate
that our model outperforms other competing baselines, especially under
challenging conditions that contain irregular spatial regions. We also provide
a qualitative analysis to derive the critical time information where our model
assigns high attention scores in prediction. | [
"Chung Park",
"Junui Hong",
"Cheonbok Park",
"Taesan Kim",
"Minsung Choi",
"Jaegul Choo"
] | 2023-10-02 14:10:42 | http://arxiv.org/abs/2310.02284v1 | http://arxiv.org/pdf/2310.02284v1 | 2310.02284v1 |
ScaLearn: Simple and Highly Parameter-Efficient Task Transfer by Learning to Scale | Multi-task learning (MTL) has shown considerable practical benefits,
particularly when using pre-trained language models (PLMs). While this is
commonly achieved by simultaneously learning $n$ tasks under a joint
optimization procedure, recent methods such as AdapterFusion structure the
problem into two distinct stages: (i) task learning, where knowledge specific
to a task is encapsulated within sets of parameters (\eg adapters), and (ii)
transfer, where this already learned knowledge is leveraged for a target task.
This separation of concerns provides numerous benefits, such as promoting
reusability, and addressing cases involving data privacy and societal concerns;
on the flip side, current two-stage MTL methods come with the cost of
introducing a substantial number of additional parameters. In this work, we
address this issue by leveraging the usefulness of linearly scaling the output
representations of source adapters for transfer learning. We introduce
ScaLearn, a simple and highly parameter-efficient two-stage MTL method that
capitalizes on the knowledge of the source tasks by learning a minimal set of
scaling parameters that enable effective knowledge transfer to a target task.
Our experiments on three benchmarks (GLUE, SuperGLUE, and HumSet) show that our
ScaLearn, in addition to facilitating the benefits of two-stage MTL,
consistently outperforms strong baselines with only a small number of transfer
parameters - roughly 0.35% of those of AdapterFusion. Remarkably, we observe
that ScaLearn maintains its strong abilities even when further reducing
parameters through uniform scaling and layer-sharing, achieving similarly
competitive results with only $8$ transfer parameters for each target task. Our
proposed approach thus demonstrates the power of simple scaling as a promise
for more efficient task transfer. | [
"Markus Frohmann",
"Carolin Holtermann",
"Shahed Masoudian",
"Anne Lauscher",
"Navid Rekabsaz"
] | 2023-10-02 14:01:36 | http://arxiv.org/abs/2310.01217v1 | http://arxiv.org/pdf/2310.01217v1 | 2310.01217v1 |
From Bricks to Bridges: Product of Invariances to Enhance Latent Space Communication | It has been observed that representations learned by distinct neural networks
conceal structural similarities when the models are trained under similar
inductive biases. From a geometric perspective, identifying the classes of
transformations and the related invariances that connect these representations
is fundamental to unlocking applications, such as merging, stitching, and
reusing different neural modules. However, estimating task-specific
transformations a priori can be challenging and expensive due to several
factors (e.g., weights initialization, training hyperparameters, or data
modality). To this end, we introduce a versatile method to directly incorporate
a set of invariances into the representations, constructing a product space of
invariant components on top of the latent representations without requiring
prior knowledge about the optimal invariance to infuse. We validate our
solution on classification and reconstruction tasks, observing consistent
latent similarity and downstream performance improvements in a zero-shot
stitching setting. The experimental analysis comprises three modalities
(vision, text, and graphs), twelve pretrained foundational models, eight
benchmarks, and several architectures trained from scratch. | [
"Irene Cannistraci",
"Luca Moschella",
"Marco Fumero",
"Valentino Maiorca",
"Emanuele Rodolà"
] | 2023-10-02 13:55:38 | http://arxiv.org/abs/2310.01211v1 | http://arxiv.org/pdf/2310.01211v1 | 2310.01211v1 |
Towards Robust Cardiac Segmentation using Graph Convolutional Networks | Fully automatic cardiac segmentation can be a fast and reproducible method to
extract clinical measurements from an echocardiography examination. The U-Net
architecture is the current state-of-the-art deep learning architecture for
medical segmentation and can segment cardiac structures in real-time with
average errors comparable to inter-observer variability. However, this
architecture still generates large outliers that are often anatomically
incorrect. This work uses the concept of graph convolutional neural networks
that predict the contour points of the structures of interest instead of
labeling each pixel. We propose a graph architecture that uses two
convolutional rings based on cardiac anatomy and show that this eliminates
anatomical incorrect multi-structure segmentations on the publicly available
CAMUS dataset. Additionally, this work contributes with an ablation study on
the graph convolutional architecture and an evaluation of clinical measurements
on the clinical HUNT4 dataset. Finally, we propose to use the inter-model
agreement of the U-Net and the graph network as a predictor of both the input
and segmentation quality. We show this predictor can detect out-of-distribution
and unsuitable input images in real-time. Source code is available online:
https://github.com/gillesvntnu/GCN_multistructure | [
"Gilles Van De Vyver",
"Sarina Thomas",
"Guy Ben-Yosef",
"Sindre Hellum Olaisen",
"Håvard Dalen",
"Lasse Løvstakken",
"Erik Smistad"
] | 2023-10-02 13:55:06 | http://arxiv.org/abs/2310.01210v2 | http://arxiv.org/pdf/2310.01210v2 | 2310.01210v2 |
Unified Uncertainty Calibration | To build robust, fair, and safe AI systems, we would like our classifiers to
say ``I don't know'' when facing test examples that are difficult or fall
outside of the training classes.The ubiquitous strategy to predict under
uncertainty is the simplistic \emph{reject-or-classify} rule: abstain from
prediction if epistemic uncertainty is high, classify otherwise.Unfortunately,
this recipe does not allow different sources of uncertainty to communicate with
each other, produces miscalibrated predictions, and it does not allow to
correct for misspecifications in our uncertainty estimates. To address these
three issues, we introduce \emph{unified uncertainty calibration (U2C)}, a
holistic framework to combine aleatoric and epistemic uncertainties. U2C
enables a clean learning-theoretical analysis of uncertainty estimation, and
outperforms reject-or-classify across a variety of ImageNet benchmarks. | [
"Kamalika Chaudhuri",
"David Lopez-Paz"
] | 2023-10-02 13:42:36 | http://arxiv.org/abs/2310.01202v1 | http://arxiv.org/pdf/2310.01202v1 | 2310.01202v1 |
SWoTTeD: An Extension of Tensor Decomposition to Temporal Phenotyping | Tensor decomposition has recently been gaining attention in the machine
learning community for the analysis of individual traces, such as Electronic
Health Records (EHR). However, this task becomes significantly more difficult
when the data follows complex temporal patterns. This paper introduces the
notion of a temporal phenotype as an arrangement of features over time and it
proposes SWoTTeD (Sliding Window for Temporal Tensor Decomposition), a novel
method to discover hidden temporal patterns. SWoTTeD integrates several
constraints and regularizations to enhance the interpretability of the
extracted phenotypes. We validate our proposal using both synthetic and
real-world datasets, and we present an original usecase using data from the
Greater Paris University Hospital. The results show that SWoTTeD achieves at
least as accurate reconstruction as recent state-of-the-art tensor
decomposition models, and extracts temporal phenotypes that are meaningful for
clinicians. | [
"Hana Sebia",
"Thomas Guyet",
"Etienne Audureau"
] | 2023-10-02 13:42:11 | http://arxiv.org/abs/2310.01201v1 | http://arxiv.org/pdf/2310.01201v1 | 2310.01201v1 |
Federated K-means Clustering | Federated learning is a technique that enables the use of distributed
datasets for machine learning purposes without requiring data to be pooled,
thereby better preserving privacy and ownership of the data. While supervised
FL research has grown substantially over the last years, unsupervised FL
methods remain scarce. This work introduces an algorithm which implements
K-means clustering in a federated manner, addressing the challenges of varying
number of clusters between centers, as well as convergence on less separable
datasets. | [
"Swier Garst",
"Marcel Reinders"
] | 2023-10-02 13:32:00 | http://arxiv.org/abs/2310.01195v1 | http://arxiv.org/pdf/2310.01195v1 | 2310.01195v1 |
If there is no underfitting, there is no Cold Posterior Effect | The cold posterior effect (CPE) (Wenzel et al., 2020) in Bayesian deep
learning shows that, for posteriors with a temperature $T<1$, the resulting
posterior predictive could have better performances than the Bayesian posterior
($T=1$). As the Bayesian posterior is known to be optimal under perfect model
specification, many recent works have studied the presence of CPE as a model
misspecification problem, arising from the prior and/or from the likelihood
function. In this work, we provide a more nuanced understanding of the CPE as
we show that misspecification leads to CPE only when the resulting Bayesian
posterior underfits. In fact, we theoretically show that if there is no
underfitting, there is no CPE. | [
"Yijie Zhang",
"Yi-Shan Wu",
"Luis A. Ortega",
"Andrés R. Masegosa"
] | 2023-10-02 13:28:09 | http://arxiv.org/abs/2310.01189v1 | http://arxiv.org/pdf/2310.01189v1 | 2310.01189v1 |
Quantifying the Plausibility of Context Reliance in Neural Machine Translation | Establishing whether language models can use contextual information in a
human-plausible way is important to ensure their safe adoption in real-world
settings. However, the questions of when and which parts of the context affect
model generations are typically tackled separately, and current plausibility
evaluations are practically limited to a handful of artificial benchmarks. To
address this, we introduce Plausibility Evaluation of Context Reliance
(PECoRe), an end-to-end interpretability framework designed to quantify context
usage in language models' generations. Our approach leverages model internals
to (i) contrastively identify context-sensitive target tokens in generated
texts and (ii) link them to contextual cues justifying their prediction. We use
PECoRe to quantify the plausibility of context-aware machine translation
models, comparing model rationales with human annotations across several
discourse-level phenomena. Finally, we apply our method to unannotated
generations to identify context-mediated predictions and highlight instances of
(im)plausible context usage in model translations. | [
"Gabriele Sarti",
"Grzegorz Chrupała",
"Malvina Nissim",
"Arianna Bisazza"
] | 2023-10-02 13:26:43 | http://arxiv.org/abs/2310.01188v1 | http://arxiv.org/pdf/2310.01188v1 | 2310.01188v1 |
Graph Isomorphic Networks for Assessing Reliability of the Medium-Voltage Grid | Ensuring electricity grid reliability becomes increasingly challenging with
the shift towards renewable energy and declining conventional capacities.
Distribution System Operators (DSOs) aim to achieve grid reliability by
verifying the n-1 principle, ensuring continuous operation in case of component
failure. Electricity networks' complex graph-based data holds crucial
information for n-1 assessment: graph structure and data about stations/cables.
Unlike traditional machine learning methods, Graph Neural Networks (GNNs)
directly handle graph-structured data. This paper proposes using Graph
Isomorphic Networks (GINs) for n-1 assessments in medium voltage grids. The GIN
framework is designed to generalise to unseen grids and utilise graph structure
and data about stations/cables. The proposed GIN approach demonstrates faster
and more reliable grid assessments than a traditional mathematical optimisation
approach, reducing prediction times by approximately a factor of 1000. The
findings offer a promising approach to address computational challenges and
enhance the reliability and efficiency of energy grid assessments. | [
"Charlotte Cambier van Nooten",
"Tom van de Poll",
"Sonja Füllhase",
"Jacco Heres",
"Tom Heskes",
"Yuliya Shapovalova"
] | 2023-10-02 13:19:35 | http://arxiv.org/abs/2310.01181v2 | http://arxiv.org/pdf/2310.01181v2 | 2310.01181v2 |
Evolutionary Neural Architecture Search for Transformer in Knowledge Tracing | Knowledge tracing (KT) aims to trace students' knowledge states by predicting
whether students answer correctly on exercises. Despite the excellent
performance of existing Transformer-based KT approaches, they are criticized
for the manually selected input features for fusion and the defect of single
global context modelling to directly capture students' forgetting behavior in
KT, when the related records are distant from the current record in terms of
time. To address the issues, this paper first considers adding convolution
operations to the Transformer to enhance its local context modelling ability
used for students' forgetting behavior, then proposes an evolutionary neural
architecture search approach to automate the input feature selection and
automatically determine where to apply which operation for achieving the
balancing of the local/global context modelling. In the search space, the
original global path containing the attention module in Transformer is replaced
with the sum of a global path and a local path that could contain different
convolutions, and the selection of input features is also considered. To search
the best architecture, we employ an effective evolutionary algorithm to explore
the search space and also suggest a search space reduction strategy to
accelerate the convergence of the algorithm. Experimental results on the two
largest and most challenging education datasets demonstrate the effectiveness
of the architecture found by the proposed approach. | [
"Shangshang Yang",
"Xiaoshan Yu",
"Ye Tian",
"Xueming Yan",
"Haiping Ma",
"Xingyi Zhang"
] | 2023-10-02 13:19:33 | http://arxiv.org/abs/2310.01180v1 | http://arxiv.org/pdf/2310.01180v1 | 2310.01180v1 |
Light Schrödinger Bridge | Despite the recent advances in the field of computational Schrodinger Bridges
(SB), most existing SB solvers are still heavy-weighted and require complex
optimization of several neural networks. It turns out that there is no
principal solver which plays the role of simple-yet-effective baseline for SB
just like, e.g., $k$-means method in clustering, logistic regression in
classification or Sinkhorn algorithm in discrete optimal transport. We address
this issue and propose a novel fast and simple SB solver. Our development is a
smart combination of two ideas which recently appeared in the field: (a)
parameterization of the Schrodinger potentials with sum-exp quadratic functions
and (b) viewing the log-Schrodinger potentials as the energy functions. We show
that combined together these ideas yield a lightweight, simulation-free and
theoretically justified SB solver with a simple straightforward optimization
objective. As a result, it allows solving SB in moderate dimensions in a matter
of minutes on CPU without a painful hyperparameter selection. Our light solver
resembles the Gaussian mixture model which is widely used for density
estimation. Inspired by this similarity, we also prove an important theoretical
result showing that our light solver is a universal approximator of SBs. The
code for the LightSB solver can be found at
https://github.com/ngushchin/LightSB | [
"Alexander Korotin",
"Nikita Gushchin",
"Evgeny Burnaev"
] | 2023-10-02 13:06:45 | http://arxiv.org/abs/2310.01174v1 | http://arxiv.org/pdf/2310.01174v1 | 2310.01174v1 |
Towards guarantees for parameter isolation in continual learning | Deep learning has proved to be a successful paradigm for solving many
challenges in machine learning. However, deep neural networks fail when trained
sequentially on multiple tasks, a shortcoming known as catastrophic forgetting
in the continual learning literature. Despite a recent flourish of learning
algorithms successfully addressing this problem, we find that provable
guarantees against catastrophic forgetting are lacking. In this work, we study
the relationship between learning and forgetting by looking at the geometry of
neural networks' loss landscape. We offer a unifying perspective on a family of
continual learning algorithms, namely methods based on parameter isolation, and
we establish guarantees on catastrophic forgetting for some of them. | [
"Giulia Lanzillotta",
"Sidak Pal Singh",
"Benjamin F. Grewe",
"Thomas Hofmann"
] | 2023-10-02 12:50:15 | http://arxiv.org/abs/2310.01165v1 | http://arxiv.org/pdf/2310.01165v1 | 2310.01165v1 |
DINE: Dimensional Interpretability of Node Embeddings | Graphs are ubiquitous due to their flexibility in representing social and
technological systems as networks of interacting elements. Graph representation
learning methods, such as node embeddings, are powerful approaches to map nodes
into a latent vector space, allowing their use for various graph tasks. Despite
their success, only few studies have focused on explaining node embeddings
locally. Moreover, global explanations of node embeddings remain unexplored,
limiting interpretability and debugging potentials. We address this gap by
developing human-understandable explanations for dimensions in node embeddings.
Towards that, we first develop new metrics that measure the global
interpretability of embedding vectors based on the marginal contribution of the
embedding dimensions to predicting graph structure. We say that an embedding
dimension is more interpretable if it can faithfully map to an understandable
sub-structure in the input graph - like community structure. Having observed
that standard node embeddings have low interpretability, we then introduce DINE
(Dimension-based Interpretable Node Embedding), a novel approach that can
retrofit existing node embeddings by making them more interpretable without
sacrificing their task performance. We conduct extensive experiments on
synthetic and real-world graphs and show that we can simultaneously learn
highly interpretable node embeddings with effective performance in link
prediction. | [
"Simone Piaggesi",
"Megha Khosla",
"André Panisson",
"Avishek Anand"
] | 2023-10-02 12:47:42 | http://arxiv.org/abs/2310.01162v1 | http://arxiv.org/pdf/2310.01162v1 | 2310.01162v1 |
Iterative Semi-Supervised Learning for Abdominal Organs and Tumor Segmentation | Deep-learning (DL) based methods are playing an important role in the task of
abdominal organs and tumors segmentation in CT scans. However, the large
requirements of annotated datasets heavily limit its development. The FLARE23
challenge provides a large-scale dataset with both partially and fully
annotated data, which also focuses on both segmentation accuracy and
computational efficiency. In this study, we propose to use the strategy of
Semi-Supervised Learning (SSL) and iterative pseudo labeling to address
FLARE23. Initially, a deep model (nn-UNet) trained on datasets with complete
organ annotations (about 220 scans) generates pseudo labels for the whole
dataset. These pseudo labels are then employed to train a more powerful
segmentation model. Employing the FLARE23 dataset, our approach achieves an
average DSC score of 89.63% for organs and 46.07% for tumors on online
validation leaderboard. For organ segmentation, We obtain 0.9007\% DSC and
0.9493\% NSD. For tumor segmentation, we obtain 0.3785% DSC and 0.2842% NSD.
Our code is available at https://github.com/USTguy/Flare23. | [
"Jiaxin Zhuang",
"Luyang Luo",
"Zhixuan Chen",
"Linshan Wu"
] | 2023-10-02 12:45:13 | http://arxiv.org/abs/2310.01159v1 | http://arxiv.org/pdf/2310.01159v1 | 2310.01159v1 |
RRR-Net: Reusing, Reducing, and Recycling a Deep Backbone Network | It has become mainstream in computer vision and other machine learning
domains to reuse backbone networks pre-trained on large datasets as
preprocessors. Typically, the last layer is replaced by a shallow learning
machine of sorts; the newly-added classification head and (optionally) deeper
layers are fine-tuned on a new task. Due to its strong performance and
simplicity, a common pre-trained backbone network is ResNet152.However,
ResNet152 is relatively large and induces inference latency. In many cases, a
compact and efficient backbone with similar performance would be preferable
over a larger, slower one. This paper investigates techniques to reuse a
pre-trained backbone with the objective of creating a smaller and faster model.
Starting from a large ResNet152 backbone pre-trained on ImageNet, we first
reduce it from 51 blocks to 5 blocks, reducing its number of parameters and
FLOPs by more than 6 times, without significant performance degradation. Then,
we split the model after 3 blocks into several branches, while preserving the
same number of parameters and FLOPs, to create an ensemble of sub-networks to
improve performance. Our experiments on a large benchmark of $40$ image
classification datasets from various domains suggest that our techniques match
the performance (if not better) of ``classical backbone fine-tuning'' while
achieving a smaller model size and faster inference speed. | [
"Haozhe Sun",
"Isabelle Guyon",
"Felix Mohr",
"Hedi Tabia"
] | 2023-10-02 12:44:08 | http://arxiv.org/abs/2310.01157v1 | http://arxiv.org/pdf/2310.01157v1 | 2310.01157v1 |
Subsets and Splits
No saved queries yet
Save your SQL queries to embed, download, and access them later. Queries will appear here once saved.