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
|
|---|---|---|---|---|---|---|
Sharing Information Between Machine Tools to Improve Surface Finish Forecasting | At present, most surface-quality prediction methods can only perform
single-task prediction which results in under-utilised datasets, repetitive
work and increased experimental costs. To counter this, the authors propose a
Bayesian hierarchical model to predict surface-roughness measurements for a
turning machining process. The hierarchical model is compared to multiple
independent Bayesian linear regression models to showcase the benefits of
partial pooling in a machining setting with respect to prediction accuracy and
uncertainty quantification. | [
"Daniel R. Clarkson",
"Lawrence A. Bull",
"Tina A. Dardeno",
"Chandula T. Wickramarachchi",
"Elizabeth J. Cross",
"Timothy J. Rogers",
"Keith Worden",
"Nikolaos Dervilis",
"Aidan J. Hughes"
] | 2023-10-09 15:44:35 | http://arxiv.org/abs/2310.05807v1 | http://arxiv.org/pdf/2310.05807v1 | 2310.05807v1 |
Boosted Control Functions | Modern machine learning methods and the availability of large-scale data
opened the door to accurately predict target quantities from large sets of
covariates. However, existing prediction methods can perform poorly when the
training and testing data are different, especially in the presence of hidden
confounding. While hidden confounding is well studied for causal effect
estimation (e.g., instrumental variables), this is not the case for prediction
tasks. This work aims to bridge this gap by addressing predictions under
different training and testing distributions in the presence of unobserved
confounding. In particular, we establish a novel connection between the field
of distribution generalization from machine learning, and simultaneous equation
models and control function from econometrics. Central to our contribution are
simultaneous equation models for distribution generalization (SIMDGs) which
describe the data-generating process under a set of distributional shifts.
Within this framework, we propose a strong notion of invariance for a
predictive model and compare it with existing (weaker) versions. Building on
the control function approach from instrumental variable regression, we propose
the boosted control function (BCF) as a target of inference and prove its
ability to successfully predict even in intervened versions of the underlying
SIMDG. We provide necessary and sufficient conditions for identifying the BCF
and show that it is worst-case optimal. We introduce the ControlTwicing
algorithm to estimate the BCF and analyze its predictive performance on
simulated and real world data. | [
"Nicola Gnecco",
"Jonas Peters",
"Sebastian Engelke",
"Niklas Pfister"
] | 2023-10-09 15:43:46 | http://arxiv.org/abs/2310.05805v1 | http://arxiv.org/pdf/2310.05805v1 | 2310.05805v1 |
An operator preconditioning perspective on training in physics-informed machine learning | In this paper, we investigate the behavior of gradient descent algorithms in
physics-informed machine learning methods like PINNs, which minimize residuals
connected to partial differential equations (PDEs). Our key result is that the
difficulty in training these models is closely related to the conditioning of a
specific differential operator. This operator, in turn, is associated to the
Hermitian square of the differential operator of the underlying PDE. If this
operator is ill-conditioned, it results in slow or infeasible training.
Therefore, preconditioning this operator is crucial. We employ both rigorous
mathematical analysis and empirical evaluations to investigate various
strategies, explaining how they better condition this critical operator, and
consequently improve training. | [
"Tim De Ryck",
"Florent Bonnet",
"Siddhartha Mishra",
"Emmanuel de Bézenac"
] | 2023-10-09 15:37:06 | http://arxiv.org/abs/2310.05801v1 | http://arxiv.org/pdf/2310.05801v1 | 2310.05801v1 |
The First Cadenza Signal Processing Challenge: Improving Music for Those With a Hearing Loss | The Cadenza project aims to improve the audio quality of music for those who
have a hearing loss. This is being done through a series of signal processing
challenges, to foster better and more inclusive technologies. In the first
round, two common listening scenarios are considered: listening to music over
headphones, and with a hearing aid in a car. The first scenario is cast as a
demixing-remixing problem, where the music is decomposed into vocals, bass,
drums and other components. These can then be intelligently remixed in a
personalized way, to increase the audio quality for a person who has a hearing
loss. In the second scenario, music is coming from car loudspeakers, and the
music has to be enhanced to overcome the masking effect of the car noise. This
is done by taking into account the music, the hearing ability of the listener,
the hearing aid and the speed of the car. The audio quality of the submissions
will be evaluated using the Hearing Aid Audio Quality Index (HAAQI) for
objective assessment and by a panel of people with hearing loss for subjective
evaluation. | [
"Gerardo Roa Dabike",
"Scott Bannister",
"Jennifer Firth",
"Simone Graetzer",
"Rebecca Vos",
"Michael A. Akeroyd",
"Jon Barker",
"Trevor J. Cox",
"Bruno Fazenda",
"Alinka Greasley",
"William Whitmer"
] | 2023-10-09 15:36:15 | http://arxiv.org/abs/2310.05799v1 | http://arxiv.org/pdf/2310.05799v1 | 2310.05799v1 |
Are Large Language Models Post Hoc Explainers? | Large Language Models (LLMs) are increasingly used as powerful tools for a
plethora of natural language processing (NLP) applications. A recent
innovation, in-context learning (ICL), enables LLMs to learn new tasks by
supplying a few examples in the prompt during inference time, thereby
eliminating the need for model fine-tuning. While LLMs have been utilized in
several applications, their applicability in explaining the behavior of other
models remains relatively unexplored. Despite the growing number of new
explanation techniques, many require white-box access to the model and/or are
computationally expensive, highlighting a need for next-generation post hoc
explainers. In this work, we present the first framework to study the
effectiveness of LLMs in explaining other predictive models. More specifically,
we propose a novel framework encompassing multiple prompting strategies: i)
Perturbation-based ICL, ii) Prediction-based ICL, iii) Instruction-based ICL,
and iv) Explanation-based ICL, with varying levels of information about the
underlying ML model and the local neighborhood of the test sample. We conduct
extensive experiments with real-world benchmark datasets to demonstrate that
LLM-generated explanations perform on par with state-of-the-art post hoc
explainers using their ability to leverage ICL examples and their internal
knowledge in generating model explanations. On average, across four datasets
and two ML models, we observe that LLMs identify the most important feature
with 72.19% accuracy, opening up new frontiers in explainable artificial
intelligence (XAI) to explore LLM-based explanation frameworks. | [
"Nicholas Kroeger",
"Dan Ley",
"Satyapriya Krishna",
"Chirag Agarwal",
"Himabindu Lakkaraju"
] | 2023-10-09 15:31:03 | http://arxiv.org/abs/2310.05797v2 | http://arxiv.org/pdf/2310.05797v2 | 2310.05797v2 |
DiffuSeq-v2: Bridging Discrete and Continuous Text Spaces for Accelerated Seq2Seq Diffusion Models | Diffusion models have gained prominence in generating high-quality sequences
of text. Nevertheless, current approaches predominantly represent discrete text
within a continuous diffusion space, which incurs substantial computational
overhead during training and results in slower sampling speeds. In this paper,
we introduce a soft absorbing state that facilitates the diffusion model in
learning to reconstruct discrete mutations based on the underlying Gaussian
space, thereby enhancing its capacity to recover conditional signals. During
the sampling phase, we employ state-of-the-art ODE solvers within the
continuous space to expedite the sampling process. Comprehensive experimental
evaluations reveal that our proposed method effectively accelerates the
training convergence by 4x and generates samples of similar quality 800x
faster, rendering it significantly closer to practical application.
\footnote{The code is released at \url{https://github.com/Shark-NLP/DiffuSeq} | [
"Shansan Gong",
"Mukai Li",
"Jiangtao Feng",
"Zhiyong Wu",
"Lingpeng Kong"
] | 2023-10-09 15:29:10 | http://arxiv.org/abs/2310.05793v2 | http://arxiv.org/pdf/2310.05793v2 | 2310.05793v2 |
Efficient Hybrid Oversampling and Intelligent Undersampling for Imbalanced Big Data Classification | Imbalanced classification is a well-known challenge faced by many real-world
applications. This issue occurs when the distribution of the target variable is
skewed, leading to a prediction bias toward the majority class. With the
arrival of the Big Data era, there is a pressing need for efficient solutions
to solve this problem. In this work, we present a novel resampling method
called SMOTENN that combines intelligent undersampling and oversampling using a
MapReduce framework. Both procedures are performed on the same pass over the
data, conferring efficiency to the technique. The SMOTENN method is
complemented with an efficient implementation of the neighborhoods related to
the minority samples. Our experimental results show the virtues of this
approach, outperforming alternative resampling techniques for small- and
medium-sized datasets while achieving positive results on large datasets with
reduced running times. | [
"Carla Vairetti",
"José Luis Assadi",
"Sebastián Maldonado"
] | 2023-10-09 15:22:13 | http://arxiv.org/abs/2310.05789v1 | http://arxiv.org/pdf/2310.05789v1 | 2310.05789v1 |
Why Should This Article Be Deleted? Transparent Stance Detection in Multilingual Wikipedia Editor Discussions | The moderation of content on online platforms is usually non-transparent. On
Wikipedia, however, this discussion is carried out publicly and the editors are
encouraged to use the content moderation policies as explanations for making
moderation decisions. Currently, only a few comments explicitly mention those
policies -- 20% of the English ones, but as few as 2% of the German and Turkish
comments. To aid in this process of understanding how content is moderated, we
construct a novel multilingual dataset of Wikipedia editor discussions along
with their reasoning in three languages. The dataset contains the stances of
the editors (keep, delete, merge, comment), along with the stated reason, and a
content moderation policy, for each edit decision. We demonstrate that stance
and corresponding reason (policy) can be predicted jointly with a high degree
of accuracy, adding transparency to the decision-making process. We release
both our joint prediction models and the multilingual content moderation
dataset for further research on automated transparent content moderation. | [
"Lucie-Aimée Kaffee",
"Arnav Arora",
"Isabelle Augenstein"
] | 2023-10-09 15:11:02 | http://arxiv.org/abs/2310.05779v2 | http://arxiv.org/pdf/2310.05779v2 | 2310.05779v2 |
Rethinking Memory and Communication Cost for Efficient Large Language Model Training | As model sizes and training datasets continue to increase, large-scale model
training frameworks reduce memory consumption by various sharding techniques.
However, the huge communication overhead reduces the training efficiency,
especially in public cloud environments with varying network bandwidths. In
this paper, we rethink the impact of memory consumption and communication
overhead on the training speed of large language model, and propose a
memory-communication balanced \underline{Pa}rtial \underline{R}edundancy
\underline{O}ptimizer (PaRO). PaRO reduces the amount and frequency of
inter-group communication by grouping GPU clusters and introducing minor
intra-group memory redundancy, thereby improving the training efficiency of the
model. Additionally, we propose a Hierarchical Overlapping Ring (HO-Ring)
communication topology to enhance communication efficiency between nodes or
across switches in large model training. Our experiments demonstrate that the
HO-Ring algorithm improves communication efficiency by 32.6\% compared to the
traditional Ring algorithm. Compared to the baseline ZeRO, PaRO significantly
improves training throughput by 1.2x-2.6x and achieves a near-linear
scalability. Therefore, the PaRO strategy provides more fine-grained options
for the trade-off between memory consumption and communication overhead in
different training scenarios. | [
"Chan Wu",
"Hanxiao Zhang",
"Lin Ju",
"Jinjing Huang",
"Youshao Xiao",
"Zhaoxin Huan",
"Siyuan Li",
"Fanzhuang Meng",
"Lei Liang",
"Xiaolu Zhang",
"Jun Zhou"
] | 2023-10-09 15:08:32 | http://arxiv.org/abs/2310.06003v1 | http://arxiv.org/pdf/2310.06003v1 | 2310.06003v1 |
Foundation Models Meet Visualizations: Challenges and Opportunities | Recent studies have indicated that foundation models, such as BERT and GPT,
excel in adapting to a variety of downstream tasks. This adaptability has
established them as the dominant force in building artificial intelligence (AI)
systems. As visualization techniques intersect with these models, a new
research paradigm emerges. This paper divides these intersections into two main
areas: visualizations for foundation models (VIS4FM) and foundation models for
visualizations (FM4VIS). In VIS4FM, we explore the primary role of
visualizations in understanding, refining, and evaluating these intricate
models. This addresses the pressing need for transparency, explainability,
fairness, and robustness. Conversely, within FM4VIS, we highlight how
foundation models can be utilized to advance the visualization field itself.
The confluence of foundation models and visualizations holds great promise, but
it also comes with its own set of challenges. By highlighting these challenges
and the growing opportunities, this paper seeks to provide a starting point for
continued exploration in this promising avenue. | [
"Weikai Yang",
"Mengchen Liu",
"Zheng Wang",
"Shixia Liu"
] | 2023-10-09 14:57:05 | http://arxiv.org/abs/2310.05771v1 | http://arxiv.org/pdf/2310.05771v1 | 2310.05771v1 |
Harmonic Self-Conditioned Flow Matching for Multi-Ligand Docking and Binding Site Design | A significant amount of protein function requires binding small molecules,
including enzymatic catalysis. As such, designing binding pockets for small
molecules has several impactful applications ranging from drug synthesis to
energy storage. Towards this goal, we first develop HarmonicFlow, an improved
generative process over 3D protein-ligand binding structures based on our
self-conditioned flow matching objective. FlowSite extends this flow model to
jointly generate a protein pocket's discrete residue types and the molecule's
binding 3D structure. We show that HarmonicFlow improves upon the
state-of-the-art generative processes for docking in simplicity, generality,
and performance. Enabled by this structure modeling, FlowSite designs binding
sites substantially better than baseline approaches and provides the first
general solution for binding site design. | [
"Hannes Stärk",
"Bowen Jing",
"Regina Barzilay",
"Tommi Jaakkola"
] | 2023-10-09 14:45:33 | http://arxiv.org/abs/2310.05764v1 | http://arxiv.org/pdf/2310.05764v1 | 2310.05764v1 |
LCOT: Linear circular optimal transport | The optimal transport problem for measures supported on non-Euclidean spaces
has recently gained ample interest in diverse applications involving
representation learning. In this paper, we focus on circular probability
measures, i.e., probability measures supported on the unit circle, and
introduce a new computationally efficient metric for these measures, denoted as
Linear Circular Optimal Transport (LCOT). The proposed metric comes with an
explicit linear embedding that allows one to apply Machine Learning (ML)
algorithms to the embedded measures and seamlessly modify the underlying metric
for the ML algorithm to LCOT. We show that the proposed metric is rooted in the
Circular Optimal Transport (COT) and can be considered the linearization of the
COT metric with respect to a fixed reference measure. We provide a theoretical
analysis of the proposed metric and derive the computational complexities for
pairwise comparison of circular probability measures. Lastly, through a set of
numerical experiments, we demonstrate the benefits of LCOT in learning
representations of circular measures. | [
"Rocio Diaz Martin",
"Ivan Medri",
"Yikun Bai",
"Xinran Liu",
"Kangbai Yan",
"Gustavo K. Rohde",
"Soheil Kolouri"
] | 2023-10-09 14:37:56 | http://arxiv.org/abs/2310.06002v1 | http://arxiv.org/pdf/2310.06002v1 | 2310.06002v1 |
Nonlinear Correct and Smooth for Semi-Supervised Learning | Graph-based semi-supervised learning (GSSL) has been used successfully in
various applications. Existing methods leverage the graph structure and labeled
samples for classification. Label Propagation (LP) and Graph Neural Networks
(GNNs) both iteratively pass messages on graphs, where LP propagates node
labels through edges and GNN aggregates node features from the neighborhood.
Recently, combining LP and GNN has led to improved performance. However,
utilizing labels and features jointly in higher-order graphs has not been
explored. Therefore, we propose Nonlinear Correct and Smooth (NLCS), which
improves the existing post-processing approach by incorporating non-linearity
and higher-order representation into the residual propagation to handle
intricate node relationships effectively. Systematic evaluations show that our
method achieves remarkable average improvements of 13.71% over base prediction
and 2.16% over the state-of-the-art post-processing method on six commonly used
datasets. Comparisons and analyses show our method effectively utilizes labels
and features jointly in higher-order graphs to resolve challenging graph
relationships. | [
"Yuanhang Shao",
"Xiuwen Liu"
] | 2023-10-09 14:33:32 | http://arxiv.org/abs/2310.05757v1 | http://arxiv.org/pdf/2310.05757v1 | 2310.05757v1 |
Deep Concept Removal | We address the problem of concept removal in deep neural networks, aiming to
learn representations that do not encode certain specified concepts (e.g.,
gender etc.) We propose a novel method based on adversarial linear classifiers
trained on a concept dataset, which helps to remove the targeted attribute
while maintaining model performance. Our approach Deep Concept Removal
incorporates adversarial probing classifiers at various layers of the network,
effectively addressing concept entanglement and improving out-of-distribution
generalization. We also introduce an implicit gradient-based technique to
tackle the challenges associated with adversarial training using linear
classifiers. We evaluate the ability to remove a concept on a set of popular
distributionally robust optimization (DRO) benchmarks with spurious
correlations, as well as out-of-distribution (OOD) generalization tasks. | [
"Yegor Klochkov",
"Jean-Francois Ton",
"Ruocheng Guo",
"Yang Liu",
"Hang Li"
] | 2023-10-09 14:31:03 | http://arxiv.org/abs/2310.05755v1 | http://arxiv.org/pdf/2310.05755v1 | 2310.05755v1 |
Unleashing the power of Neural Collapse for Transferability Estimation | Transferability estimation aims to provide heuristics for quantifying how
suitable a pre-trained model is for a specific downstream task, without
fine-tuning them all. Prior studies have revealed that well-trained models
exhibit the phenomenon of Neural Collapse. Based on a widely used neural
collapse metric in existing literature, we observe a strong correlation between
the neural collapse of pre-trained models and their corresponding fine-tuned
models. Inspired by this observation, we propose a novel method termed Fair
Collapse (FaCe) for transferability estimation by comprehensively measuring the
degree of neural collapse in the pre-trained model. Typically, FaCe comprises
two different terms: the variance collapse term, which assesses the class
separation and within-class compactness, and the class fairness term, which
quantifies the fairness of the pre-trained model towards each class. We
investigate FaCe on a variety of pre-trained classification models across
different network architectures, source datasets, and training loss functions.
Results show that FaCe yields state-of-the-art performance on different tasks
including image classification, semantic segmentation, and text classification,
which demonstrate the effectiveness and generalization of our method. | [
"Yuhe Ding",
"Bo Jiang",
"Lijun Sheng",
"Aihua Zheng",
"Jian Liang"
] | 2023-10-09 14:30:10 | http://arxiv.org/abs/2310.05754v1 | http://arxiv.org/pdf/2310.05754v1 | 2310.05754v1 |
Estimating Shape Distances on Neural Representations with Limited Samples | Measuring geometric similarity between high-dimensional network
representations is a topic of longstanding interest to neuroscience and deep
learning. Although many methods have been proposed, only a few works have
rigorously analyzed their statistical efficiency or quantified estimator
uncertainty in data-limited regimes. Here, we derive upper and lower bounds on
the worst-case convergence of standard estimators of shape
distance$\unicode{x2014}$a measure of representational dissimilarity proposed
by Williams et al. (2021). These bounds reveal the challenging nature of the
problem in high-dimensional feature spaces. To overcome these challenges, we
introduce a new method-of-moments estimator with a tunable bias-variance
tradeoff. We show that this estimator achieves superior performance to standard
estimators in simulation and on neural data, particularly in high-dimensional
settings. Thus, we lay the foundation for a rigorous statistical theory for
high-dimensional shape analysis, and we contribute a new estimation method that
is well-suited to practical scientific settings. | [
"Dean A. Pospisil",
"Brett W. Larsen",
"Sarah E. Harvey",
"Alex H. Williams"
] | 2023-10-09 14:16:34 | http://arxiv.org/abs/2310.05742v1 | http://arxiv.org/pdf/2310.05742v1 | 2310.05742v1 |
LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models | Large language models (LLMs) have been applied in various applications due to
their astonishing capabilities. With advancements in technologies such as
chain-of-thought (CoT) prompting and in-context learning (ICL), the prompts fed
to LLMs are becoming increasingly lengthy, even exceeding tens of thousands of
tokens. To accelerate model inference and reduce cost, this paper presents
LLMLingua, a coarse-to-fine prompt compression method that involves a budget
controller to maintain semantic integrity under high compression ratios, a
token-level iterative compression algorithm to better model the interdependence
between compressed contents, and an instruction tuning based method for
distribution alignment between language models. We conduct experiments and
analysis over four datasets from different scenarios, i.e., GSM8K, BBH,
ShareGPT, and Arxiv-March23; showing that the proposed approach yields
state-of-the-art performance and allows for up to 20x compression with little
performance loss. Our code is available at https://aka.ms/LLMLingua. | [
"Huiqiang Jiang",
"Qianhui Wu",
"Chin-Yew Lin",
"Yuqing Yang",
"Lili Qiu"
] | 2023-10-09 14:10:21 | http://arxiv.org/abs/2310.05736v1 | http://arxiv.org/pdf/2310.05736v1 | 2310.05736v1 |
The Program Testing Ability of Large Language Models for Code | Recent development of large language models (LLMs) for code like CodeX and
CodeT5+ demonstrates tremendous promise in achieving code intelligence. Their
ability of synthesizing code that completes a program for performing a
pre-defined task has been intensively tested and verified on benchmark datasets
including HumanEval and MBPP. Yet, evaluation of these LLMs from more
perspectives (than just program synthesis) is also anticipated, considering
their broad scope of applications in software engineering. In this paper, we
explore the ability of LLMs for testing programs/code. By performing thorough
analyses of recent LLMs for code in program testing, we show a series of
intriguing properties of these models and demonstrate how program testing
ability of LLMs can be improved. Following recent work which utilizes generated
test cases to enhance program synthesis, we further leverage our findings in
improving the quality of the synthesized programs and show +11.77% and +4.22%
higher code pass rates on HumanEval+ comparing with the GPT-3.5-turbo baseline
and the recent state-of-the-art, respectively. | [
"Weimin Xiong",
"Yiwen Guo",
"Hao Chen"
] | 2023-10-09 13:55:45 | http://arxiv.org/abs/2310.05727v1 | http://arxiv.org/pdf/2310.05727v1 | 2310.05727v1 |
Post-hoc Bias Scoring Is Optimal For Fair Classification | We consider a binary classification problem under group fairness constraints,
which can be one of Demographic Parity (DP), Equalized Opportunity (EOp), or
Equalized Odds (EO). We propose an explicit characterization of Bayes optimal
classifier under the fairness constraints, which turns out to be a simple
modification rule of the unconstrained classifier. Namely, we introduce a novel
instance-level measure of bias, which we call bias score, and the modification
rule is a simple linear rule on top of the finite amount of bias scores. Based
on this characterization, we develop a post-hoc approach that allows us to
adapt to fairness constraints while maintaining high accuracy. In the case of
DP and EOp constraints, the modification rule is thresholding a single bias
score, while in the case of EO constraints we are required to fit a linear
modification rule with 2 parameters. The method can also be applied for
composite group-fairness criteria, such as ones involving several sensitive
attributes. We achieve competitive or better performance compared to both
in-processing and post-processing methods across three datasets: Adult, COMPAS,
and CelebA. Unlike most post-processing methods, we do not require access to
sensitive attributes during the inference time. | [
"Wenlong Chen",
"Yegor Klochkov",
"Yang Liu"
] | 2023-10-09 13:54:08 | http://arxiv.org/abs/2310.05725v1 | http://arxiv.org/pdf/2310.05725v1 | 2310.05725v1 |
Planning to Go Out-of-Distribution in Offline-to-Online Reinforcement Learning | Offline pretraining with a static dataset followed by online fine-tuning
(offline-to-online, or OtO) is a paradigm that is well matched to a real-world
RL deployment process: in few real settings would one deploy an offline policy
with no test runs and tuning. In this scenario, we aim to find the
best-performing policy within a limited budget of online interactions. Previous
work in the OtO setting has focused on correcting for bias introduced by the
policy-constraint mechanisms of offline RL algorithms. Such constraints keep
the learned policy close to the behavior policy that collected the dataset, but
this unnecessarily limits policy performance if the behavior policy is far from
optimal. Instead, we forgo policy constraints and frame OtO RL as an
exploration problem: we must maximize the benefit of the online
data-collection. We study major online RL exploration paradigms, adapting them
to work well with the OtO setting. These adapted methods contribute several
strong baselines. Also, we introduce an algorithm for planning to go out of
distribution (PTGOOD), which targets online exploration in relatively
high-reward regions of the state-action space unlikely to be visited by the
behavior policy. By leveraging concepts from the Conditional Entropy
Bottleneck, PTGOOD encourages data collected online to provide new information
relevant to improving the final deployment policy. In that way the limited
interaction budget is used effectively. We show that PTGOOD significantly
improves agent returns during online fine-tuning and finds the optimal policy
in as few as 10k online steps in Walker and in as few as 50k in complex control
tasks like Humanoid. Also, we find that PTGOOD avoids the suboptimal policy
convergence that many of our baselines exhibit in several environments. | [
"Trevor McInroe",
"Stefano V. Albrecht",
"Amos Storkey"
] | 2023-10-09 13:47:05 | http://arxiv.org/abs/2310.05723v1 | http://arxiv.org/pdf/2310.05723v1 | 2310.05723v1 |
Transformer Fusion with Optimal Transport | Fusion is a technique for merging multiple independently-trained neural
networks in order to combine their capabilities. Past attempts have been
restricted to the case of fully-connected, convolutional, and residual
networks. In this paper, we present a systematic approach for fusing two or
more transformer-based networks exploiting Optimal Transport to (soft-)align
the various architectural components. We flesh out an abstraction for layer
alignment, that can generalize to arbitrary architectures -- in principle --
and we apply this to the key ingredients of Transformers such as multi-head
self-attention, layer-normalization, and residual connections, and we discuss
how to handle them via various ablation studies. Furthermore, our method allows
the fusion of models of different sizes (heterogeneous fusion), providing a new
and efficient way for compression of Transformers. The proposed approach is
evaluated on both image classification tasks via Vision Transformer and natural
language modeling tasks using BERT. Our approach consistently outperforms
vanilla fusion, and, after a surprisingly short finetuning, also outperforms
the individual converged parent models. In our analysis, we uncover intriguing
insights about the significant role of soft alignment in the case of
Transformers. Our results showcase the potential of fusing multiple
Transformers, thus compounding their expertise, in the budding paradigm of
model fusion and recombination. | [
"Moritz Imfeld",
"Jacopo Graldi",
"Marco Giordano",
"Thomas Hofmann",
"Sotiris Anagnostidis",
"Sidak Pal Singh"
] | 2023-10-09 13:40:31 | http://arxiv.org/abs/2310.05719v2 | http://arxiv.org/pdf/2310.05719v2 | 2310.05719v2 |
EdVAE: Mitigating Codebook Collapse with Evidential Discrete Variational Autoencoders | Codebook collapse is a common problem in training deep generative models with
discrete representation spaces like Vector Quantized Variational Autoencoders
(VQ-VAEs). We observe that the same problem arises for the alternatively
designed discrete variational autoencoders (dVAEs) whose encoder directly
learns a distribution over the codebook embeddings to represent the data. We
hypothesize that using the softmax function to obtain a probability
distribution causes the codebook collapse by assigning overconfident
probabilities to the best matching codebook elements. In this paper, we propose
a novel way to incorporate evidential deep learning (EDL) instead of softmax to
combat the codebook collapse problem of dVAE. We evidentially monitor the
significance of attaining the probability distribution over the codebook
embeddings, in contrast to softmax usage. Our experiments using various
datasets show that our model, called EdVAE, mitigates codebook collapse while
improving the reconstruction performance, and enhances the codebook usage
compared to dVAE and VQ-VAE based models. | [
"Gulcin Baykal",
"Melih Kandemir",
"Gozde Unal"
] | 2023-10-09 13:39:26 | http://arxiv.org/abs/2310.05718v1 | http://arxiv.org/pdf/2310.05718v1 | 2310.05718v1 |
Imitator Learning: Achieve Out-of-the-Box Imitation Ability in Variable Environments | Imitation learning (IL) enables agents to mimic expert behaviors. Most
previous IL techniques focus on precisely imitating one policy through mass
demonstrations. However, in many applications, what humans require is the
ability to perform various tasks directly through a few demonstrations of
corresponding tasks, where the agent would meet many unexpected changes when
deployed. In this scenario, the agent is expected to not only imitate the
demonstration but also adapt to unforeseen environmental changes.
This motivates us to propose a new topic called imitator learning (ItorL),
which aims to derive an imitator module that can on-the-fly reconstruct the
imitation policies based on very limited expert demonstrations for different
unseen tasks, without any extra adjustment. In this work, we focus on imitator
learning based on only one expert demonstration. To solve ItorL, we propose
Demo-Attention Actor-Critic (DAAC), which integrates IL into a
reinforcement-learning paradigm that can regularize policies' behaviors in
unexpected situations. Besides, for autonomous imitation policy building, we
design a demonstration-based attention architecture for imitator policy that
can effectively output imitated actions by adaptively tracing the suitable
states in demonstrations. We develop a new navigation benchmark and a robot
environment for \topic~and show that DAAC~outperforms previous imitation
methods \textit{with large margins} both on seen and unseen tasks. | [
"Xiong-Hui Chen",
"Junyin Ye",
"Hang Zhao",
"Yi-Chen Li",
"Haoran Shi",
"Yu-Yan Xu",
"Zhihao Ye",
"Si-Hang Yang",
"Anqi Huang",
"Kai Xu",
"Zongzhang Zhang",
"Yang Yu"
] | 2023-10-09 13:35:28 | http://arxiv.org/abs/2310.05712v1 | http://arxiv.org/pdf/2310.05712v1 | 2310.05712v1 |
Guiding Language Model Reasoning with Planning Tokens | Large language models (LLMs) have recently attracted considerable interest
for their ability to perform complex reasoning tasks, such as chain-of-thought
reasoning. However, most of the existing approaches to enhance this ability
rely heavily on data-driven methods, while neglecting the structural aspects of
the model's reasoning capacity. We find that while LLMs can manage individual
reasoning steps well, they struggle with maintaining consistency across an
entire reasoning chain. To solve this, we introduce 'planning tokens' at the
start of each reasoning step, serving as a guide for the model. These token
embeddings are then fine-tuned along with the rest of the model parameters. Our
approach requires a negligible increase in trainable parameters (just 0.001%)
and can be applied through either full fine-tuning or a more
parameter-efficient scheme. We demonstrate our method's effectiveness by
applying it to three different LLMs, showing notable accuracy improvements
across three math word problem datasets w.r.t. plain chain-of-thought
fine-tuning baselines. | [
"Xinyi Wang",
"Lucas Caccia",
"Oleksiy Ostapenko",
"Xingdi Yuan",
"Alessandro Sordoni"
] | 2023-10-09 13:29:37 | http://arxiv.org/abs/2310.05707v1 | http://arxiv.org/pdf/2310.05707v1 | 2310.05707v1 |
An Attribution Method for Siamese Encoders | Despite the success of Siamese encoder models such as sentence transformers
(ST), little is known about the aspects of inputs they pay attention to. A
barrier is that their predictions cannot be attributed to individual features,
as they compare two inputs rather than processing a single one. This paper
derives a local attribution method for Siamese encoders by generalizing the
principle of integrated gradients to models with multiple inputs. The solution
takes the form of feature-pair attributions, and can be reduced to a
token-token matrix for STs. Our method involves the introduction of integrated
Jacobians and inherits the advantageous formal properties of integrated
gradients: it accounts for the model's full computation graph and is guaranteed
to converge to the actual prediction. A pilot study shows that in an ST few
token-pairs can often explain large fractions of predictions, and it focuses on
nouns and verbs. For accurate predictions, it however needs to attend to the
majority of tokens and parts of speech. | [
"Lucas Möller",
"Dmitry Nikolaev",
"Sebastian Padó"
] | 2023-10-09 13:24:44 | http://arxiv.org/abs/2310.05703v2 | http://arxiv.org/pdf/2310.05703v2 | 2310.05703v2 |
Combining recurrent and residual learning for deforestation monitoring using multitemporal SAR images | With its vast expanse, exceeding that of Western Europe by twice, the Amazon
rainforest stands as the largest forest of the Earth, holding immense
importance in global climate regulation. Yet, deforestation detection from
remote sensing data in this region poses a critical challenge, often hindered
by the persistent cloud cover that obscures optical satellite data for much of
the year. Addressing this need, this paper proposes three deep-learning models
tailored for deforestation monitoring, utilizing SAR (Synthetic Aperture Radar)
multitemporal data moved by its independence on atmospheric conditions.
Specifically, the study proposes three novel recurrent fully convolutional
network architectures-namely, RRCNN-1, RRCNN-2, and RRCNN-3, crafted to enhance
the accuracy of deforestation detection. Additionally, this research explores
replacing a bitemporal with multitemporal SAR sequences, motivated by the
hypothesis that deforestation signs quickly fade in SAR images over time. A
comprehensive assessment of the proposed approaches was conducted using a
Sentinel-1 multitemporal sequence from a sample site in the Brazilian
rainforest. The experimental analysis confirmed that analyzing a sequence of
SAR images over an observation period can reveal deforestation spots
undetectable in a pair of images. Notably, experimental results underscored the
superiority of the multitemporal approach, yielding approximately a five
percent enhancement in F1-Score across all tested network architectures.
Particularly the RRCNN-1 achieved the highest accuracy and also boasted half
the processing time of its closest counterpart. | [
"Carla Nascimento Neves",
"Raul Queiroz Feitosa",
"Mabel X. Ortega Adarme",
"Gilson Antonio Giraldi"
] | 2023-10-09 13:16:20 | http://arxiv.org/abs/2310.05697v1 | http://arxiv.org/pdf/2310.05697v1 | 2310.05697v1 |
Protecting Sensitive Data through Federated Co-Training | In many critical applications, sensitive data is inherently distributed.
Federated learning trains a model collaboratively by aggregating the parameters
of locally trained models. This avoids exposing sensitive local data. It is
possible, though, to infer upon the sensitive data from the shared model
parameters. At the same time, many types of machine learning models do not lend
themselves to parameter aggregation, such as decision trees, or rule ensembles.
It has been observed that in many applications, in particular healthcare, large
unlabeled datasets are publicly available. They can be used to exchange
information between clients by distributed distillation, i.e., co-regularizing
local training via the discrepancy between the soft predictions of each local
client on the unlabeled dataset. This, however, still discloses private
information and restricts the types of models to those trainable via
gradient-based methods. We propose to go one step further and use a form of
federated co-training, where local hard labels on the public unlabeled datasets
are shared and aggregated into a consensus label. This consensus label can be
used for local training by any supervised machine learning model. We show that
this federated co-training approach achieves a model quality comparable to both
federated learning and distributed distillation on a set of benchmark datasets
and real-world medical datasets. It improves privacy over both approaches,
protecting against common membership inference attacks to the highest degree.
Furthermore, we show that federated co-training can collaboratively train
interpretable models, such as decision trees and rule ensembles, achieving a
model quality comparable to centralized training. | [
"Amr Abourayya",
"Jens Kleesiek",
"Kanishka Rao",
"Erman Ayday",
"Bharat Rao",
"Geoff Webb",
"Michael Kamp"
] | 2023-10-09 13:16:10 | http://arxiv.org/abs/2310.05696v1 | http://arxiv.org/pdf/2310.05696v1 | 2310.05696v1 |
Hierarchical Reinforcement Learning for Temporal Pattern Prediction | In this work, we explore the use of hierarchical reinforcement learning (HRL)
for the task of temporal sequence prediction. Using a combination of deep
learning and HRL, we develop a stock agent to predict temporal price sequences
from historical stock price data and a vehicle agent to predict steering angles
from first person, dash cam images. Our results in both domains indicate that a
type of HRL, called feudal reinforcement learning, provides significant
improvements to training speed and stability and prediction accuracy over
standard RL. A key component to this success is the multi-resolution structure
that introduces both temporal and spatial abstraction into the network
hierarchy. | [
"Faith Johnson",
"Kristin Dana"
] | 2023-10-09 13:15:57 | http://arxiv.org/abs/2310.05695v1 | http://arxiv.org/pdf/2310.05695v1 | 2310.05695v1 |
Analysis of Rainfall Variability and Water Extent of Selected Hydropower Reservoir Using Google Earth Engine (GEE): A Case Study from Two Tropical Countries, Sri Lanka and Vietnam | This study presents a comprehensive remote sensing analysis of rainfall
patterns and selected hydropower reservoir water extent in two tropical monsoon
countries, Vietnam and Sri Lanka. The aim is to understand the relationship
between remotely sensed rainfall data and the dynamic changes (monthly) in
reservoir water extent. The analysis utilizes high-resolution optical imagery
and Sentinel-1 Synthetic Aperture Radar (SAR) data to observe and monitor water
bodies during different weather conditions, especially during the monsoon
season. The average annual rainfall for both countries is determined, and
spatiotemporal variations in monthly average rainfall are examined at regional
and reservoir basin levels using the Climate Hazards Group InfraRed
Precipitation with Station (CHIRPS) dataset from 1981 to 2022. Water extents
are derived for selected reservoirs using Sentinel-1 SAR Ground Range Detected
(GRD) images in Vietnam and Sri Lanka from 2017 to 2022. The images are
pre-processed and corrected using terrain correction and refined Lee filter. An
automated thresholding algorithm, OTSU, distinguishes water and land, taking
advantage of both VV and VH polarization data. The connected pixel count
threshold is applied to enhance result accuracy. The results indicate a clear
relationship between rainfall patterns and reservoir water extent, with
increased precipitation during the monsoon season leading to higher water
extents in the later months. This study contributes to understanding how
rainfall variability impacts reservoir water resources in tropical monsoon
regions. The preliminary findings can inform water resource management
strategies and support these countries' decision-making processes related to
hydropower generation, flood management, and irrigation. | [
"Punsisi Rajakaruna",
"Surajit Ghosh",
"Bunyod Holmatov"
] | 2023-10-09 12:51:46 | http://arxiv.org/abs/2310.05682v2 | http://arxiv.org/pdf/2310.05682v2 | 2310.05682v2 |
Making Scalable Meta Learning Practical | Despite its flexibility to learn diverse inductive biases in machine learning
programs, meta learning (i.e., learning to learn) has long been recognized to
suffer from poor scalability due to its tremendous compute/memory costs,
training instability, and a lack of efficient distributed training support. In
this work, we focus on making scalable meta learning practical by introducing
SAMA, which combines advances in both implicit differentiation algorithms and
systems. Specifically, SAMA is designed to flexibly support a broad range of
adaptive optimizers in the base level of meta learning programs, while reducing
computational burden by avoiding explicit computation of second-order gradient
information, and exploiting efficient distributed training techniques
implemented for first-order gradients. Evaluated on multiple large-scale meta
learning benchmarks, SAMA showcases up to 1.7/4.8x increase in throughput and
2.0/3.8x decrease in memory consumption respectively on single-/multi-GPU
setups compared to other baseline meta learning algorithms. Furthermore, we
show that SAMA-based data optimization leads to consistent improvements in text
classification accuracy with BERT and RoBERTa large language models, and
achieves state-of-the-art results in both small- and large-scale data pruning
on image classification tasks, demonstrating the practical applicability of
scalable meta learning across language and vision domains. | [
"Sang Keun Choe",
"Sanket Vaibhav Mehta",
"Hwijeen Ahn",
"Willie Neiswanger",
"Pengtao Xie",
"Emma Strubell",
"Eric Xing"
] | 2023-10-09 12:45:13 | http://arxiv.org/abs/2310.05674v2 | http://arxiv.org/pdf/2310.05674v2 | 2310.05674v2 |
Multi-timestep models for Model-based Reinforcement Learning | In model-based reinforcement learning (MBRL), most algorithms rely on
simulating trajectories from one-step dynamics models learned on data. A
critical challenge of this approach is the compounding of one-step prediction
errors as length of the trajectory grows. In this paper we tackle this issue by
using a multi-timestep objective to train one-step models. Our objective is a
weighted sum of a loss function (e.g., negative log-likelihood) at various
future horizons. We explore and test a range of weights profiles. We find that
exponentially decaying weights lead to models that significantly improve the
long-horizon R2 score. This improvement is particularly noticeable when the
models were evaluated on noisy data. Finally, using a soft actor-critic (SAC)
agent in pure batch reinforcement learning (RL) and iterated batch RL
scenarios, we found that our multi-timestep models outperform or match standard
one-step models. This was especially evident in a noisy variant of the
considered environment, highlighting the potential of our approach in
real-world applications. | [
"Abdelhakim Benechehab",
"Giuseppe Paolo",
"Albert Thomas",
"Maurizio Filippone",
"Balázs Kégl"
] | 2023-10-09 12:42:39 | http://arxiv.org/abs/2310.05672v2 | http://arxiv.org/pdf/2310.05672v2 | 2310.05672v2 |
LARA: A Light and Anti-overfitting Retraining Approach for Unsupervised Anomaly Detection | Most of current anomaly detection models assume that the normal pattern
remains same all the time. However, the normal patterns of Web services change
dramatically and frequently. The model trained on old-distribution data is
outdated after such changes. Retraining the whole model every time is
expensive. Besides, at the beginning of normal pattern changes, there is not
enough observation data from the new distribution. Retraining a large neural
network model with limited data is vulnerable to overfitting. Thus, we propose
a Light and Anti-overfitting Retraining Approach (LARA) for deep variational
auto-encoder based time series anomaly detection methods (VAEs). This work aims
to make three novel contributions: 1) the retraining process is formulated as a
convex problem and can converge at a fast rate as well as prevent overfitting;
2) designing a ruminate block, which leverages the historical data without the
need to store them; 3) mathematically proving that when fine-tuning the latent
vector and reconstructed data, the linear formations can achieve the least
adjusting errors between the ground truths and the fine-tuned ones.
Moreover, we have performed many experiments to verify that retraining LARA
with even 43 time slots of data from new distribution can result in its
competitive F1 Score in comparison with the state-of-the-art anomaly detection
models trained with sufficient data. Besides, we verify its light overhead. | [
"Feiyi Chen",
"Zhen Qing",
"Yingying Zhang",
"Shuiguang Deng",
"Yi Xiao",
"Guansong Pang",
"Qingsong Wen"
] | 2023-10-09 12:36:16 | http://arxiv.org/abs/2310.05668v1 | http://arxiv.org/pdf/2310.05668v1 | 2310.05668v1 |
Causal structure learning with momentum: Sampling distributions over Markov Equivalence Classes of DAGs | In the context of inferring a Bayesian network structure (directed acyclic
graph, DAG for short), we devise a non-reversible continuous time Markov chain,
the "Causal Zig-Zag sampler", that targets a probability distribution over
classes of observationally equivalent (Markov equivalent) DAGs. The classes are
represented as completed partially directed acyclic graphs (CPDAGs). The
non-reversible Markov chain relies on the operators used in Chickering's Greedy
Equivalence Search (GES) and is endowed with a momentum variable, which
improves mixing significantly as we show empirically. The possible target
distributions include posterior distributions based on a prior over DAGs and a
Markov equivalent likelihood. We offer an efficient implementation wherein we
develop new algorithms for listing, counting, uniformly sampling, and applying
possible moves of the GES operators, all of which significantly improve upon
the state-of-the-art. | [
"Moritz Schauer",
"Marcel Wienöbst"
] | 2023-10-09 12:10:51 | http://arxiv.org/abs/2310.05655v1 | http://arxiv.org/pdf/2310.05655v1 | 2310.05655v1 |
FENCE: Fairplay Ensuring Network Chain Entity for Real-Time Multiple ID Detection at Scale In Fantasy Sports | Dream11 takes pride in being a unique platform that enables over 190 million
fantasy sports users to demonstrate their skills and connect deeper with their
favorite sports. While managing such a scale, one issue we are faced with is
duplicate/multiple account creation in the system. This is done by some users
with the intent of abusing the platform, typically for bonus offers. The
challenge is to detect these multiple accounts before it is too late. We
propose a graph-based solution to solve this problem in which we first predict
edges/associations between users. Using the edge information we highlight
clusters of colluding multiple accounts. In this paper, we talk about our
distributed ML system which is deployed to serve and support the inferences
from our detection models. The challenge is to do this in real-time in order to
take corrective actions. A core part of this setup also involves
human-in-the-loop components for validation, feedback, and ground-truth
labeling. | [
"Akriti Upreti",
"Kartavya Kothari",
"Utkarsh Thukral",
"Vishal Verma"
] | 2023-10-09 12:04:50 | http://arxiv.org/abs/2310.05651v1 | http://arxiv.org/pdf/2310.05651v1 | 2310.05651v1 |
Diagnosing Catastrophe: Large parts of accuracy loss in continual learning can be accounted for by readout misalignment | Unlike primates, training artificial neural networks on changing data
distributions leads to a rapid decrease in performance on old tasks. This
phenomenon is commonly referred to as catastrophic forgetting. In this paper,
we investigate the representational changes that underlie this performance
decrease and identify three distinct processes that together account for the
phenomenon. The largest component is a misalignment between hidden
representations and readout layers. Misalignment occurs due to learning on
additional tasks and causes internal representations to shift. Representational
geometry is partially conserved under this misalignment and only a small part
of the information is irrecoverably lost. All types of representational changes
scale with the dimensionality of hidden representations. These insights have
implications for deep learning applications that need to be continuously
updated, but may also aid aligning ANN models to the rather robust biological
vision. | [
"Daniel Anthes",
"Sushrut Thorat",
"Peter König",
"Tim C. Kietzmann"
] | 2023-10-09 11:57:46 | http://arxiv.org/abs/2310.05644v1 | http://arxiv.org/pdf/2310.05644v1 | 2310.05644v1 |
Binary Classification with Confidence Difference | Recently, learning with soft labels has been shown to achieve better
performance than learning with hard labels in terms of model generalization,
calibration, and robustness. However, collecting pointwise labeling confidence
for all training examples can be challenging and time-consuming in real-world
scenarios. This paper delves into a novel weakly supervised binary
classification problem called confidence-difference (ConfDiff) classification.
Instead of pointwise labeling confidence, we are given only unlabeled data
pairs with confidence difference that specifies the difference in the
probabilities of being positive. We propose a risk-consistent approach to
tackle this problem and show that the estimation error bound achieves the
optimal convergence rate. We also introduce a risk correction approach to
mitigate overfitting problems, whose consistency and convergence rate are also
proven. Extensive experiments on benchmark data sets and a real-world
recommender system data set validate the effectiveness of our proposed
approaches in exploiting the supervision information of the confidence
difference. | [
"Wei Wang",
"Lei Feng",
"Yuchen Jiang",
"Gang Niu",
"Min-Ling Zhang",
"Masashi Sugiyama"
] | 2023-10-09 11:44:50 | http://arxiv.org/abs/2310.05632v1 | http://arxiv.org/pdf/2310.05632v1 | 2310.05632v1 |
Integrating Stock Features and Global Information via Large Language Models for Enhanced Stock Return Prediction | The remarkable achievements and rapid advancements of Large Language Models
(LLMs) such as ChatGPT and GPT-4 have showcased their immense potential in
quantitative investment. Traders can effectively leverage these LLMs to analyze
financial news and predict stock returns accurately. However, integrating LLMs
into existing quantitative models presents two primary challenges: the
insufficient utilization of semantic information embedded within LLMs and the
difficulties in aligning the latent information within LLMs with pre-existing
quantitative stock features. We propose a novel framework consisting of two
components to surmount these challenges. The first component, the Local-Global
(LG) model, introduces three distinct strategies for modeling global
information. These approaches are grounded respectively on stock features, the
capabilities of LLMs, and a hybrid method combining the two paradigms. The
second component, Self-Correlated Reinforcement Learning (SCRL), focuses on
aligning the embeddings of financial news generated by LLMs with stock features
within the same semantic space. By implementing our framework, we have
demonstrated superior performance in Rank Information Coefficient and returns,
particularly compared to models relying only on stock features in the China
A-share market. | [
"Yujie Ding",
"Shuai Jia",
"Tianyi Ma",
"Bingcheng Mao",
"Xiuze Zhou",
"Liuliu Li",
"Dongming Han"
] | 2023-10-09 11:34:18 | http://arxiv.org/abs/2310.05627v1 | http://arxiv.org/pdf/2310.05627v1 | 2310.05627v1 |
Locality-Aware Generalizable Implicit Neural Representation | Generalizable implicit neural representation (INR) enables a single
continuous function, i.e., a coordinate-based neural network, to represent
multiple data instances by modulating its weights or intermediate features
using latent codes. However, the expressive power of the state-of-the-art
modulation is limited due to its inability to localize and capture fine-grained
details of data entities such as specific pixels and rays. To address this
issue, we propose a novel framework for generalizable INR that combines a
transformer encoder with a locality-aware INR decoder. The transformer encoder
predicts a set of latent tokens from a data instance to encode local
information into each latent token. The locality-aware INR decoder extracts a
modulation vector by selectively aggregating the latent tokens via
cross-attention for a coordinate input and then predicts the output by
progressively decoding with coarse-to-fine modulation through multiple
frequency bandwidths. The selective token aggregation and the multi-band
feature modulation enable us to learn locality-aware representation in spatial
and spectral aspects, respectively. Our framework significantly outperforms
previous generalizable INRs and validates the usefulness of the locality-aware
latents for downstream tasks such as image generation. | [
"Doyup Lee",
"Chiheon Kim",
"Minsu Cho",
"Wook-Shin Han"
] | 2023-10-09 11:26:58 | http://arxiv.org/abs/2310.05624v2 | http://arxiv.org/pdf/2310.05624v2 | 2310.05624v2 |
Domain Watermark: Effective and Harmless Dataset Copyright Protection is Closed at Hand | The prosperity of deep neural networks (DNNs) is largely benefited from
open-source datasets, based on which users can evaluate and improve their
methods. In this paper, we revisit backdoor-based dataset ownership
verification (DOV), which is currently the only feasible approach to protect
the copyright of open-source datasets. We reveal that these methods are
fundamentally harmful given that they could introduce malicious
misclassification behaviors to watermarked DNNs by the adversaries. In this
paper, we design DOV from another perspective by making watermarked models
(trained on the protected dataset) correctly classify some `hard' samples that
will be misclassified by the benign model. Our method is inspired by the
generalization property of DNNs, where we find a \emph{hardly-generalized
domain} for the original dataset (as its \emph{domain watermark}). It can be
easily learned with the protected dataset containing modified samples.
Specifically, we formulate the domain generation as a bi-level optimization and
propose to optimize a set of visually-indistinguishable clean-label modified
data with similar effects to domain-watermarked samples from the
hardly-generalized domain to ensure watermark stealthiness. We also design a
hypothesis-test-guided ownership verification via our domain watermark and
provide the theoretical analyses of our method. Extensive experiments on three
benchmark datasets are conducted, which verify the effectiveness of our method
and its resistance to potential adaptive methods. The code for reproducing main
experiments is available at
\url{https://github.com/JunfengGo/Domain-Watermark}. | [
"Junfeng Guo",
"Yiming Li",
"Lixu Wang",
"Shu-Tao Xia",
"Heng Huang",
"Cong Liu",
"Bo Li"
] | 2023-10-09 11:23:05 | http://arxiv.org/abs/2310.14942v1 | http://arxiv.org/pdf/2310.14942v1 | 2310.14942v1 |
Adaptive Multi-head Contrastive Learning | In contrastive learning, two views of an original image generated by
different augmentations are considered as a positive pair whose similarity is
required to be high. Moreover, two views of two different images are considered
as a negative pair, and their similarity is encouraged to be low. Normally, a
single similarity measure given by a single projection head is used to evaluate
positive and negative sample pairs, respectively. However, due to the various
augmentation strategies and varying intra-sample similarity, augmented views
from the same image are often not similar. Moreover, due to inter-sample
similarity, augmented views of two different images may be more similar than
augmented views from the same image. As such, enforcing a high similarity for
positive pairs and a low similarity for negative pairs may not always be
achievable, and in the case of some pairs, forcing so may be detrimental to the
performance. To address this issue, we propose to use multiple projection
heads, each producing a separate set of features. Our loss function for
pre-training emerges from a solution to the maximum likelihood estimation over
head-wise posterior distributions of positive samples given observations. The
loss contains the similarity measure over positive and negative pairs, each
re-weighted by an individual adaptive temperature that is regularized to
prevent ill solutions. Our adaptive multi-head contrastive learning (AMCL) can
be applied to and experimentally improves several popular contrastive learning
methods such as SimCLR, MoCo and Barlow Twins. Such improvement is consistent
under various backbones and linear probing epoches and is more significant when
multiple augmentation methods are used. | [
"Lei Wang",
"Piotr Koniusz",
"Tom Gedeon",
"Liang Zheng"
] | 2023-10-09 11:08:34 | http://arxiv.org/abs/2310.05615v1 | http://arxiv.org/pdf/2310.05615v1 | 2310.05615v1 |
Cost-sensitive probabilistic predictions for support vector machines | Support vector machines (SVMs) are widely used and constitute one of the best
examined and used machine learning models for two-class classification.
Classification in SVM is based on a score procedure, yielding a deterministic
classification rule, which can be transformed into a probabilistic rule (as
implemented in off-the-shelf SVM libraries), but is not probabilistic in
nature. On the other hand, the tuning of the regularization parameters in SVM
is known to imply a high computational effort and generates pieces of
information that are not fully exploited, not being used to build a
probabilistic classification rule. In this paper we propose a novel approach to
generate probabilistic outputs for the SVM. The new method has the following
three properties. First, it is designed to be cost-sensitive, and thus the
different importance of sensitivity (or true positive rate, TPR) and
specificity (true negative rate, TNR) is readily accommodated in the model. As
a result, the model can deal with imbalanced datasets which are common in
operational business problems as churn prediction or credit scoring. Second,
the SVM is embedded in an ensemble method to improve its performance, making
use of the valuable information generated in the parameters tuning process.
Finally, the probabilities estimation is done via bootstrap estimates, avoiding
the use of parametric models as competing approaches. Numerical tests on a wide
range of datasets show the advantages of our approach over benchmark
procedures. | [
"Sandra Benítez-Peña",
"Rafael Blanquero",
"Emilio Carrizosa",
"Pepa Ramírez-Cobo"
] | 2023-10-09 11:00:17 | http://arxiv.org/abs/2310.05997v1 | http://arxiv.org/pdf/2310.05997v1 | 2310.05997v1 |
On Prediction-Modelers and Decision-Makers: Why Fairness Requires More Than a Fair Prediction Model | An implicit ambiguity in the field of prediction-based decision-making
regards the relation between the concepts of prediction and decision. Much of
the literature in the field tends to blur the boundaries between the two
concepts and often simply speaks of 'fair prediction.' In this paper, we point
out that a differentiation of these concepts is helpful when implementing
algorithmic fairness. Even if fairness properties are related to the features
of the used prediction model, what is more properly called 'fair' or 'unfair'
is a decision system, not a prediction model. This is because fairness is about
the consequences on human lives, created by a decision, not by a prediction. We
clarify the distinction between the concepts of prediction and decision and
show the different ways in which these two elements influence the final
fairness properties of a prediction-based decision system. In addition to
exploring this relationship conceptually and practically, we propose a
framework that enables a better understanding and reasoning of the conceptual
logic of creating fairness in prediction-based decision-making. In our
framework, we specify different roles, namely the 'prediction-modeler' and the
'decision-maker,' and the information required from each of them for being able
to implement fairness of the system. Our framework allows for deriving distinct
responsibilities for both roles and discussing some insights related to ethical
and legal requirements. Our contribution is twofold. First, we shift the focus
from abstract algorithmic fairness to context-dependent decision-making,
recognizing diverse actors with unique objectives and independent actions.
Second, we provide a conceptual framework that can help structure
prediction-based decision problems with respect to fairness issues, identify
responsibilities, and implement fairness governance mechanisms in real-world
scenarios. | [
"Teresa Scantamburlo",
"Joachim Baumann",
"Christoph Heitz"
] | 2023-10-09 10:34:42 | http://arxiv.org/abs/2310.05598v1 | http://arxiv.org/pdf/2310.05598v1 | 2310.05598v1 |
ODEFormer: Symbolic Regression of Dynamical Systems with Transformers | We introduce ODEFormer, the first transformer able to infer multidimensional
ordinary differential equation (ODE) systems in symbolic form from the
observation of a single solution trajectory. We perform extensive evaluations
on two datasets: (i) the existing "Strogatz" dataset featuring two-dimensional
systems; (ii) ODEBench, a collection of one- to four-dimensional systems that
we carefully curated from the literature to provide a more holistic benchmark.
ODEFormer consistently outperforms existing methods while displaying
substantially improved robustness to noisy and irregularly sampled
observations, as well as faster inference. We release our code, model and
benchmark dataset publicly. | [
"Stéphane d'Ascoli",
"Sören Becker",
"Alexander Mathis",
"Philippe Schwaller",
"Niki Kilbertus"
] | 2023-10-09 09:54:12 | http://arxiv.org/abs/2310.05573v1 | http://arxiv.org/pdf/2310.05573v1 | 2310.05573v1 |
A Simple and Robust Framework for Cross-Modality Medical Image Segmentation applied to Vision Transformers | When it comes to clinical images, automatic segmentation has a wide variety
of applications and a considerable diversity of input domains, such as
different types of Magnetic Resonance Images (MRIs) and Computerized Tomography
(CT) scans. This heterogeneity is a challenge for cross-modality algorithms
that should equally perform independently of the input image type fed to them.
Often, segmentation models are trained using a single modality, preventing
generalization to other types of input data without resorting to transfer
learning techniques. Furthermore, the multi-modal or cross-modality
architectures proposed in the literature frequently require registered images,
which are not easy to collect in clinical environments, or need additional
processing steps, such as synthetic image generation. In this work, we propose
a simple framework to achieve fair image segmentation of multiple modalities
using a single conditional model that adapts its normalization layers based on
the input type, trained with non-registered interleaved mixed data. We show
that our framework outperforms other cross-modality segmentation methods, when
applied to the same 3D UNet baseline model, on the Multi-Modality Whole Heart
Segmentation Challenge. Furthermore, we define the Conditional Vision
Transformer (C-ViT) encoder, based on the proposed cross-modality framework,
and we show that it brings significant improvements to the resulting
segmentation, up to 6.87\% of Dice accuracy, with respect to its baseline
reference. The code to reproduce our experiments and the trained model weights
are available at https://github.com/matteo-bastico/MI-Seg. | [
"Matteo Bastico",
"David Ryckelynck",
"Laurent Corté",
"Yannick Tillier",
"Etienne Decencière"
] | 2023-10-09 09:51:44 | http://arxiv.org/abs/2310.05572v1 | http://arxiv.org/pdf/2310.05572v1 | 2310.05572v1 |
Aggregated f-average Neural Network for Interpretable Ensembling | Ensemble learning leverages multiple models (i.e., weak learners) on a common
machine learning task to enhance prediction performance. Basic ensembling
approaches average the weak learners outputs, while more sophisticated ones
stack a machine learning model in between the weak learners outputs and the
final prediction. This work fuses both aforementioned frameworks. We introduce
an aggregated f-average (AFA) shallow neural network which models and combines
different types of averages to perform an optimal aggregation of the weak
learners predictions. We emphasise its interpretable architecture and simple
training strategy, and illustrate its good performance on the problem of
few-shot class incremental learning. | [
"Mathieu Vu",
"Emilie Chouzenoux",
"Jean-Christophe Pesquet",
"Ismail Ben Ayed"
] | 2023-10-09 09:43:08 | http://arxiv.org/abs/2310.05566v1 | http://arxiv.org/pdf/2310.05566v1 | 2310.05566v1 |
A New Transformation Approach for Uplift Modeling with Binary Outcome | Uplift modeling has been used effectively in fields such as marketing and
customer retention, to target those customers who are more likely to respond
due to the campaign or treatment. Essentially, it is a machine learning
technique that predicts the gain from performing some action with respect to
not taking it. A popular class of uplift models is the transformation approach
that redefines the target variable with the original treatment indicator. These
transformation approaches only need to train and predict the difference in
outcomes directly. The main drawback of these approaches is that in general it
does not use the information in the treatment indicator beyond the construction
of the transformed outcome and usually is not efficient. In this paper, we
design a novel transformed outcome for the case of the binary target variable
and unlock the full value of the samples with zero outcome. From a practical
perspective, our new approach is flexible and easy to use. Experimental results
on synthetic and real-world datasets obviously show that our new approach
outperforms the traditional one. At present, our new approach has already been
applied to precision marketing in a China nation-wide financial holdings group. | [
"Kun Li",
"Jiang Tian",
"Xiaojia Xiang"
] | 2023-10-09 09:17:52 | http://arxiv.org/abs/2310.05549v1 | http://arxiv.org/pdf/2310.05549v1 | 2310.05549v1 |
M3FPolypSegNet: Segmentation Network with Multi-frequency Feature Fusion for Polyp Localization in Colonoscopy Images | Polyp segmentation is crucial for preventing colorectal cancer a common type
of cancer. Deep learning has been used to segment polyps automatically, which
reduces the risk of misdiagnosis. Localizing small polyps in colonoscopy images
is challenging because of its complex characteristics, such as color,
occlusion, and various shapes of polyps. To address this challenge, a novel
frequency-based fully convolutional neural network, Multi-Frequency Feature
Fusion Polyp Segmentation Network (M3FPolypSegNet) was proposed to decompose
the input image into low/high/full-frequency components to use the
characteristics of each component. We used three independent multi-frequency
encoders to map multiple input images into a high-dimensional feature space. In
the Frequency-ASPP Scalable Attention Module (F-ASPP SAM), ASPP was applied
between each frequency component to preserve scale information. Subsequently,
scalable attention was applied to emphasize polyp regions in a high-dimensional
feature space. Finally, we designed three multi-task learning (i.e., region,
edge, and distance) in four decoder blocks to learn the structural
characteristics of the region. The proposed model outperformed various
segmentation models with performance gains of 6.92% and 7.52% on average for
all metrics on CVC-ClinicDB and BKAI-IGH-NeoPolyp, respectively. | [
"Ju-Hyeon Nam",
"Seo-Hyeong Park",
"Nur Suriza Syazwany",
"Yerim Jung",
"Yu-Han Im",
"Sang-Chul Lee"
] | 2023-10-09 09:01:53 | http://arxiv.org/abs/2310.05538v2 | http://arxiv.org/pdf/2310.05538v2 | 2310.05538v2 |
ParFam -- Symbolic Regression Based on Continuous Global Optimization | The problem of symbolic regression (SR) arises in many different
applications, such as identifying physical laws or deriving mathematical
equations describing the behavior of financial markets from given data. Various
methods exist to address the problem of SR, often based on genetic programming.
However, these methods are usually quite complicated and require a lot of
hyperparameter tuning and computational resources. In this paper, we present
our new method ParFam that utilizes parametric families of suitable symbolic
functions to translate the discrete symbolic regression problem into a
continuous one, resulting in a more straightforward setup compared to current
state-of-the-art methods. In combination with a powerful global optimizer, this
approach results in an effective method to tackle the problem of SR.
Furthermore, it can be easily extended to more advanced algorithms, e.g., by
adding a deep neural network to find good-fitting parametric families. We prove
the performance of ParFam with extensive numerical experiments based on the
common SR benchmark suit SRBench, showing that we achieve state-of-the-art
results. Our code and results can be found at
https://github.com/Philipp238/parfam . | [
"Philipp Scholl",
"Katharina Bieker",
"Hillary Hauger",
"Gitta Kutyniok"
] | 2023-10-09 09:01:25 | http://arxiv.org/abs/2310.05537v2 | http://arxiv.org/pdf/2310.05537v2 | 2310.05537v2 |
NetTiSA: Extended IP Flow with Time-series Features for Universal Bandwidth-constrained High-speed Network Traffic Classification | Network traffic monitoring based on IP Flows is a standard monitoring
approach that can be deployed to various network infrastructures, even the
large IPS-based networks connecting millions of people. Since flow records
traditionally contain only limited information (addresses, transport ports, and
amount of exchanged data), they are also commonly extended for additional
features that enable network traffic analysis with high accuracy. Nevertheless,
the flow extensions are often too large or hard to compute, which limits their
deployment only to smaller-sized networks. This paper proposes a novel extended
IP flow called NetTiSA (Network Time Series Analysed), which is based on the
analysis of the time series of packet sizes. By thoroughly testing 25 different
network classification tasks, we show the broad applicability and high
usability of NetTiSA, which often outperforms the best-performing related
works. For practical deployment, we also consider the sizes of flows extended
for NetTiSA and evaluate the performance impacts of its computation in the flow
exporter. The novel feature set proved universal and deployable to high-speed
ISP networks with 100\,Gbps lines; thus, it enables accurate and widespread
network security protection. | [
"Josef Koumar",
"Karel Hynek",
"Jaroslav Pešek",
"Tomáš Čejka"
] | 2023-10-09 08:51:00 | http://arxiv.org/abs/2310.05530v1 | http://arxiv.org/pdf/2310.05530v1 | 2310.05530v1 |
A novel Network Science Algorithm for Improving Triage of Patients | Patient triage plays a crucial role in healthcare, ensuring timely and
appropriate care based on the urgency of patient conditions. Traditional triage
methods heavily rely on human judgment, which can be subjective and prone to
errors. Recently, a growing interest has been in leveraging artificial
intelligence (AI) to develop algorithms for triaging patients. This paper
presents the development of a novel algorithm for triaging patients. It is
based on the analysis of patient data to produce decisions regarding their
prioritization. The algorithm was trained on a comprehensive data set
containing relevant patient information, such as vital signs, symptoms, and
medical history. The algorithm was designed to accurately classify patients
into triage categories through rigorous preprocessing and feature engineering.
Experimental results demonstrate that our algorithm achieved high accuracy and
performance, outperforming traditional triage methods. By incorporating
computer science into the triage process, healthcare professionals can benefit
from improved efficiency, accuracy, and consistency, prioritizing patients
effectively and optimizing resource allocation. Although further research is
needed to address challenges such as biases in training data and model
interpretability, the development of AI-based algorithms for triaging patients
shows great promise in enhancing healthcare delivery and patient outcomes. | [
"Pietro Hiram Guzzi",
"Annamaria De Filippo",
"Pierangelo Veltri"
] | 2023-10-09 08:47:12 | http://arxiv.org/abs/2310.05996v1 | http://arxiv.org/pdf/2310.05996v1 | 2310.05996v1 |
Projecting infinite time series graphs to finite marginal graphs using number theory | In recent years, a growing number of method and application works have
adapted and applied the causal-graphical-model framework to time series data.
Many of these works employ time-resolved causal graphs that extend infinitely
into the past and future and whose edges are repetitive in time, thereby
reflecting the assumption of stationary causal relationships. However, most
results and algorithms from the causal-graphical-model framework are not
designed for infinite graphs. In this work, we develop a method for projecting
infinite time series graphs with repetitive edges to marginal graphical models
on a finite time window. These finite marginal graphs provide the answers to
$m$-separation queries with respect to the infinite graph, a task that was
previously unresolved. Moreover, we argue that these marginal graphs are useful
for causal discovery and causal effect estimation in time series, effectively
enabling to apply results developed for finite graphs to the infinite graphs.
The projection procedure relies on finding common ancestors in the
to-be-projected graph and is, by itself, not new. However, the projection
procedure has not yet been algorithmically implemented for time series graphs
since in these infinite graphs there can be infinite sets of paths that might
give rise to common ancestors. We solve the search over these possibly infinite
sets of paths by an intriguing combination of path-finding techniques for
finite directed graphs and solution theory for linear Diophantine equations. By
providing an algorithm that carries out the projection, our paper makes an
important step towards a theoretically-grounded and method-agnostic
generalization of a range of causal inference methods and results to time
series. | [
"Andreas Gerhardus",
"Jonas Wahl",
"Sofia Faltenbacher",
"Urmi Ninad",
"Jakob Runge"
] | 2023-10-09 08:45:06 | http://arxiv.org/abs/2310.05526v1 | http://arxiv.org/pdf/2310.05526v1 | 2310.05526v1 |
On Double Descent in Reinforcement Learning with LSTD and Random Features | Temporal Difference (TD) algorithms are widely used in Deep Reinforcement
Learning (RL). Their performance is heavily influenced by the size of the
neural network. While in supervised learning, the regime of
over-parameterization and its benefits are well understood, the situation in RL
is much less clear. In this paper, we present a theoretical analysis of the
influence of network size and $l_2$-regularization on performance. We identify
the ratio between the number of parameters and the number of visited states as
a crucial factor and define over-parameterization as the regime when it is
larger than one. Furthermore, we observe a double descent phenomenon, i.e., a
sudden drop in performance around the parameter/state ratio of one. Leveraging
random features and the lazy training regime, we study the regularized
Least-Square Temporal Difference (LSTD) algorithm in an asymptotic regime, as
both the number of parameters and states go to infinity, maintaining a constant
ratio. We derive deterministic limits of both the empirical and the true
Mean-Square Bellman Error (MSBE) that feature correction terms responsible for
the double-descent. Correction terms vanish when the $l_2$-regularization is
increased or the number of unvisited states goes to zero. Numerical experiments
with synthetic and small real-world environments closely match the theoretical
predictions. | [
"David Brellmann",
"Eloïse Berthier",
"David Filliat",
"Goran Frehse"
] | 2023-10-09 08:33:22 | http://arxiv.org/abs/2310.05518v2 | http://arxiv.org/pdf/2310.05518v2 | 2310.05518v2 |
WeatherGNN: Exploiting Complicated Relationships in Numerical Weather Prediction Bias Correction | Numerical weather prediction (NWP) may be inaccurate or biased due to
incomplete atmospheric physical processes, insufficient spatial-temporal
resolution, and inherent uncertainty of weather. Previous studies have
attempted to correct biases by using handcrafted features and domain knowledge,
or by applying general machine learning models naively. They do not fully
explore the complicated meteorologic interactions and spatial dependencies in
the atmosphere dynamically, which limits their applicability in NWP
bias-correction. Specifically, weather factors interact with each other in
complex ways, and these interactions can vary regionally. In addition, the
interactions between weather factors are further complicated by the spatial
dependencies between regions, which are influenced by varied terrain and
atmospheric motions. To address these issues, we propose WeatherGNN, an NWP
bias-correction method that utilizes Graph Neural Networks (GNN) to learn
meteorologic and geographic relationships in a unified framework. Our approach
includes a factor-wise GNN that captures meteorological interactions within
each grid (a specific location) adaptively, and a fast hierarchical GNN that
captures spatial dependencies between grids dynamically. Notably, the fast
hierarchical GNN achieves linear complexity with respect to the number of
grids, enhancing model efficiency and scalability. Our experimental results on
two real-world datasets demonstrate the superiority of WeatherGNN in comparison
with other SOTA methods, with an average improvement of 40.50\% on RMSE
compared to the original NWP. | [
"Binqing Wu",
"Weiqi Chen",
"Wengwei Wang",
"Bingqing Peng",
"Liang Sun",
"Ling Chen"
] | 2023-10-09 08:33:19 | http://arxiv.org/abs/2310.05517v1 | http://arxiv.org/pdf/2310.05517v1 | 2310.05517v1 |
Query and Response Augmentation Cannot Help Out-of-domain Math Reasoning Generalization | In math reasoning with large language models (LLMs), fine-tuning data
augmentation by query evolution and diverse reasoning paths is empirically
verified effective, profoundly narrowing the gap between open-sourced LLMs and
cutting-edge proprietary LLMs. In this paper, we conduct an investigation for
such data augmentation in math reasoning and are intended to answer: (1) What
strategies of data augmentation are more effective; (2) What is the scaling
relationship between the amount of augmented data and model performance; and
(3) Can data augmentation incentivize generalization to out-of-domain
mathematical reasoning tasks? To this end, we create a new dataset, AugGSM8K,
by complicating and diversifying the queries from GSM8K and sampling multiple
reasoning paths. We obtained a series of LLMs called MuggleMath by fine-tuning
on subsets of AugGSM8K. MuggleMath substantially achieves new state-of-the-art
on GSM8K (from 54% to 68.4% at the scale of 7B, and from 63.9% to 74.0% at the
scale of 13B). A log-linear relationship is presented between MuggleMath's
performance and the amount of augmented data. We also find that MuggleMath is
weak in out-of-domain math reasoning generalization to MATH. This is attributed
to the differences in query distribution between AugGSM8K and MATH which
suggest that augmentation on a single benchmark could not help with overall
math reasoning performance. Codes and AugGSM8K will be uploaded to
https://github.com/OFA-Sys/gsm8k-ScRel. | [
"Chengpeng Li",
"Zheng Yuan",
"Guanting Dong",
"Keming Lu",
"Jiancan Wu",
"Chuanqi Tan",
"Xiang Wang",
"Chang Zhou"
] | 2023-10-09 08:18:58 | http://arxiv.org/abs/2310.05506v1 | http://arxiv.org/pdf/2310.05506v1 | 2310.05506v1 |
A Neural Tangent Kernel View on Federated Averaging for Deep Linear Neural Network | Federated averaging (FedAvg) is a widely employed paradigm for
collaboratively training models from distributed clients without sharing data.
Nowadays, the neural network has achieved remarkable success due to its
extraordinary performance, which makes it a preferred choice as the model in
FedAvg. However, the optimization problem of the neural network is often
non-convex even non-smooth. Furthermore, FedAvg always involves multiple
clients and local updates, which results in an inaccurate updating direction.
These properties bring difficulties in analyzing the convergence of FedAvg in
training neural networks. Recently, neural tangent kernel (NTK) theory has been
proposed towards understanding the convergence of first-order methods in
tackling the non-convex problem of neural networks. The deep linear neural
network is a classical model in theoretical subject due to its simple
formulation. Nevertheless, there exists no theoretical result for the
convergence of FedAvg in training the deep linear neural network. By applying
NTK theory, we make a further step to provide the first theoretical guarantee
for the global convergence of FedAvg in training deep linear neural networks.
Specifically, we prove FedAvg converges to the global minimum at a linear rate
$\mathcal{O}\big((1-\eta K /N)^t\big)$, where $t$ is the number of iterations,
$\eta$ is the learning rate, $N$ is the number of clients and $K$ is the number
of local updates. Finally, experimental evaluations on two benchmark datasets
are conducted to empirically validate the correctness of our theoretical
findings. | [
"Xin Liu",
"Dazhi Zhan",
"Wei Tao",
"Xin Ma",
"Yu Pan",
"Yu Ding",
"Zhisong Pan"
] | 2023-10-09 07:56:56 | http://arxiv.org/abs/2310.05495v1 | http://arxiv.org/pdf/2310.05495v1 | 2310.05495v1 |
How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition | Large language models (LLMs) with enormous pre-training tokens and parameter
amounts emerge abilities, including math reasoning, code generation, and
instruction following. These abilities are further enhanced by supervised
fine-tuning (SFT). The open-source community has studied on ad-hoc SFT for each
ability, while proprietary LLMs are versatile for all abilities. It is
important to investigate how to unlock them with multiple abilities via SFT. In
this study, we specifically focus on the data composition between mathematical
reasoning, code generation, and general human-aligning abilities during SFT.
From a scaling perspective, we investigate the relationship between model
abilities and various factors including data amounts, data composition ratio,
model parameters, and SFT strategies. Our experiments reveal that different
abilities exhibit different scaling patterns, and larger models generally show
superior performance with the same amount of data. Mathematical reasoning and
code generation improve as data amounts increase consistently, while the
general ability is enhanced with about a thousand samples and improves slowly.
We find data composition results in various abilities improvements with low
data amounts, while conflicts of abilities with high data amounts. Our
experiments further show that composition data amount impacts performance,
while the influence of composition ratio is insignificant. Regarding the SFT
strategies, we evaluate sequential learning multiple abilities are prone to
catastrophic forgetting. Our proposed Dual-stage Mixed Fine-tuning (DMT)
strategy learns specialized abilities first and then learns general abilities
with a small amount of specialized data to prevent forgetting, offering a
promising solution to learn multiple abilities with different scaling patterns. | [
"Guanting Dong",
"Hongyi Yuan",
"Keming Lu",
"Chengpeng Li",
"Mingfeng Xue",
"Dayiheng Liu",
"Wei Wang",
"Zheng Yuan",
"Chang Zhou",
"Jingren Zhou"
] | 2023-10-09 07:56:16 | http://arxiv.org/abs/2310.05492v1 | http://arxiv.org/pdf/2310.05492v1 | 2310.05492v1 |
Integration-free Training for Spatio-temporal Multimodal Covariate Deep Kernel Point Processes | In this study, we propose a novel deep spatio-temporal point process model,
Deep Kernel Mixture Point Processes (DKMPP), that incorporates multimodal
covariate information. DKMPP is an enhanced version of Deep Mixture Point
Processes (DMPP), which uses a more flexible deep kernel to model complex
relationships between events and covariate data, improving the model's
expressiveness. To address the intractable training procedure of DKMPP due to
the non-integrable deep kernel, we utilize an integration-free method based on
score matching, and further improve efficiency by adopting a scalable denoising
score matching method. Our experiments demonstrate that DKMPP and its
corresponding score-based estimators outperform baseline models, showcasing the
advantages of incorporating covariate information, utilizing a deep kernel, and
employing score-based estimators. | [
"Yixuan Zhang",
"Quyu Kong",
"Feng Zhou"
] | 2023-10-09 07:44:37 | http://arxiv.org/abs/2310.05485v1 | http://arxiv.org/pdf/2310.05485v1 | 2310.05485v1 |
IDTraffickers: An Authorship Attribution Dataset to link and connect Potential Human-Trafficking Operations on Text Escort Advertisements | Human trafficking (HT) is a pervasive global issue affecting vulnerable
individuals, violating their fundamental human rights. Investigations reveal
that a significant number of HT cases are associated with online advertisements
(ads), particularly in escort markets. Consequently, identifying and connecting
HT vendors has become increasingly challenging for Law Enforcement Agencies
(LEAs). To address this issue, we introduce IDTraffickers, an extensive dataset
consisting of 87,595 text ads and 5,244 vendor labels to enable the
verification and identification of potential HT vendors on online escort
markets. To establish a benchmark for authorship identification, we train a
DeCLUTR-small model, achieving a macro-F1 score of 0.8656 in a closed-set
classification environment. Next, we leverage the style representations
extracted from the trained classifier to conduct authorship verification,
resulting in a mean r-precision score of 0.8852 in an open-set ranking
environment. Finally, to encourage further research and ensure responsible data
sharing, we plan to release IDTraffickers for the authorship attribution task
to researchers under specific conditions, considering the sensitive nature of
the data. We believe that the availability of our dataset and benchmarks will
empower future researchers to utilize our findings, thereby facilitating the
effective linkage of escort ads and the development of more robust approaches
for identifying HT indicators. | [
"Vageesh Saxena",
"Benjamin Bashpole",
"Gijs Van Dijck",
"Gerasimos Spanakis"
] | 2023-10-09 07:43:57 | http://arxiv.org/abs/2310.05484v1 | http://arxiv.org/pdf/2310.05484v1 | 2310.05484v1 |
Vibroacoustic Frequency Response Prediction with Query-based Operator Networks | Understanding vibroacoustic wave propagation in mechanical structures like
airplanes, cars and houses is crucial to ensure health and comfort of their
users. To analyze such systems, designers and engineers primarily consider the
dynamic response in the frequency domain, which is computed through expensive
numerical simulations like the finite element method. In contrast, data-driven
surrogate models offer the promise of speeding up these simulations, thereby
facilitating tasks like design optimization, uncertainty quantification, and
design space exploration. We present a structured benchmark for a
representative vibroacoustic problem: Predicting the frequency response for
vibrating plates with varying forms of beadings. The benchmark features a total
of 12,000 plate geometries with an associated numerical solution and introduces
evaluation metrics to quantify the prediction quality. To address the frequency
response prediction task, we propose a novel frequency query operator model,
which is trained to map plate geometries to frequency response functions. By
integrating principles from operator learning and implicit models for shape
encoding, our approach effectively addresses the prediction of resonance peaks
of frequency responses. We evaluate the method on our vibrating-plates
benchmark and find that it outperforms DeepONets, Fourier Neural Operators and
more traditional neural network architectures. The code and dataset are
available from https://eckerlab.org/code/delden2023_plate. | [
"Jan van Delden",
"Julius Schultz",
"Christopher Blech",
"Sabine C. Langer",
"Timo Lüddecke"
] | 2023-10-09 07:26:35 | http://arxiv.org/abs/2310.05469v2 | http://arxiv.org/pdf/2310.05469v2 | 2310.05469v2 |
ExIFFI and EIF+: Interpretability and Enhanced Generalizability to Extend the Extended Isolation Forest | Anomaly detection, an essential unsupervised machine learning task, involves
identifying unusual behaviors within complex datasets and systems. While
Machine Learning algorithms and decision support systems (DSSs) offer effective
solutions for this task, simply pinpointing anomalies often falls short in
real-world applications. Users of these systems often require insight into the
underlying reasons behind predictions to facilitate Root Cause Analysis and
foster trust in the model. However, due to the unsupervised nature of anomaly
detection, creating interpretable tools is challenging. This work introduces
EIF+, an enhanced variant of Extended Isolation Forest (EIF), designed to
enhance generalization capabilities. Additionally, we present ExIFFI, a novel
approach that equips Extended Isolation Forest with interpretability features,
specifically feature rankings. Experimental results provide a comprehensive
comparative analysis of Isolation-based approaches for Anomaly Detection,
including synthetic and real dataset evaluations that demonstrate ExIFFI's
effectiveness in providing explanations. We also illustrate how ExIFFI serves
as a valid feature selection technique in unsupervised settings. To facilitate
further research and reproducibility, we also provide open-source code to
replicate the results. | [
"Alessio Arcudi",
"Davide Frizzo",
"Chiara Masiero",
"Gian Antonio Susto"
] | 2023-10-09 07:24:04 | http://arxiv.org/abs/2310.05468v1 | http://arxiv.org/pdf/2310.05468v1 | 2310.05468v1 |
Temporal Convolutional Explorer Helps Understand 1D-CNN's Learning Behavior in Time Series Classification from Frequency Domain | While one-dimensional convolutional neural networks (1D-CNNs) have been
empirically proven effective in time series classification tasks, we find that
there remain undesirable outcomes that could arise in their application,
motivating us to further investigate and understand their underlying
mechanisms. In this work, we propose a Temporal Convolutional Explorer (TCE) to
empirically explore the learning behavior of 1D-CNNs from the perspective of
the frequency domain. Our TCE analysis highlights that deeper 1D-CNNs tend to
distract the focus from the low-frequency components leading to the accuracy
degradation phenomenon, and the disturbing convolution is the driving factor.
Then, we leverage our findings to the practical application and propose a
regulatory framework, which can easily be integrated into existing 1D-CNNs. It
aims to rectify the suboptimal learning behavior by enabling the network to
selectively bypass the specified disturbing convolutions. Finally, through
comprehensive experiments on widely-used UCR, UEA, and UCI benchmarks, we
demonstrate that 1) TCE's insight into 1D-CNN's learning behavior; 2) our
regulatory framework enables state-of-the-art 1D-CNNs to get improved
performances with less consumption of memory and computational overhead. | [
"Junru Zhang",
"Lang Feng",
"Yang He",
"Yuhan Wu",
"Yabo Dong"
] | 2023-10-09 07:22:22 | http://arxiv.org/abs/2310.05467v1 | http://arxiv.org/pdf/2310.05467v1 | 2310.05467v1 |
Cost-Sensitive Best Subset Selection for Logistic Regression: A Mixed-Integer Conic Optimization Perspective | A key challenge in machine learning is to design interpretable models that
can reduce their inputs to the best subset for making transparent predictions,
especially in the clinical domain. In this work, we propose a certifiably
optimal feature selection procedure for logistic regression from a
mixed-integer conic optimization perspective that can take an auxiliary cost to
obtain features into account. Based on an extensive review of the literature,
we carefully create a synthetic dataset generator for clinical prognostic model
research. This allows us to systematically evaluate different heuristic and
optimal cardinality- and budget-constrained feature selection procedures. The
analysis shows key limitations of the methods for the low-data regime and when
confronted with label noise. Our paper not only provides empirical
recommendations for suitable methods and dataset designs, but also paves the
way for future research in the area of meta-learning. | [
"Ricardo Knauer",
"Erik Rodner"
] | 2023-10-09 07:13:40 | http://arxiv.org/abs/2310.05464v1 | http://arxiv.org/pdf/2310.05464v1 | 2310.05464v1 |
Ensemble-based Hybrid Optimization of Bayesian Neural Networks and Traditional Machine Learning Algorithms | This research introduces a novel methodology for optimizing Bayesian Neural
Networks (BNNs) by synergistically integrating them with traditional machine
learning algorithms such as Random Forests (RF), Gradient Boosting (GB), and
Support Vector Machines (SVM). Feature integration solidifies these results by
emphasizing the second-order conditions for optimality, including stationarity
and positive definiteness of the Hessian matrix. Conversely, hyperparameter
tuning indicates a subdued impact in improving Expected Improvement (EI),
represented by EI(x). Overall, the ensemble method stands out as a robust,
algorithmically optimized approach. | [
"Peiwen Tan"
] | 2023-10-09 06:59:17 | http://arxiv.org/abs/2310.05456v1 | http://arxiv.org/pdf/2310.05456v1 | 2310.05456v1 |
RetSeg: Retention-based Colorectal Polyps Segmentation Network | Vision Transformers (ViTs) have revolutionized medical imaging analysis,
showcasing superior efficacy compared to conventional Convolutional Neural
Networks (CNNs) in vital tasks such as polyp classification, detection, and
segmentation. Leveraging attention mechanisms to focus on specific image
regions, ViTs exhibit contextual awareness in processing visual data,
culminating in robust and precise predictions, even for intricate medical
images. Moreover, the inherent self-attention mechanism in Transformers
accommodates varying input sizes and resolutions, granting an unprecedented
flexibility absent in traditional CNNs. However, Transformers grapple with
challenges like excessive memory usage and limited training parallelism due to
self-attention, rendering them impractical for real-time disease detection on
resource-constrained devices. In this study, we address these hurdles by
investigating the integration of the recently introduced retention mechanism
into polyp segmentation, introducing RetSeg, an encoder-decoder network
featuring multi-head retention blocks. Drawing inspiration from Retentive
Networks (RetNet), RetSeg is designed to bridge the gap between precise polyp
segmentation and resource utilization, particularly tailored for colonoscopy
images. We train and validate RetSeg for polyp segmentation employing two
publicly available datasets: Kvasir-SEG and CVC-ClinicDB. Additionally, we
showcase RetSeg's promising performance across diverse public datasets,
including CVC-ColonDB, ETIS-LaribPolypDB, CVC-300, and BKAI-IGH NeoPolyp. While
our work represents an early-stage exploration, further in-depth studies are
imperative to advance these promising findings. | [
"Khaled ELKarazle",
"Valliappan Raman",
"Caslon Chua",
"Patrick Then"
] | 2023-10-09 06:43:38 | http://arxiv.org/abs/2310.05446v3 | http://arxiv.org/pdf/2310.05446v3 | 2310.05446v3 |
Replication of Multi-agent Reinforcement Learning for the "Hide and Seek" Problem | Reinforcement learning generates policies based on reward functions and
hyperparameters. Slight changes in these can significantly affect results. The
lack of documentation and reproducibility in Reinforcement learning research
makes it difficult to replicate once-deduced strategies. While previous
research has identified strategies using grounded maneuvers, there is limited
work in more complex environments. The agents in this study are simulated
similarly to Open Al's hider and seek agents, in addition to a flying
mechanism, enhancing their mobility, and expanding their range of possible
actions and strategies. This added functionality improves the Hider agents to
develop a chasing strategy from approximately 2 million steps to 1.6 million
steps and hiders | [
"Haider Kamal",
"Muaz A. Niazi",
"Hammad Afzal"
] | 2023-10-09 06:06:34 | http://arxiv.org/abs/2310.05430v1 | http://arxiv.org/pdf/2310.05430v1 | 2310.05430v1 |
Reward-Consistent Dynamics Models are Strongly Generalizable for Offline Reinforcement Learning | Learning a precise dynamics model can be crucial for offline reinforcement
learning, which, unfortunately, has been found to be quite challenging.
Dynamics models that are learned by fitting historical transitions often
struggle to generalize to unseen transitions. In this study, we identify a
hidden but pivotal factor termed dynamics reward that remains consistent across
transitions, offering a pathway to better generalization. Therefore, we propose
the idea of reward-consistent dynamics models: any trajectory generated by the
dynamics model should maximize the dynamics reward derived from the data. We
implement this idea as the MOREC (Model-based Offline reinforcement learning
with Reward Consistency) method, which can be seamlessly integrated into
previous offline model-based reinforcement learning (MBRL) methods. MOREC
learns a generalizable dynamics reward function from offline data, which is
subsequently employed as a transition filter in any offline MBRL method: when
generating transitions, the dynamics model generates a batch of transitions and
selects the one with the highest dynamics reward value. On a synthetic task, we
visualize that MOREC has a strong generalization ability and can surprisingly
recover some distant unseen transitions. On 21 offline tasks in D4RL and NeoRL
benchmarks, MOREC improves the previous state-of-the-art performance by a
significant margin, i.e., 4.6% on D4RL tasks and 25.9% on NeoRL tasks. Notably,
MOREC is the first method that can achieve above 95% online RL performance in 6
out of 12 D4RL tasks and 3 out of 9 NeoRL tasks. | [
"Fan-Ming Luo",
"Tian Xu",
"Xingchen Cao",
"Yang Yu"
] | 2023-10-09 05:37:58 | http://arxiv.org/abs/2310.05422v1 | http://arxiv.org/pdf/2310.05422v1 | 2310.05422v1 |
Automating Customer Service using LangChain: Building custom open-source GPT Chatbot for organizations | In the digital age, the dynamics of customer service are evolving, driven by
technological advancements and the integration of Large Language Models (LLMs).
This research paper introduces a groundbreaking approach to automating customer
service using LangChain, a custom LLM tailored for organizations. The paper
explores the obsolescence of traditional customer support techniques,
particularly Frequently Asked Questions (FAQs), and proposes a paradigm shift
towards responsive, context-aware, and personalized customer interactions. The
heart of this innovation lies in the fusion of open-source methodologies, web
scraping, fine-tuning, and the seamless integration of LangChain into customer
service platforms. This open-source state-of-the-art framework, presented as
"Sahaay," demonstrates the ability to scale across industries and
organizations, offering real-time support and query resolution. Key elements of
this research encompass data collection via web scraping, the role of
embeddings, the utilization of Google's Flan T5 XXL, Base and Small language
models for knowledge retrieval, and the integration of the chatbot into
customer service platforms. The results section provides insights into their
performance and use cases, here particularly within an educational institution.
This research heralds a new era in customer service, where technology is
harnessed to create efficient, personalized, and responsive interactions.
Sahaay, powered by LangChain, redefines the customer-company relationship,
elevating customer retention, value extraction, and brand image. As
organizations embrace LLMs, customer service becomes a dynamic and
customer-centric ecosystem. | [
"Keivalya Pandya",
"Mehfuza Holia"
] | 2023-10-09 05:35:10 | http://arxiv.org/abs/2310.05421v1 | http://arxiv.org/pdf/2310.05421v1 | 2310.05421v1 |
On sparse regression, Lp-regularization, and automated model discovery | Sparse regression and feature extraction are the cornerstones of knowledge
discovery from massive data. Their goal is to discover interpretable and
predictive models that provide simple relationships among scientific variables.
While the statistical tools for model discovery are well established in the
context of linear regression, their generalization to nonlinear regression in
material modeling is highly problem-specific and insufficiently understood.
Here we explore the potential of neural networks for automatic model discovery
and induce sparsity by a hybrid approach that combines two strategies:
regularization and physical constraints. We integrate the concept of Lp
regularization for subset selection with constitutive neural networks that
leverage our domain knowledge in kinematics and thermodynamics. We train our
networks with both, synthetic and real data, and perform several thousand
discovery runs to infer common guidelines and trends: L2 regularization or
ridge regression is unsuitable for model discovery; L1 regularization or lasso
promotes sparsity, but induces strong bias; only L0 regularization allows us to
transparently fine-tune the trade-off between interpretability and
predictability, simplicity and accuracy, and bias and variance. With these
insights, we demonstrate that Lp regularized constitutive neural networks can
simultaneously discover both, interpretable models and physically meaningful
parameters. We anticipate that our findings will generalize to alternative
discovery techniques such as sparse and symbolic regression, and to other
domains such as biology, chemistry, or medicine. Our ability to automatically
discover material models from data could have tremendous applications in
generative material design and open new opportunities to manipulate matter,
alter properties of existing materials, and discover new materials with
user-defined properties. | [
"Jeremy A. McCulloch",
"Skyler R. St. Pierre",
"Kevin Linka",
"Ellen Kuhl"
] | 2023-10-09 05:34:21 | http://arxiv.org/abs/2310.06872v1 | http://arxiv.org/pdf/2310.06872v1 | 2310.06872v1 |
Entropy-MCMC: Sampling from Flat Basins with Ease | Bayesian deep learning counts on the quality of posterior distribution
estimation. However, the posterior of deep neural networks is highly
multi-modal in nature, with local modes exhibiting varying generalization
performance. Given a practical budget, sampling from the original posterior can
lead to suboptimal performance, as some samples may become trapped in "bad"
modes and suffer from overfitting. Leveraging the observation that "good" modes
with low generalization error often reside in flat basins of the energy
landscape, we propose to bias sampling on the posterior toward these flat
regions. Specifically, we introduce an auxiliary guiding variable, the
stationary distribution of which resembles a smoothed posterior free from sharp
modes, to lead the MCMC sampler to flat basins. By integrating this guiding
variable with the model parameter, we create a simple joint distribution that
enables efficient sampling with minimal computational overhead. We prove the
convergence of our method and further show that it converges faster than
several existing flatness-aware methods in the strongly convex setting.
Empirical results demonstrate that our method can successfully sample from flat
basins of the posterior, and outperforms all compared baselines on multiple
benchmarks including classification, calibration, and out-of-distribution
detection. | [
"Bolian Li",
"Ruqi Zhang"
] | 2023-10-09 04:40:20 | http://arxiv.org/abs/2310.05401v1 | http://arxiv.org/pdf/2310.05401v1 | 2310.05401v1 |
Find Your Optimal Assignments On-the-fly: A Holistic Framework for Clustered Federated Learning | Federated Learning (FL) is an emerging distributed machine learning approach
that preserves client privacy by storing data on edge devices. However, data
heterogeneity among clients presents challenges in training models that perform
well on all local distributions. Recent studies have proposed clustering as a
solution to tackle client heterogeneity in FL by grouping clients with
distribution shifts into different clusters. However, the diverse learning
frameworks used in current clustered FL methods make it challenging to
integrate various clustered FL methods, gather their benefits, and make further
improvements.
To this end, this paper presents a comprehensive investigation into current
clustered FL methods and proposes a four-tier framework, namely HCFL, to
encompass and extend existing approaches. Based on the HCFL, we identify the
remaining challenges associated with current clustering methods in each tier
and propose an enhanced clustering method called HCFL+ to address these
challenges. Through extensive numerical evaluations, we showcase the
effectiveness of our clustering framework and the improved components. Our code
will be publicly available. | [
"Yongxin Guo",
"Xiaoying Tang",
"Tao Lin"
] | 2023-10-09 04:23:11 | http://arxiv.org/abs/2310.05397v1 | http://arxiv.org/pdf/2310.05397v1 | 2310.05397v1 |
Robust Image Watermarking based on Cross-Attention and Invariant Domain Learning | Image watermarking involves embedding and extracting watermarks within a
cover image, with deep learning approaches emerging to bolster generalization
and robustness. Predominantly, current methods employ convolution and
concatenation for watermark embedding, while also integrating conceivable
augmentation in the training process. This paper explores a robust image
watermarking methodology by harnessing cross-attention and invariant domain
learning, marking two novel, significant advancements. First, we design a
watermark embedding technique utilizing a multi-head cross attention mechanism,
enabling information exchange between the cover image and watermark to identify
semantically suitable embedding locations. Second, we advocate for learning an
invariant domain representation that encapsulates both semantic and
noise-invariant information concerning the watermark, shedding light on
promising avenues for enhancing image watermarking techniques. | [
"Agnibh Dasgupta",
"Xin Zhong"
] | 2023-10-09 04:19:27 | http://arxiv.org/abs/2310.05395v1 | http://arxiv.org/pdf/2310.05395v1 | 2310.05395v1 |
Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels | Discovering governing equations from data is important to many scientific and
engineering applications. Despite promising successes, existing methods are
still challenged by data sparsity as well as noise issues, both of which are
ubiquitous in practice. Moreover, state-of-the-art methods lack uncertainty
quantification and/or are costly in training. To overcome these limitations, we
propose a novel equation discovery method based on Kernel learning and BAyesian
Spike-and-Slab priors (KBASS). We use kernel regression to estimate the target
function, which is flexible, expressive, and more robust to data sparsity and
noises. We combine it with a Bayesian spike-and-slab prior -- an ideal Bayesian
sparse distribution -- for effective operator selection and uncertainty
quantification. We develop an expectation propagation expectation-maximization
(EP-EM) algorithm for efficient posterior inference and function estimation. To
overcome the computational challenge of kernel regression, we place the
function values on a mesh and induce a Kronecker product construction, and we
use tensor algebra methods to enable efficient computation and optimization. We
show the significant advantages of KBASS on a list of benchmark ODE and PDE
discovery tasks. | [
"Da Long",
"Wei W. Xing",
"Aditi S. Krishnapriyan",
"Robert M. Kirby",
"Shandian Zhe",
"Michael W. Mahoney"
] | 2023-10-09 03:55:09 | http://arxiv.org/abs/2310.05387v1 | http://arxiv.org/pdf/2310.05387v1 | 2310.05387v1 |
CCAE: A Corpus of Chinese-based Asian Englishes | Language models have been foundations in various scenarios of NLP
applications, but it has not been well applied in language variety studies,
even for the most popular language like English. This paper represents one of
the few initial efforts to utilize the NLP technology in the paradigm of World
Englishes, specifically in creating a multi-variety corpus for studying Asian
Englishes. We present an overview of the CCAE -- Corpus of Chinese-based Asian
English, a suite of corpora comprising six Chinese-based Asian English
varieties. It is based on 340 million tokens in 448 thousand web documents from
six regions. The ontology of data would make the corpus a helpful resource with
enormous research potential for Asian Englishes (especially for Chinese
Englishes for which there has not been a publicly accessible corpus yet so far)
and an ideal source for variety-specific language modeling and downstream
tasks, thus setting the stage for NLP-based World Englishes studies. And
preliminary experiments on this corpus reveal the practical value of CCAE.
Finally, we make CCAE available at
\href{https://huggingface.co/datasets/CCAE/CCAE-Corpus}{this https URL}. | [
"Yang Liu",
"Melissa Xiaohui Qin",
"Long Wang",
"Chao Huang"
] | 2023-10-09 03:34:15 | http://arxiv.org/abs/2310.05381v1 | http://arxiv.org/pdf/2310.05381v1 | 2310.05381v1 |
Augmented Embeddings for Custom Retrievals | Information retrieval involves selecting artifacts from a corpus that are
most relevant to a given search query. The flavor of retrieval typically used
in classical applications can be termed as homogeneous and relaxed, where
queries and corpus elements are both natural language (NL) utterances
(homogeneous) and the goal is to pick most relevant elements from the corpus in
the Top-K, where K is large, such as 10, 25, 50 or even 100 (relaxed).
Recently, retrieval is being used extensively in preparing prompts for large
language models (LLMs) to enable LLMs to perform targeted tasks. These new
applications of retrieval are often heterogeneous and strict -- the queries and
the corpus contain different kinds of entities, such as NL and code, and there
is a need for improving retrieval at Top-K for small values of K, such as K=1
or 3 or 5. Current dense retrieval techniques based on pretrained embeddings
provide a general-purpose and powerful approach for retrieval, but they are
oblivious to task-specific notions of similarity of heterogeneous artifacts. We
introduce Adapted Dense Retrieval, a mechanism to transform embeddings to
enable improved task-specific, heterogeneous and strict retrieval. Adapted
Dense Retrieval works by learning a low-rank residual adaptation of the
pretrained black-box embedding. We empirically validate our approach by showing
improvements over the state-of-the-art general-purpose embeddings-based
baseline. | [
"Anirudh Khatry",
"Yasharth Bajpai",
"Priyanshu Gupta",
"Sumit Gulwani",
"Ashish Tiwari"
] | 2023-10-09 03:29:35 | http://arxiv.org/abs/2310.05380v1 | http://arxiv.org/pdf/2310.05380v1 | 2310.05380v1 |
Improving End-to-End Speech Processing by Efficient Text Data Utilization with Latent Synthesis | Training a high performance end-to-end speech (E2E) processing model requires
an enormous amount of labeled speech data, especially in the era of
data-centric artificial intelligence. However, labeled speech data are usually
scarcer and more expensive for collection, compared to textual data. We propose
Latent Synthesis (LaSyn), an efficient textual data utilization framework for
E2E speech processing models. We train a latent synthesizer to convert textual
data into an intermediate latent representation of a pre-trained speech model.
These pseudo acoustic representations of textual data augment acoustic data for
model training. We evaluate LaSyn on low-resource automatic speech recognition
(ASR) and spoken language understanding (SLU) tasks. For ASR, LaSyn improves an
E2E baseline trained on LibriSpeech train-clean-100, with relative word error
rate reductions over 22.3% on different test sets. For SLU, LaSyn improves our
E2E baseline by absolute 4.1% for intent classification accuracy and 3.8% for
slot filling SLU-F1 on SLURP, and absolute 4.49% and 2.25% for exact match (EM)
and EM-Tree accuracies on STOP respectively. With fewer parameters, the results
of LaSyn are competitive to published state-of-the-art works. The results
demonstrate the quality of the augmented training data. The source code will be
available to the community. | [
"Jianqiao Lu",
"Wenyong Huang",
"Nianzu Zheng",
"Xingshan Zeng",
"Yu Ting Yeung",
"Xiao Chen"
] | 2023-10-09 03:10:49 | http://arxiv.org/abs/2310.05374v2 | http://arxiv.org/pdf/2310.05374v2 | 2310.05374v2 |
Quantum Bayesian Optimization | Kernelized bandits, also known as Bayesian optimization (BO), has been a
prevalent method for optimizing complicated black-box reward functions. Various
BO algorithms have been theoretically shown to enjoy upper bounds on their
cumulative regret which are sub-linear in the number T of iterations, and a
regret lower bound of Omega(sqrt(T)) has been derived which represents the
unavoidable regrets for any classical BO algorithm. Recent works on quantum
bandits have shown that with the aid of quantum computing, it is possible to
achieve tighter regret upper bounds better than their corresponding classical
lower bounds. However, these works are restricted to either multi-armed or
linear bandits, and are hence not able to solve sophisticated real-world
problems with non-linear reward functions. To this end, we introduce the
quantum-Gaussian process-upper confidence bound (Q-GP-UCB) algorithm. To the
best of our knowledge, our Q-GP-UCB is the first BO algorithm able to achieve a
regret upper bound of O(polylog T), which is significantly smaller than its
regret lower bound of Omega(sqrt(T)) in the classical setting. Moreover, thanks
to our novel analysis of the confidence ellipsoid, our Q-GP-UCB with the linear
kernel achieves a smaller regret than the quantum linear UCB algorithm from the
previous work. We use simulations, as well as an experiment using a real
quantum computer, to verify that the theoretical quantum speedup achieved by
our Q-GP-UCB is also potentially relevant in practice. | [
"Zhongxiang Dai",
"Gregory Kang Ruey Lau",
"Arun Verma",
"Yao Shu",
"Bryan Kian Hsiang Low",
"Patrick Jaillet"
] | 2023-10-09 03:10:42 | http://arxiv.org/abs/2310.05373v1 | http://arxiv.org/pdf/2310.05373v1 | 2310.05373v1 |
Enhancing Prostate Cancer Diagnosis with Deep Learning: A Study using mpMRI Segmentation and Classification | Prostate cancer (PCa) is a severe disease among men globally. It is important
to identify PCa early and make a precise diagnosis for effective treatment. For
PCa diagnosis, Multi-parametric magnetic resonance imaging (mpMRI) emerged as
an invaluable imaging modality that offers a precise anatomical view of the
prostate gland and its tissue structure. Deep learning (DL) models can enhance
existing clinical systems and improve patient care by locating regions of
interest for physicians. Recently, DL techniques have been employed to develop
a pipeline for segmenting and classifying different cancer types. These studies
show that DL can be used to increase diagnostic precision and give objective
results without variability. This work uses well-known DL models for the
classification and segmentation of mpMRI images to detect PCa. Our
implementation involves four pipelines; Semantic DeepSegNet with ResNet50,
DeepSegNet with recurrent neural network (RNN), U-Net with RNN, and U-Net with
a long short-term memory (LSTM). Each segmentation model is paired with a
different classifier to evaluate the performance using different metrics. The
results of our experiments show that the pipeline that uses the combination of
U-Net and the LSTM model outperforms all other combinations, excelling in both
segmentation and classification tasks. | [
"Anil B. Gavade",
"Neel Kanwal",
"Priyanka A. Gavade",
"Rajendra Nerli"
] | 2023-10-09 03:00:15 | http://arxiv.org/abs/2310.05371v2 | http://arxiv.org/pdf/2310.05371v2 | 2310.05371v2 |
Molecular De Novo Design through Transformer-based Reinforcement Learning | In this work, we introduce a method to fine-tune a Transformer-based
generative model for molecular de novo design. Leveraging the superior sequence
learning capacity of Transformers over Recurrent Neural Networks (RNNs), our
model can generate molecular structures with desired properties effectively. In
contrast to the traditional RNN-based models, our proposed method exhibits
superior performance in generating compounds predicted to be active against
various biological targets, capturing long-term dependencies in the molecular
structure sequence. The model's efficacy is demonstrated across numerous tasks,
including generating analogues to a query structure and producing compounds
with particular attributes, outperforming the baseline RNN-based methods. Our
approach can be used for scaffold hopping, library expansion starting from a
single molecule, and generating compounds with high predicted activity against
biological targets. | [
"Tao Feng",
"Pengcheng Xu",
"Tianfan Fu",
"Siddhartha Laghuvarapu",
"Jimeng Sun"
] | 2023-10-09 02:51:01 | http://arxiv.org/abs/2310.05365v2 | http://arxiv.org/pdf/2310.05365v2 | 2310.05365v2 |
Generalized Neural Collapse for a Large Number of Classes | Neural collapse provides an elegant mathematical characterization of learned
last layer representations (a.k.a. features) and classifier weights in deep
classification models. Such results not only provide insights but also motivate
new techniques for improving practical deep models. However, most of the
existing empirical and theoretical studies in neural collapse focus on the case
that the number of classes is small relative to the dimension of the feature
space. This paper extends neural collapse to cases where the number of classes
are much larger than the dimension of feature space, which broadly occur for
language models, retrieval systems, and face recognition applications. We show
that the features and classifier exhibit a generalized neural collapse
phenomenon, where the minimum one-vs-rest margins is maximized.We provide
empirical study to verify the occurrence of generalized neural collapse in
practical deep neural networks. Moreover, we provide theoretical study to show
that the generalized neural collapse provably occurs under unconstrained
feature model with spherical constraint, under certain technical conditions on
feature dimension and number of classes. | [
"Jiachen Jiang",
"Jinxin Zhou",
"Peng Wang",
"Qing Qu",
"Dustin Mixon",
"Chong You",
"Zhihui Zhu"
] | 2023-10-09 02:27:04 | http://arxiv.org/abs/2310.05351v2 | http://arxiv.org/pdf/2310.05351v2 | 2310.05351v2 |
Scaling Studies for Efficient Parameter Search and Parallelism for Large Language Model Pre-training | AI accelerator processing capabilities and memory constraints largely dictate
the scale in which machine learning workloads (e.g., training and inference)
can be executed within a desirable time frame. Training a state of the art,
transformer-based model today requires use of GPU-accelerated high performance
computers with high-speed interconnects. As datasets and models continue to
increase in size, computational requirements and memory demands for AI also
continue to grow. These challenges have inspired the development of distributed
algorithm and circuit-based optimization techniques that enable the ability to
progressively scale models in multi-node environments, efficiently minimize
neural network cost functions for faster convergence, and store more parameters
into a set number of available resources. In our research project, we focus on
parallel and distributed machine learning algorithm development, specifically
for optimizing the data processing and pre-training of a set of 5
encoder-decoder LLMs, ranging from 580 million parameters to 13 billion
parameters. We performed a fine-grained study to quantify the relationships
between three ML parallelism methods, specifically exploring Microsoft
DeepSpeed Zero Redundancy Optimizer (ZeRO) stages. | [
"Michael Benington",
"Leo Phan",
"Chris Pierre Paul",
"Evan Shoemaker",
"Priyanka Ranade",
"Torstein Collett",
"Grant Hodgson Perez",
"Christopher Krieger"
] | 2023-10-09 02:22:00 | http://arxiv.org/abs/2310.05350v2 | http://arxiv.org/pdf/2310.05350v2 | 2310.05350v2 |
Continuous Invariance Learning | Invariance learning methods aim to learn invariant features in the hope that
they generalize under distributional shifts. Although many tasks are naturally
characterized by continuous domains, current invariance learning techniques
generally assume categorically indexed domains. For example, auto-scaling in
cloud computing often needs a CPU utilization prediction model that generalizes
across different times (e.g., time of a day and date of a year), where `time'
is a continuous domain index. In this paper, we start by theoretically showing
that existing invariance learning methods can fail for continuous domain
problems. Specifically, the naive solution of splitting continuous domains into
discrete ones ignores the underlying relationship among domains, and therefore
potentially leads to suboptimal performance. To address this challenge, we then
propose Continuous Invariance Learning (CIL), which extracts invariant features
across continuously indexed domains. CIL is a novel adversarial procedure that
measures and controls the conditional independence between the labels and
continuous domain indices given the extracted features. Our theoretical
analysis demonstrates the superiority of CIL over existing invariance learning
methods. Empirical results on both synthetic and real-world datasets (including
data collected from production systems) show that CIL consistently outperforms
strong baselines among all the tasks. | [
"Yong Lin",
"Fan Zhou",
"Lu Tan",
"Lintao Ma",
"Jiameng Liu",
"Yansu He",
"Yuan Yuan",
"Yu Liu",
"James Zhang",
"Yujiu Yang",
"Hao Wang"
] | 2023-10-09 02:18:45 | http://arxiv.org/abs/2310.05348v1 | http://arxiv.org/pdf/2310.05348v1 | 2310.05348v1 |
SteerLM: Attribute Conditioned SFT as an (User-Steerable) Alternative to RLHF | Model alignment with human preferences is an essential step in making Large
Language Models (LLMs) helpful and consistent with human values. It typically
consists of supervised fine-tuning (SFT) and reinforcement learning from human
feedback (RLHF) stages. However, RLHF faces inherent limitations stemming from
a complex training setup and its tendency to align the model with implicit
values that end users cannot control at run-time. Moreover, reward models in
RLHF stage commonly rely on single-dimensional feedback as opposed to explicit,
multifaceted signals that indicate attributes such as helpfulness, humor, and
toxicity. To address these limitations, we propose SteerLM, a supervised
fine-tuning method that empowers end-users to control responses during
inference. SteerLM conditions responses to conform to an explicitly defined
multi-dimensional set of attributes, thereby empowering a steerable AI capable
of generating helpful and high-quality responses while maintaining
customizability. Experiments show that SteerLM trained on open source datasets
generates responses that are preferred by human and automatic evaluators to
many state-of-the-art baselines trained with RLHF while being much easier to
train. Try SteerLM at https://huggingface.co/nvidia/SteerLM-llama2-13B | [
"Yi Dong",
"Zhilin Wang",
"Makesh Narsimhan Sreedhar",
"Xianchao Wu",
"Oleksii Kuchaiev"
] | 2023-10-09 02:11:21 | http://arxiv.org/abs/2310.05344v1 | http://arxiv.org/pdf/2310.05344v1 | 2310.05344v1 |
Investigating Continuous Learning in Spiking Neural Networks | In this paper, the use of third-generation machine learning, also known as
spiking neural network architecture, for continuous learning was investigated
and compared to conventional models. The experimentation was divided into three
separate phases. The first phase focused on training the conventional models
via transfer learning. The second phase trains a Nengo model from their
library. Lastly, each conventional model is converted into a spiking neural
network and trained. Initial results from phase 1 are inline with known
knowledge about continuous learning within current machine learning literature.
All models were able to correctly identify the current classes, but they would
immediately see a sharp performance drop in previous classes due to
catastrophic forgetting. However, the SNN models were able to retain some
information about previous classes. Although many of the previous classes were
still identified as the current trained classes, the output probabilities
showed a higher than normal value to the actual class. This indicates that the
SNN models do have potential to overcome catastrophic forgetting but much work
is still needed. | [
"C. Tanner Fredieu"
] | 2023-10-09 02:08:18 | http://arxiv.org/abs/2310.05343v1 | http://arxiv.org/pdf/2310.05343v1 | 2310.05343v1 |
What do larger image classifiers memorise? | The success of modern neural networks has prompted study of the connection
between memorisation and generalisation: overparameterised models generalise
well, despite being able to perfectly fit (memorise) completely random labels.
To carefully study this issue, Feldman proposed a metric to quantify the degree
of memorisation of individual training examples, and empirically computed the
corresponding memorisation profile of a ResNet on image classification
bench-marks. While an exciting first glimpse into what real-world models
memorise, this leaves open a fundamental question: do larger neural models
memorise more? We present a comprehensive empirical analysis of this question
on image classification benchmarks. We find that training examples exhibit an
unexpectedly diverse set of memorisation trajectories across model sizes: most
samples experience decreased memorisation under larger models, while the rest
exhibit cap-shaped or increasing memorisation. We show that various proxies for
the Feldman memorization score fail to capture these fundamental trends.
Lastly, we find that knowledge distillation, an effective and popular model
compression technique, tends to inhibit memorisation, while also improving
generalisation. Specifically, memorisation is mostly inhibited on examples with
increasing memorisation trajectories, thus pointing at how distillation
improves generalisation. | [
"Michal Lukasik",
"Vaishnavh Nagarajan",
"Ankit Singh Rawat",
"Aditya Krishna Menon",
"Sanjiv Kumar"
] | 2023-10-09 01:52:07 | http://arxiv.org/abs/2310.05337v1 | http://arxiv.org/pdf/2310.05337v1 | 2310.05337v1 |
GReAT: A Graph Regularized Adversarial Training Method | This paper proposes a regularization method called GReAT, Graph Regularized
Adversarial Training, to improve deep learning models' classification
performance. Adversarial examples are a well-known challenge in machine
learning, where small, purposeful perturbations to input data can mislead
models. Adversarial training, a powerful and one of the most effective defense
strategies, involves training models with both regular and adversarial
examples. However, it often neglects the underlying structure of the data. In
response, we propose GReAT, a method that leverages data graph structure to
enhance model robustness. GReAT deploys the graph structure of the data into
the adversarial training process, resulting in more robust models that better
generalize its testing performance and defend against adversarial attacks.
Through extensive evaluation on benchmark datasets, we demonstrate GReAT's
effectiveness compared to state-of-the-art classification methods, highlighting
its potential in improving deep learning models' classification performance. | [
"Samet Bayram",
"Kenneth Barner"
] | 2023-10-09 01:44:06 | http://arxiv.org/abs/2310.05336v1 | http://arxiv.org/pdf/2310.05336v1 | 2310.05336v1 |
DiffCPS: Diffusion Model based Constrained Policy Search for Offline Reinforcement Learning | Constrained policy search (CPS) is a fundamental problem in offline
reinforcement learning, which is generally solved by advantage weighted
regression (AWR). However, previous methods may still encounter
out-of-distribution actions due to the limited expressivity of Gaussian-based
policies. On the other hand, directly applying the state-of-the-art models with
distribution expression capabilities (i.e., diffusion models) in the AWR
framework is insufficient since AWR requires exact policy probability
densities, which is intractable in diffusion models. In this paper, we propose
a novel approach called $\textbf{Diffusion Model based Constrained Policy
Search (DiffCPS)}$, which tackles the diffusion-based constrained policy search
without resorting to AWR. The theoretical analysis reveals our key insights by
leveraging the action distribution of the diffusion model to eliminate the
policy distribution constraint in the CPS and then utilizing the Evidence Lower
Bound (ELBO) of diffusion-based policy to approximate the KL constraint.
Consequently, DiffCPS admits the high expressivity of diffusion models while
circumventing the cumbersome density calculation brought by AWR. Extensive
experimental results based on the D4RL benchmark demonstrate the efficacy of
our approach. We empirically show that DiffCPS achieves better or at least
competitive performance compared to traditional AWR-based baselines as well as
recent diffusion-based offline RL methods. The code is now available at
$\href{https://github.com/felix-thu/DiffCPS}{https://github.com/felix-thu/DiffCPS}$. | [
"Longxiang He",
"Linrui Zhang",
"Junbo Tan",
"Xueqian Wang"
] | 2023-10-09 01:29:17 | http://arxiv.org/abs/2310.05333v1 | http://arxiv.org/pdf/2310.05333v1 | 2310.05333v1 |
Unlearning with Fisher Masking | Machine unlearning aims to revoke some training data after learning in
response to requests from users, model developers, and administrators. Most
previous methods are based on direct fine-tuning, which may neither remove data
completely nor retain full performances on the remain data. In this work, we
find that, by first masking some important parameters before fine-tuning, the
performances of unlearning could be significantly improved. We propose a new
masking strategy tailored to unlearning based on Fisher information.
Experiments on various datasets and network structures show the effectiveness
of the method: without any fine-tuning, the proposed Fisher masking could
unlearn almost completely while maintaining most of the performance on the
remain data. It also exhibits stronger stability compared to other unlearning
baselines | [
"Yufang Liu",
"Changzhi Sun",
"Yuanbin Wu",
"Aimin Zhou"
] | 2023-10-09 01:24:06 | http://arxiv.org/abs/2310.05331v1 | http://arxiv.org/pdf/2310.05331v1 | 2310.05331v1 |
Provable Compositional Generalization for Object-Centric Learning | Learning representations that generalize to novel compositions of known
concepts is crucial for bridging the gap between human and machine perception.
One prominent effort is learning object-centric representations, which are
widely conjectured to enable compositional generalization. Yet, it remains
unclear when this conjecture will be true, as a principled theoretical or
empirical understanding of compositional generalization is lacking. In this
work, we investigate when compositional generalization is guaranteed for
object-centric representations through the lens of identifiability theory. We
show that autoencoders that satisfy structural assumptions on the decoder and
enforce encoder-decoder consistency will learn object-centric representations
that provably generalize compositionally. We validate our theoretical result
and highlight the practical relevance of our assumptions through experiments on
synthetic image data. | [
"Thaddäus Wiedemer",
"Jack Brady",
"Alexander Panfilov",
"Attila Juhos",
"Matthias Bethge",
"Wieland Brendel"
] | 2023-10-09 01:18:07 | http://arxiv.org/abs/2310.05327v1 | http://arxiv.org/pdf/2310.05327v1 | 2310.05327v1 |
Increasing Entropy to Boost Policy Gradient Performance on Personalization Tasks | In this effort, we consider the impact of regularization on the diversity of
actions taken by policies generated from reinforcement learning agents trained
using a policy gradient. Policy gradient agents are prone to entropy collapse,
which means certain actions are seldomly, if ever, selected. We augment the
optimization objective function for the policy with terms constructed from
various $\varphi$-divergences and Maximum Mean Discrepancy which encourages
current policies to follow different state visitation and/or action choice
distribution than previously computed policies. We provide numerical
experiments using MNIST, CIFAR10, and Spotify datasets. The results demonstrate
the advantage of diversity-promoting policy regularization and that its use on
gradient-based approaches have significantly improved performance on a variety
of personalization tasks. Furthermore, numerical evidence is given to show that
policy regularization increases performance without losing accuracy. | [
"Andrew Starnes",
"Anton Dereventsov",
"Clayton Webster"
] | 2023-10-09 01:03:05 | http://arxiv.org/abs/2310.05324v1 | http://arxiv.org/pdf/2310.05324v1 | 2310.05324v1 |
Optimizing Solution-Samplers for Combinatorial Problems: The Landscape of Policy-Gradient Methods | Deep Neural Networks and Reinforcement Learning methods have empirically
shown great promise in tackling challenging combinatorial problems. In those
methods a deep neural network is used as a solution generator which is then
trained by gradient-based methods (e.g., policy gradient) to successively
obtain better solution distributions. In this work we introduce a novel
theoretical framework for analyzing the effectiveness of such methods. We ask
whether there exist generative models that (i) are expressive enough to
generate approximately optimal solutions; (ii) have a tractable, i.e,
polynomial in the size of the input, number of parameters; (iii) their
optimization landscape is benign in the sense that it does not contain
sub-optimal stationary points. Our main contribution is a positive answer to
this question. Our result holds for a broad class of combinatorial problems
including Max- and Min-Cut, Max-$k$-CSP, Maximum-Weight-Bipartite-Matching, and
the Traveling Salesman Problem. As a byproduct of our analysis we introduce a
novel regularization process over vanilla gradient descent and provide
theoretical and experimental evidence that it helps address vanishing-gradient
issues and escape bad stationary points. | [
"Constantine Caramanis",
"Dimitris Fotakis",
"Alkis Kalavasis",
"Vasilis Kontonis",
"Christos Tzamos"
] | 2023-10-08 23:39:38 | http://arxiv.org/abs/2310.05309v1 | http://arxiv.org/pdf/2310.05309v1 | 2310.05309v1 |
Adversarial Attacks on Combinatorial Multi-Armed Bandits | We study reward poisoning attacks on Combinatorial Multi-armed Bandits
(CMAB). We first provide a sufficient and necessary condition for the
attackability of CMAB, which depends on the intrinsic properties of the
corresponding CMAB instance such as the reward distributions of super arms and
outcome distributions of base arms. Additionally, we devise an attack algorithm
for attackable CMAB instances. Contrary to prior understanding of multi-armed
bandits, our work reveals a surprising fact that the attackability of a
specific CMAB instance also depends on whether the bandit instance is known or
unknown to the adversary. This finding indicates that adversarial attacks on
CMAB are difficult in practice and a general attack strategy for any CMAB
instance does not exist since the environment is mostly unknown to the
adversary. We validate our theoretical findings via extensive experiments on
real-world CMAB applications including probabilistic maximum covering problem,
online minimum spanning tree, cascading bandits for online ranking, and online
shortest path. | [
"Rishab Balasubramanian",
"Jiawei Li",
"Prasad Tadepalli",
"Huazheng Wang",
"Qingyun Wu",
"Haoyu Zhao"
] | 2023-10-08 23:22:36 | http://arxiv.org/abs/2310.05308v1 | http://arxiv.org/pdf/2310.05308v1 | 2310.05308v1 |
Successive Data Injection in Conditional Quantum GAN Applied to Time Series Anomaly Detection | Classical GAN architectures have shown interesting results for solving
anomaly detection problems in general and for time series anomalies in
particular, such as those arising in communication networks. In recent years,
several quantum GAN architectures have been proposed in the literature. When
detecting anomalies in time series using QGANs, huge challenges arise due to
the limited number of qubits compared to the size of the data. To address these
challenges, we propose a new high-dimensional encoding approach, named
Successive Data Injection (SuDaI). In this approach, we explore a larger
portion of the quantum state than that in the conventional angle encoding, the
method used predominantly in the literature, through repeated data injections
into the quantum state. SuDaI encoding allows us to adapt the QGAN for anomaly
detection with network data of a much higher dimensionality than with the
existing known QGANs implementations. In addition, SuDaI encoding applies to
other types of high-dimensional time series and can be used in contexts beyond
anomaly detection and QGANs, opening up therefore multiple fields of
application. | [
"Benjamin Kalfon",
"Soumaya Cherkaoui",
"Jean-Frédéric Laprade",
"Ola Ahmad",
"Shengrui Wang"
] | 2023-10-08 22:58:44 | http://arxiv.org/abs/2310.05307v1 | http://arxiv.org/pdf/2310.05307v1 | 2310.05307v1 |
Progressive Neural Compression for Adaptive Image Offloading under Timing Constraints | IoT devices are increasingly the source of data for machine learning (ML)
applications running on edge servers. Data transmissions from devices to
servers are often over local wireless networks whose bandwidth is not just
limited but, more importantly, variable. Furthermore, in cyber-physical systems
interacting with the physical environment, image offloading is also commonly
subject to timing constraints. It is, therefore, important to develop an
adaptive approach that maximizes the inference performance of ML applications
under timing constraints and the resource constraints of IoT devices. In this
paper, we use image classification as our target application and propose
progressive neural compression (PNC) as an efficient solution to this problem.
Although neural compression has been used to compress images for different ML
applications, existing solutions often produce fixed-size outputs that are
unsuitable for timing-constrained offloading over variable bandwidth. To
address this limitation, we train a multi-objective rateless autoencoder that
optimizes for multiple compression rates via stochastic taildrop to create a
compression solution that produces features ordered according to their
importance to inference performance. Features are then transmitted in that
order based on available bandwidth, with classification ultimately performed
using the (sub)set of features received by the deadline. We demonstrate the
benefits of PNC over state-of-the-art neural compression approaches and
traditional compression methods on a testbed comprising an IoT device and an
edge server connected over a wireless network with varying bandwidth. | [
"Ruiqi Wang",
"Hanyang Liu",
"Jiaming Qiu",
"Moran Xu",
"Roch Guerin",
"Chenyang Lu"
] | 2023-10-08 22:58:31 | http://arxiv.org/abs/2310.05306v1 | http://arxiv.org/pdf/2310.05306v1 | 2310.05306v1 |
Image Compression and Decompression Framework Based on Latent Diffusion Model for Breast Mammography | This research presents a novel framework for the compression and
decompression of medical images utilizing the Latent Diffusion Model (LDM). The
LDM represents advancement over the denoising diffusion probabilistic model
(DDPM) with a potential to yield superior image quality while requiring fewer
computational resources in the image decompression process. A possible
application of LDM and Torchvision for image upscaling has been explored using
medical image data, serving as an alternative to traditional image compression
and decompression algorithms. The experimental outcomes demonstrate that this
approach surpasses a conventional file compression algorithm, and convolutional
neural network (CNN) models trained with decompressed files perform comparably
to those trained with original image files. This approach also significantly
reduces dataset size so that it can be distributed with a smaller size, and
medical images take up much less space in medical devices. The research
implications extend to noise reduction in lossy compression algorithms and
substitute for complex wavelet-based lossless algorithms. | [
"InChan Hwang",
"MinJae Woo"
] | 2023-10-08 22:08:59 | http://arxiv.org/abs/2310.05299v1 | http://arxiv.org/pdf/2310.05299v1 | 2310.05299v1 |
Tailoring Self-Attention for Graph via Rooted Subtrees | Attention mechanisms have made significant strides in graph learning, yet
they still exhibit notable limitations: local attention faces challenges in
capturing long-range information due to the inherent problems of the
message-passing scheme, while global attention cannot reflect the hierarchical
neighborhood structure and fails to capture fine-grained local information. In
this paper, we propose a novel multi-hop graph attention mechanism, named
Subtree Attention (STA), to address the aforementioned issues. STA seamlessly
bridges the fully-attentional structure and the rooted subtree, with
theoretical proof that STA approximates the global attention under extreme
settings. By allowing direct computation of attention weights among multi-hop
neighbors, STA mitigates the inherent problems in existing graph attention
mechanisms. Further we devise an efficient form for STA by employing kernelized
softmax, which yields a linear time complexity. Our resulting GNN architecture,
the STAGNN, presents a simple yet performant STA-based graph neural network
leveraging a hop-aware attention strategy. Comprehensive evaluations on ten
node classification datasets demonstrate that STA-based models outperform
existing graph transformers and mainstream GNNs. The code is available at
https://github.com/LUMIA-Group/SubTree-Attention. | [
"Siyuan Huang",
"Yunchong Song",
"Jiayue Zhou",
"Zhouhan Lin"
] | 2023-10-08 21:47:18 | http://arxiv.org/abs/2310.05296v1 | http://arxiv.org/pdf/2310.05296v1 | 2310.05296v1 |
Clustering Three-Way Data with Outliers | Matrix-variate distributions are a recent addition to the model-based
clustering field, thereby making it possible to analyze data in matrix form
with complex structure such as images and time series. Due to its recent
appearance, there is limited literature on matrix-variate data, with even less
on dealing with outliers in these models. An approach for clustering
matrix-variate normal data with outliers is discussed. The approach, which uses
the distribution of subset log-likelihoods, extends the OCLUST algorithm to
matrix-variate normal data and uses an iterative approach to detect and trim
outliers. | [
"Katharine M. Clark",
"Paul D. McNicholas"
] | 2023-10-08 21:27:29 | http://arxiv.org/abs/2310.05288v2 | http://arxiv.org/pdf/2310.05288v2 | 2310.05288v2 |
Generalizable Error Modeling for Search Relevance Data Annotation Tasks | Human data annotation is critical in shaping the quality of machine learning
(ML) and artificial intelligence (AI) systems. One significant challenge in
this context is posed by annotation errors, as their effects can degrade the
performance of ML models. This paper presents a predictive error model trained
to detect potential errors in search relevance annotation tasks for three
industry-scale ML applications (music streaming, video streaming, and mobile
apps) and assesses its potential to enhance the quality and efficiency of the
data annotation process. Drawing on real-world data from an extensive search
relevance annotation program, we illustrate that errors can be predicted with
moderate model performance (AUC=0.65-0.75) and that model performance
generalizes well across applications (i.e., a global, task-agnostic model
performs on par with task-specific models). We present model explainability
analyses to identify which types of features are the main drivers of predictive
performance. Additionally, we demonstrate the usefulness of the model in the
context of auditing, where prioritizing tasks with high predicted error
probabilities considerably increases the amount of corrected annotation errors
(e.g., 40% efficiency gains for the music streaming application). These results
underscore that automated error detection models can yield considerable
improvements in the efficiency and quality of data annotation processes. Thus,
our findings reveal critical insights into effective error management in the
data annotation process, thereby contributing to the broader field of
human-in-the-loop ML. | [
"Heinrich Peters",
"Alireza Hashemi",
"James Rae"
] | 2023-10-08 21:21:19 | http://arxiv.org/abs/2310.05286v1 | http://arxiv.org/pdf/2310.05286v1 | 2310.05286v1 |
Learning force laws in many-body systems | Scientific laws describing natural systems may be more complex than our
intuition can handle, and thus how we discover laws must change. Machine
learning (ML) models can analyze large quantities of data, but their structure
should match the underlying physical constraints to provide useful insight.
Here we demonstrate a ML approach that incorporates such physical intuition to
infer force laws in dusty plasma experiments. Trained on 3D particle
trajectories, the model accounts for inherent symmetries and non-identical
particles, accurately learns the effective non-reciprocal forces between
particles, and extracts each particle's mass and charge. The model's accuracy
(R^2 > 0.99) points to new physics in dusty plasma beyond the resolution of
current theories and demonstrates how ML-powered approaches can guide new
routes of scientific discovery in many-body systems. | [
"Wentao Yu",
"Eslam Abdelaleem",
"Ilya Nemenman",
"Justin C. Burton"
] | 2023-10-08 20:12:34 | http://arxiv.org/abs/2310.05273v1 | http://arxiv.org/pdf/2310.05273v1 | 2310.05273v1 |
Federated Learning: A Cutting-Edge Survey of the Latest Advancements and Applications | In the realm of machine learning (ML) systems featuring client-host
connections, the enhancement of privacy security can be effectively achieved
through federated learning (FL) as a secure distributed ML methodology. FL
effectively integrates cloud infrastructure to transfer ML models onto edge
servers using blockchain technology. Through this mechanism, it guarantees the
streamlined processing and data storage requirements of both centralized and
decentralized systems, with an emphasis on scalability, privacy considerations,
and cost-effective communication. In current FL implementations, data owners
locally train their models, and subsequently upload the outcomes in the form of
weights, gradients, and parameters to the cloud for overall model aggregation.
This innovation obviates the necessity of engaging Internet of Things (IoT)
clients and participants to communicate raw and potentially confidential data
directly with a cloud center. This not only reduces the costs associated with
communication networks but also enhances the protection of private data. This
survey conducts an analysis and comparison of recent FL applications, aiming to
assess their efficiency, accuracy, and privacy protection. However, in light of
the complex and evolving nature of FL, it becomes evident that additional
research is imperative to address lingering knowledge gaps and effectively
confront the forthcoming challenges in this field. In this study, we categorize
recent literature into the following clusters: privacy protection, resource
allocation, case study analysis, and applications. Furthermore, at the end of
each section, we tabulate the open areas and future directions presented in the
referenced literature, affording researchers and scholars an insightful view of
the evolution of the field. | [
"Azim Akhtarshenas",
"Mohammad Ali Vahedifar",
"Navid Ayoobi",
"Behrouz Maham",
"Tohid Alizadeh",
"Sina Ebrahimi"
] | 2023-10-08 19:54:26 | http://arxiv.org/abs/2310.05269v2 | http://arxiv.org/pdf/2310.05269v2 | 2310.05269v2 |
The Emergence of Reproducibility and Consistency in Diffusion Models | Recently, diffusion models have emerged as powerful deep generative models,
showcasing cutting-edge performance across various applications such as image
generation, solving inverse problems, and text-to-image synthesis. These models
generate new data (e.g., images) by transforming random noise inputs through a
reverse diffusion process. In this work, we uncover a distinct and prevalent
phenomenon within diffusion models in contrast to most other generative models,
which we refer to as ``consistent model reproducibility''. To elaborate, our
extensive experiments have consistently shown that when starting with the same
initial noise input and sampling with a deterministic solver, diffusion models
tend to produce nearly identical output content. This consistency holds true
regardless of the choices of model architectures and training procedures.
Additionally, our research has unveiled that this exceptional model
reproducibility manifests in two distinct training regimes: (i) ``memorization
regime,'' characterized by a significantly overparameterized model which
attains reproducibility mainly by memorizing the training data; (ii)
``generalization regime,'' in which the model is trained on an extensive
dataset, and its reproducibility emerges with the model's generalization
capabilities. Our analysis provides theoretical justification for the model
reproducibility in ``memorization regime''. Moreover, our research reveals that
this valuable property generalizes to many variants of diffusion models,
including conditional diffusion models, diffusion models for solving inverse
problems, and fine-tuned diffusion models. A deeper understanding of this
phenomenon has the potential to yield more interpretable and controllable data
generative processes based on diffusion models. | [
"Huijie Zhang",
"Jinfan Zhou",
"Yifu Lu",
"Minzhe Guo",
"Liyue Shen",
"Qing Qu"
] | 2023-10-08 19:02:46 | http://arxiv.org/abs/2310.05264v1 | http://arxiv.org/pdf/2310.05264v1 | 2310.05264v1 |
Subsets and Splits
No saved queries yet
Save your SQL queries to embed, download, and access them later. Queries will appear here once saved.