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A Lightweight, Rapid and Efficient Deep Convolutional Network for Chest X-Ray Tuberculosis Detection | Tuberculosis (TB) is still recognized as one of the leading causes of death
worldwide. Recent advances in deep learning (DL) have shown to enhance
radiologists' ability to interpret chest X-ray (CXR) images accurately and with
fewer errors, leading to a better diagnosis of this disease. However, little
work has been done to develop models capable of diagnosing TB that offer good
performance while being efficient, fast and computationally inexpensive. In
this work, we propose LightTBNet, a novel lightweight, fast and efficient deep
convolutional network specially customized to detect TB from CXR images. Using
a total of 800 frontal CXR images from two publicly available datasets, our
solution yielded an accuracy, F1 and area under the ROC curve (AUC) of 0.906,
0.907 and 0.961, respectively, on an independent test subset. The proposed
model demonstrates outstanding performance while delivering a rapid prediction,
with minimal computational and memory requirements, making it highly suitable
for deployment in handheld devices that can be used in low-resource areas with
high TB prevalence. Code publicly available at
https://github.com/dani-capellan/LightTBNet. | [
"Daniel Capellán-Martín",
"Juan J. Gómez-Valverde",
"David Bermejo-Peláez",
"María J. Ledesma-Carbayo"
] | 2023-09-05 11:30:38 | http://arxiv.org/abs/2309.02140v1 | http://arxiv.org/pdf/2309.02140v1 | 2309.02140v1 |
Generalized Simplicial Attention Neural Networks | The aim of this work is to introduce Generalized Simplicial Attention Neural
Networks (GSANs), i.e., novel neural architectures designed to process data
defined on simplicial complexes using masked self-attentional layers. Hinging
on topological signal processing principles, we devise a series of
self-attention schemes capable of processing data components defined at
different simplicial orders, such as nodes, edges, triangles, and beyond. These
schemes learn how to weight the neighborhoods of the given topological domain
in a task-oriented fashion, leveraging the interplay among simplices of
different orders through the Dirac operator and its Dirac decomposition. We
also theoretically establish that GSANs are permutation equivariant and
simplicial-aware. Finally, we illustrate how our approach compares favorably
with other methods when applied to several (inductive and transductive) tasks
such as trajectory prediction, missing data imputation, graph classification,
and simplex prediction. | [
"Claudio Battiloro",
"Lucia Testa",
"Lorenzo Giusti",
"Stefania Sardellitti",
"Paolo Di Lorenzo",
"Sergio Barbarossa"
] | 2023-09-05 11:29:25 | http://arxiv.org/abs/2309.02138v1 | http://arxiv.org/pdf/2309.02138v1 | 2309.02138v1 |
Asymmetric Momentum: A Rethinking of Gradient Descent | Through theoretical and experimental validation, unlike all existing adaptive
methods like Adam which penalize frequently-changing parameters and are only
applicable to sparse gradients, we propose the simplest SGD enhanced method,
Loss-Controlled Asymmetric Momentum(LCAM). By averaging the loss, we divide
training process into different loss phases and using different momentum. It
not only can accelerates slow-changing parameters for sparse gradients, similar
to adaptive optimizers, but also can choose to accelerates frequently-changing
parameters for non-sparse gradients, thus being adaptable to all types of
datasets. We reinterpret the machine learning training process through the
concepts of weight coupling and weight traction, and experimentally validate
that weights have directional specificity, which are correlated with the
specificity of the dataset. Thus interestingly, we observe that in non-sparse
gradients, frequently-changing parameters should actually be accelerated, which
is completely opposite to traditional adaptive perspectives. Compared to
traditional SGD with momentum, this algorithm separates the weights without
additional computational costs. It is noteworthy that this method relies on the
network's ability to extract complex features. We primarily use Wide Residual
Networks for our research, employing the classic datasets Cifar10 and Cifar100
to test the ability for feature separation and conclude phenomena that are much
more important than just accuracy rates. Finally, compared to classic SGD
tuning methods, while using WRN on these two datasets and with nearly half the
training epochs, we achieve equal or better test accuracy. | [
"Gongyue Zhang",
"Dinghuang Zhang",
"Shuwen Zhao",
"Donghan Liu",
"Carrie M. Toptan",
"Honghai Liu"
] | 2023-09-05 11:16:47 | http://arxiv.org/abs/2309.02130v2 | http://arxiv.org/pdf/2309.02130v2 | 2309.02130v2 |
Exploiting Spatial-temporal Data for Sleep Stage Classification via Hypergraph Learning | Sleep stage classification is crucial for detecting patients' health
conditions. Existing models, which mainly use Convolutional Neural Networks
(CNN) for modelling Euclidean data and Graph Convolution Networks (GNN) for
modelling non-Euclidean data, are unable to consider the heterogeneity and
interactivity of multimodal data as well as the spatial-temporal correlation
simultaneously, which hinders a further improvement of classification
performance. In this paper, we propose a dynamic learning framework STHL, which
introduces hypergraph to encode spatial-temporal data for sleep stage
classification. Hypergraphs can construct multi-modal/multi-type data instead
of using simple pairwise between two subjects. STHL creates spatial and
temporal hyperedges separately to build node correlations, then it conducts
type-specific hypergraph learning process to encode the attributes into the
embedding space. Extensive experiments show that our proposed STHL outperforms
the state-of-the-art models in sleep stage classification tasks. | [
"Yuze Liu",
"Ziming Zhao",
"Tiehua Zhang",
"Kang Wang",
"Xin Chen",
"Xiaowei Huang",
"Jun Yin",
"Zhishu Shen"
] | 2023-09-05 11:01:30 | http://arxiv.org/abs/2309.02124v1 | http://arxiv.org/pdf/2309.02124v1 | 2309.02124v1 |
Leveraging Label Information for Multimodal Emotion Recognition | Multimodal emotion recognition (MER) aims to detect the emotional status of a
given expression by combining the speech and text information. Intuitively,
label information should be capable of helping the model locate the salient
tokens/frames relevant to the specific emotion, which finally facilitates the
MER task. Inspired by this, we propose a novel approach for MER by leveraging
label information. Specifically, we first obtain the representative label
embeddings for both text and speech modalities, then learn the label-enhanced
text/speech representations for each utterance via label-token and label-frame
interactions. Finally, we devise a novel label-guided attentive fusion module
to fuse the label-aware text and speech representations for emotion
classification. Extensive experiments were conducted on the public IEMOCAP
dataset, and experimental results demonstrate that our proposed approach
outperforms existing baselines and achieves new state-of-the-art performance. | [
"Peiying Wang",
"Sunlu Zeng",
"Junqing Chen",
"Lu Fan",
"Meng Chen",
"Youzheng Wu",
"Xiaodong He"
] | 2023-09-05 10:26:32 | http://arxiv.org/abs/2309.02106v1 | http://arxiv.org/pdf/2309.02106v1 | 2309.02106v1 |
Iterative Superquadric Recomposition of 3D Objects from Multiple Views | Humans are good at recomposing novel objects, i.e. they can identify
commonalities between unknown objects from general structure to finer detail,
an ability difficult to replicate by machines. We propose a framework, ISCO, to
recompose an object using 3D superquadrics as semantic parts directly from 2D
views without training a model that uses 3D supervision. To achieve this, we
optimize the superquadric parameters that compose a specific instance of the
object, comparing its rendered 3D view and 2D image silhouette. Our ISCO
framework iteratively adds new superquadrics wherever the reconstruction error
is high, abstracting first coarse regions and then finer details of the target
object. With this simple coarse-to-fine inductive bias, ISCO provides
consistent superquadrics for related object parts, despite not having any
semantic supervision. Since ISCO does not train any neural network, it is also
inherently robust to out-of-distribution objects. Experiments show that,
compared to recent single instance superquadrics reconstruction approaches,
ISCO provides consistently more accurate 3D reconstructions, even from images
in the wild. Code available at https://github.com/ExplainableML/ISCO . | [
"Stephan Alaniz",
"Massimiliano Mancini",
"Zeynep Akata"
] | 2023-09-05 10:21:37 | http://arxiv.org/abs/2309.02102v1 | http://arxiv.org/pdf/2309.02102v1 | 2309.02102v1 |
TensorBank:Tensor Lakehouse for Foundation Model Training | Storing and streaming high dimensional data for foundation model training
became a critical requirement with the rise of foundation models beyond natural
language. In this paper we introduce TensorBank, a petabyte scale tensor
lakehouse capable of streaming tensors from Cloud Object Store (COS) to GPU
memory at wire speed based on complex relational queries. We use Hierarchical
Statistical Indices (HSI) for query acceleration. Our architecture allows to
directly address tensors on block level using HTTP range reads. Once in GPU
memory, data can be transformed using PyTorch transforms. We provide a generic
PyTorch dataset type with a corresponding dataset factory translating
relational queries and requested transformations as an instance. By making use
of the HSI, irrelevant blocks can be skipped without reading them as those
indices contain statistics on their content at different hierarchical
resolution levels. This is an opinionated architecture powered by open
standards and making heavy use of open-source technology. Although, hardened
for production use using geospatial-temporal data, this architecture
generalizes to other use case like computer vision, computational neuroscience,
biological sequence analysis and more. | [
"Romeo Kienzler",
"Benedikt Blumenstiel",
"Zoltan Arnold Nagy",
"S. Karthik Mukkavilli",
"Johannes Schmude",
"Marcus Freitag",
"Michael Behrendt",
"Daniel Salles Civitarese",
"Naomi Simumba",
"Daiki Kimura",
"Hendrik Hamann"
] | 2023-09-05 10:00:33 | http://arxiv.org/abs/2309.02094v2 | http://arxiv.org/pdf/2309.02094v2 | 2309.02094v2 |
Natural Example-Based Explainability: a Survey | Explainable Artificial Intelligence (XAI) has become increasingly significant
for improving the interpretability and trustworthiness of machine learning
models. While saliency maps have stolen the show for the last few years in the
XAI field, their ability to reflect models' internal processes has been
questioned. Although less in the spotlight, example-based XAI methods have
continued to improve. It encompasses methods that use examples as explanations
for a machine learning model's predictions. This aligns with the psychological
mechanisms of human reasoning and makes example-based explanations natural and
intuitive for users to understand. Indeed, humans learn and reason by forming
mental representations of concepts based on examples.
This paper provides an overview of the state-of-the-art in natural
example-based XAI, describing the pros and cons of each approach. A "natural"
example simply means that it is directly drawn from the training data without
involving any generative process. The exclusion of methods that require
generating examples is justified by the need for plausibility which is in some
regards required to gain a user's trust. Consequently, this paper will explore
the following family of methods: similar examples, counterfactual and
semi-factual, influential instances, prototypes, and concepts. In particular,
it will compare their semantic definition, their cognitive impact, and added
values. We hope it will encourage and facilitate future work on natural
example-based XAI. | [
"Antonin Poché",
"Lucas Hervier",
"Mohamed-Chafik Bakkay"
] | 2023-09-05 09:46:20 | http://arxiv.org/abs/2309.03234v1 | http://arxiv.org/pdf/2309.03234v1 | 2309.03234v1 |
An Efficient Approach to Unsupervised Out-of-Distribution Detection with Variational Autoencoders | This paper is concerned with deep generative models (DGMs) for unsupervised
out-of-distribution (OOD) detection. In particular, we focus on vanilla
Variational Autoencoders (VAE) that use a standard normal prior distribution
for the latent variables. These models have a smaller model size, enabling
faster training and inference, making them well-suited for resource-limited
applications compared to more complex DGMs. We propose a novel OOD score called
Error Reduction (ER) specifically designed for vanilla VAE. ER incorporate the
idea of reconstructing image inputs from their lossy counterparts and takes
into account the Kolmogorov complexity of the images. Experimental results on
diverse datasets demonstrate the superiority of our approach over baseline
methods. Our code is available at: https://github.com/ZJLAB-AMMI/VAE4OOD. | [
"Zezhen Zeng",
"Bin Liu"
] | 2023-09-05 09:42:15 | http://arxiv.org/abs/2309.02084v2 | http://arxiv.org/pdf/2309.02084v2 | 2309.02084v2 |
BeeTLe: A Framework for Linear B-Cell Epitope Prediction and Classification | The process of identifying and characterizing B-cell epitopes, which are the
portions of antigens recognized by antibodies, is important for our
understanding of the immune system, and for many applications including vaccine
development, therapeutics, and diagnostics. Computational epitope prediction is
challenging yet rewarding as it significantly reduces the time and cost of
laboratory work. Most of the existing tools do not have satisfactory
performance and only discriminate epitopes from non-epitopes. This paper
presents a new deep learning-based multi-task framework for linear B-cell
epitope prediction as well as antibody type-specific epitope classification.
Specifically, a sequenced-based neural network model using recurrent layers and
Transformer blocks is developed. We propose an amino acid encoding method based
on eigen decomposition to help the model learn the representations of epitopes.
We introduce modifications to standard cross-entropy loss functions by
extending a logit adjustment technique to cope with the class imbalance.
Experimental results on data curated from the largest public epitope database
demonstrate the validity of the proposed methods and the superior performance
compared to competing ones. | [
"Xiao Yuan"
] | 2023-09-05 09:18:29 | http://arxiv.org/abs/2309.02071v1 | http://arxiv.org/pdf/2309.02071v1 | 2309.02071v1 |
Efficiency is Not Enough: A Critical Perspective of Environmentally Sustainable AI | Artificial Intelligence (AI) is currently spearheaded by machine learning
(ML) methods such as deep learning (DL) which have accelerated progress on many
tasks thought to be out of reach of AI. These ML methods can often be compute
hungry, energy intensive, and result in significant carbon emissions, a known
driver of anthropogenic climate change. Additionally, the platforms on which ML
systems run are associated with environmental impacts including and beyond
carbon emissions. The solution lionized by both industry and the ML community
to improve the environmental sustainability of ML is to increase the efficiency
with which ML systems operate in terms of both compute and energy consumption.
In this perspective, we argue that efficiency alone is not enough to make ML as
a technology environmentally sustainable. We do so by presenting three high
level discrepancies between the effect of efficiency on the environmental
sustainability of ML when considering the many variables which it interacts
with. In doing so, we comprehensively demonstrate, at multiple levels of
granularity both technical and non-technical reasons, why efficiency is not
enough to fully remedy the environmental impacts of ML. Based on this, we
present and argue for systems thinking as a viable path towards improving the
environmental sustainability of ML holistically. | [
"Dustin Wright",
"Christian Igel",
"Gabrielle Samuel",
"Raghavendra Selvan"
] | 2023-09-05 09:07:24 | http://arxiv.org/abs/2309.02065v1 | http://arxiv.org/pdf/2309.02065v1 | 2309.02065v1 |
MvFS: Multi-view Feature Selection for Recommender System | Feature selection, which is a technique to select key features in recommender
systems, has received increasing research attention. Recently, Adaptive Feature
Selection (AdaFS) has shown remarkable performance by adaptively selecting
features for each data instance, considering that the importance of a given
feature field can vary significantly across data. However, this method still
has limitations in that its selection process could be easily biased to major
features that frequently occur. To address these problems, we propose
Multi-view Feature Selection (MvFS), which selects informative features for
each instance more effectively. Most importantly, MvFS employs a multi-view
network consisting of multiple sub-networks, each of which learns to measure
the feature importance of a part of data with different feature patterns. By
doing so, MvFS mitigates the bias problem towards dominant patterns and
promotes a more balanced feature selection process. Moreover, MvFS adopts an
effective importance score modeling strategy which is applied independently to
each field without incurring dependency among features. Experimental results on
real-world datasets demonstrate the effectiveness of MvFS compared to
state-of-the-art baselines. | [
"Youngjune Lee",
"Yeongjong Jeong",
"Keunchan Park",
"SeongKu Kang"
] | 2023-09-05 09:06:34 | http://arxiv.org/abs/2309.02064v2 | http://arxiv.org/pdf/2309.02064v2 | 2309.02064v2 |
No-Regret Caching with Noisy Request Estimates | Online learning algorithms have been successfully used to design caching
policies with regret guarantees. Existing algorithms assume that the cache
knows the exact request sequence, but this may not be feasible in high load
and/or memory-constrained scenarios, where the cache may have access only to
sampled requests or to approximate requests' counters. In this paper, we
propose the Noisy-Follow-the-Perturbed-Leader (NFPL) algorithm, a variant of
the classic Follow-the-Perturbed-Leader (FPL) when request estimates are noisy,
and we show that the proposed solution has sublinear regret under specific
conditions on the requests estimator. The experimental evaluation compares the
proposed solution against classic caching policies and validates the proposed
approach under both synthetic and real request traces. | [
"Younes Ben Mazziane",
"Francescomaria Faticanti",
"Giovanni Neglia",
"Sara Alouf"
] | 2023-09-05 08:57:35 | http://arxiv.org/abs/2309.02055v1 | http://arxiv.org/pdf/2309.02055v1 | 2309.02055v1 |
Model-agnostic network inference enhancement from noisy measurements via curriculum learning | Noise is a pervasive element within real-world measurement data,
significantly undermining the performance of network inference models. However,
the quest for a comprehensive enhancement framework capable of bolstering noise
resistance across a diverse array of network inference models has remained
elusive. Here, we present an elegant and efficient framework tailored to
amplify the capabilities of network inference models in the presence of noise.
Leveraging curriculum learning, we mitigate the deleterious impact of noisy
samples on network inference models. Our proposed framework is model-agnostic,
seamlessly integrable into a plethora of model-based and model-free network
inference methods. Notably, we utilize one model-based and three model-free
network inference methods as the foundation. Extensive experimentation across
various synthetic and real-world networks, encapsulating diverse nonlinear
dynamic processes, showcases substantial performance augmentation under varied
noise types, particularly thriving in scenarios enriched with clean samples.
This framework's adeptness in fortifying both model-free and model-based
network inference methodologies paves the avenue towards a comprehensive and
unified enhancement framework, encompassing the entire spectrum of network
inference models. Available Code: https://github.com/xiaoyuans/MANIE. | [
"Kai Wu",
"Yuanyuan Li",
"Jing Liu"
] | 2023-09-05 08:51:40 | http://arxiv.org/abs/2309.02050v1 | http://arxiv.org/pdf/2309.02050v1 | 2309.02050v1 |
Probabilistic Self-supervised Learning via Scoring Rules Minimization | In this paper, we propose a novel probabilistic self-supervised learning via
Scoring Rule Minimization (ProSMIN), which leverages the power of probabilistic
models to enhance representation quality and mitigate collapsing
representations. Our proposed approach involves two neural networks; the online
network and the target network, which collaborate and learn the diverse
distribution of representations from each other through knowledge distillation.
By presenting the input samples in two augmented formats, the online network is
trained to predict the target network representation of the same sample under a
different augmented view. The two networks are trained via our new loss
function based on proper scoring rules. We provide a theoretical justification
for ProSMIN's convergence, demonstrating the strict propriety of its modified
scoring rule. This insight validates the method's optimization process and
contributes to its robustness and effectiveness in improving representation
quality. We evaluate our probabilistic model on various downstream tasks, such
as in-distribution generalization, out-of-distribution detection, dataset
corruption, low-shot learning, and transfer learning. Our method achieves
superior accuracy and calibration, surpassing the self-supervised baseline in a
wide range of experiments on large-scale datasets like ImageNet-O and
ImageNet-C, ProSMIN demonstrates its scalability and real-world applicability. | [
"Amirhossein Vahidi",
"Simon Schoßer",
"Lisa Wimmer",
"Yawei Li",
"Bernd Bischl",
"Eyke Hüllermeier",
"Mina Rezaei"
] | 2023-09-05 08:48:25 | http://arxiv.org/abs/2309.02048v1 | http://arxiv.org/pdf/2309.02048v1 | 2309.02048v1 |
Enhance Multi-domain Sentiment Analysis of Review Texts through Prompting Strategies | Large Language Models (LLMs) have made significant strides in both scientific
research and practical applications. Existing studies have demonstrated the
state-of-the-art (SOTA) performance of LLMs in various natural language
processing tasks. However, the question of how to further enhance LLMs'
performance in specific task using prompting strategies remains a pivotal
concern. This paper explores the enhancement of LLMs' performance in sentiment
analysis through the application of prompting strategies. We formulate the
process of prompting for sentiment analysis tasks and introduce two novel
strategies tailored for sentiment analysis: RolePlaying (RP) prompting and
Chain-of-thought (CoT) prompting. Specifically, we also propose the RP-CoT
prompting strategy which is a combination of RP prompting and CoT prompting. We
conduct comparative experiments on three distinct domain datasets to evaluate
the effectiveness of the proposed sentiment analysis strategies. The results
demonstrate that the adoption of the proposed prompting strategies leads to a
increasing enhancement in sentiment analysis accuracy. Further, the CoT
prompting strategy exhibits a notable impact on implicit sentiment analysis,
with the RP-CoT prompting strategy delivering the most superior performance
among all strategies. | [
"Yajing Wang",
"Zongwei Luo"
] | 2023-09-05 08:44:23 | http://arxiv.org/abs/2309.02045v1 | http://arxiv.org/pdf/2309.02045v1 | 2309.02045v1 |
Diffusion Generative Inverse Design | Inverse design refers to the problem of optimizing the input of an objective
function in order to enact a target outcome. For many real-world engineering
problems, the objective function takes the form of a simulator that predicts
how the system state will evolve over time, and the design challenge is to
optimize the initial conditions that lead to a target outcome. Recent
developments in learned simulation have shown that graph neural networks (GNNs)
can be used for accurate, efficient, differentiable estimation of simulator
dynamics, and support high-quality design optimization with gradient- or
sampling-based optimization procedures. However, optimizing designs from
scratch requires many expensive model queries, and these procedures exhibit
basic failures on either non-convex or high-dimensional problems. In this work,
we show how denoising diffusion models (DDMs) can be used to solve inverse
design problems efficiently and propose a particle sampling algorithm for
further improving their efficiency. We perform experiments on a number of fluid
dynamics design challenges, and find that our approach substantially reduces
the number of calls to the simulator compared to standard techniques. | [
"Marin Vlastelica",
"Tatiana López-Guevara",
"Kelsey Allen",
"Peter Battaglia",
"Arnaud Doucet",
"Kimberley Stachenfeld"
] | 2023-09-05 08:32:07 | http://arxiv.org/abs/2309.02040v2 | http://arxiv.org/pdf/2309.02040v2 | 2309.02040v2 |
Data-Juicer: A One-Stop Data Processing System for Large Language Models | The immense evolution in Large Language Models (LLMs) has underscored the
importance of massive, heterogeneous, and high-quality data. A data recipe is a
mixture of data from different sources for training LLMs, which plays a vital
role in LLMs' performance. Existing open-source tools for LLM data processing
are mostly tailored for specific data recipes. To continuously uncover the
potential of LLMs, incorporate data from new sources, and improve LLMs'
performance, we build a new system named Data-Juicer, with which we can
efficiently generate diverse data recipes, explore different possibilities in
forming data mixtures, and evaluate their effects on model performance.
Different from traditional data-analytics pipelines, Data-Juicer faces some
unique challenges. Firstly, the possible data sources for forming data recipes
are truly heterogeneous and massive with various qualities. Secondly, it is
extremely expensive to precisely evaluate data recipes' impact on LLMs'
performance. Thirdly, the end users of Data-Juicer, model developers, need
sufficient flexibility to configure and evaluate different data recipes.
Data-Juicer features a fine-grained abstraction of pipelines for constructing
data recipes, with over 50 built-in operators for easy composition and
extension. By incorporating visualization and auto-evaluation capabilities,
Data-Juicer enables a timely feedback loop for both LLM pre-training and
fine-tuning. Further, Data-Juicer is optimized and integrated with ecosystems
for LLM training, evaluation, and distributed computing. The data recipes
derived with Data-Juicer gain notable improvements on state-of-the-art LLMs, by
up to 7.45% increase in averaged score across 16 LLM benchmarks and 17.5%
higher win rate in pair-wise GPT-4 evaluations. Our system, data recipes, and
tutorials are released, calling for broader data-centric research on training
and understanding LLMs. | [
"Daoyuan Chen",
"Yilun Huang",
"Zhijian Ma",
"Hesen Chen",
"Xuchen Pan",
"Ce Ge",
"Dawei Gao",
"Yuexiang Xie",
"Zhaoyang Liu",
"Jinyang Gao",
"Yaliang Li",
"Bolin Ding",
"Jingren Zhou"
] | 2023-09-05 08:22:07 | http://arxiv.org/abs/2309.02033v2 | http://arxiv.org/pdf/2309.02033v2 | 2309.02033v2 |
Non-Parametric Representation Learning with Kernels | Unsupervised and self-supervised representation learning has become popular
in recent years for learning useful features from unlabelled data.
Representation learning has been mostly developed in the neural network
literature, and other models for representation learning are surprisingly
unexplored. In this work, we introduce and analyze several kernel-based
representation learning approaches: Firstly, we define two kernel
Self-Supervised Learning (SSL) models using contrastive loss functions and
secondly, a Kernel Autoencoder (AE) model based on the idea of embedding and
reconstructing data. We argue that the classical representer theorems for
supervised kernel machines are not always applicable for (self-supervised)
representation learning, and present new representer theorems, which show that
the representations learned by our kernel models can be expressed in terms of
kernel matrices. We further derive generalisation error bounds for
representation learning with kernel SSL and AE, and empirically evaluate the
performance of these methods in both small data regimes as well as in
comparison with neural network based models. | [
"Pascal Esser",
"Maximilian Fleissner",
"Debarghya Ghoshdastidar"
] | 2023-09-05 08:14:25 | http://arxiv.org/abs/2309.02028v1 | http://arxiv.org/pdf/2309.02028v1 | 2309.02028v1 |
Granger Causal Inference in Multivariate Hawkes Processes by Minimum Message Length | Multivariate Hawkes processes (MHPs) are versatile probabilistic tools used
to model various real-life phenomena: earthquakes, operations on stock markets,
neuronal activity, virus propagation and many others. In this paper, we focus
on MHPs with exponential decay kernels and estimate connectivity graphs, which
represent the Granger causal relations between their components. We approach
this inference problem by proposing an optimization criterion and model
selection algorithm based on the minimum message length (MML) principle. MML
compares Granger causal models using the Occam's razor principle in the
following way: even when models have a comparable goodness-of-fit to the
observed data, the one generating the most concise explanation of the data is
preferred. While most of the state-of-art methods using lasso-type penalization
tend to overfitting in scenarios with short time horizons, the proposed
MML-based method achieves high F1 scores in these settings. We conduct a
numerical study comparing the proposed algorithm to other related classical and
state-of-art methods, where we achieve the highest F1 scores in specific sparse
graph settings. We illustrate the proposed method also on G7 sovereign bond
data and obtain causal connections, which are in agreement with the expert
knowledge available in the literature. | [
"Katerina Hlavackova-Schindler",
"Anna Melnykova",
"Irene Tubikanec"
] | 2023-09-05 08:13:34 | http://arxiv.org/abs/2309.02027v1 | http://arxiv.org/pdf/2309.02027v1 | 2309.02027v1 |
RDGSL: Dynamic Graph Representation Learning with Structure Learning | Temporal Graph Networks (TGNs) have shown remarkable performance in learning
representation for continuous-time dynamic graphs. However, real-world dynamic
graphs typically contain diverse and intricate noise. Noise can significantly
degrade the quality of representation generation, impeding the effectiveness of
TGNs in downstream tasks. Though structure learning is widely applied to
mitigate noise in static graphs, its adaptation to dynamic graph settings poses
two significant challenges. i) Noise dynamics. Existing structure learning
methods are ill-equipped to address the temporal aspect of noise, hampering
their effectiveness in such dynamic and ever-changing noise patterns. ii) More
severe noise. Noise may be introduced along with multiple interactions between
two nodes, leading to the re-pollution of these nodes and consequently causing
more severe noise compared to static graphs. In this paper, we present RDGSL, a
representation learning method in continuous-time dynamic graphs. Meanwhile, we
propose dynamic graph structure learning, a novel supervisory signal that
empowers RDGSL with the ability to effectively combat noise in dynamic graphs.
To address the noise dynamics issue, we introduce the Dynamic Graph Filter,
where we innovatively propose a dynamic noise function that dynamically
captures both current and historical noise, enabling us to assess the temporal
aspect of noise and generate a denoised graph. We further propose the Temporal
Embedding Learner to tackle the challenge of more severe noise, which utilizes
an attention mechanism to selectively turn a blind eye to noisy edges and hence
focus on normal edges, enhancing the expressiveness for representation
generation that remains resilient to noise. Our method demonstrates robustness
towards downstream tasks, resulting in up to 5.1% absolute AUC improvement in
evolving classification versus the second-best baseline. | [
"Siwei Zhang",
"Yun Xiong",
"Yao Zhang",
"Yiheng Sun",
"Xi Chen",
"Yizhu Jiao",
"Yangyong Zhu"
] | 2023-09-05 08:03:59 | http://arxiv.org/abs/2309.02025v1 | http://arxiv.org/pdf/2309.02025v1 | 2309.02025v1 |
Dynamic Early Exiting Predictive Coding Neural Networks | Internet of Things (IoT) sensors are nowadays heavily utilized in various
real-world applications ranging from wearables to smart buildings passing by
agrotechnology and health monitoring. With the huge amounts of data generated
by these tiny devices, Deep Learning (DL) models have been extensively used to
enhance them with intelligent processing. However, with the urge for smaller
and more accurate devices, DL models became too heavy to deploy. It is thus
necessary to incorporate the hardware's limited resources in the design
process. Therefore, inspired by the human brain known for its efficiency and
low power consumption, we propose a shallow bidirectional network based on
predictive coding theory and dynamic early exiting for halting further
computations when a performance threshold is surpassed. We achieve comparable
accuracy to VGG-16 in image classification on CIFAR-10 with fewer parameters
and less computational complexity. | [
"Alaa Zniber",
"Ouassim Karrakchou",
"Mounir Ghogho"
] | 2023-09-05 08:00:01 | http://arxiv.org/abs/2309.02022v1 | http://arxiv.org/pdf/2309.02022v1 | 2309.02022v1 |
PROMISE: Preconditioned Stochastic Optimization Methods by Incorporating Scalable Curvature Estimates | This paper introduces PROMISE ($\textbf{Pr}$econditioned Stochastic
$\textbf{O}$ptimization $\textbf{M}$ethods by $\textbf{I}$ncorporating
$\textbf{S}$calable Curvature $\textbf{E}$stimates), a suite of sketching-based
preconditioned stochastic gradient algorithms for solving large-scale convex
optimization problems arising in machine learning. PROMISE includes
preconditioned versions of SVRG, SAGA, and Katyusha; each algorithm comes with
a strong theoretical analysis and effective default hyperparameter values. In
contrast, traditional stochastic gradient methods require careful
hyperparameter tuning to succeed, and degrade in the presence of
ill-conditioning, a ubiquitous phenomenon in machine learning. Empirically, we
verify the superiority of the proposed algorithms by showing that, using
default hyperparameter values, they outperform or match popular tuned
stochastic gradient optimizers on a test bed of $51$ ridge and logistic
regression problems assembled from benchmark machine learning repositories. On
the theoretical side, this paper introduces the notion of quadratic regularity
in order to establish linear convergence of all proposed methods even when the
preconditioner is updated infrequently. The speed of linear convergence is
determined by the quadratic regularity ratio, which often provides a tighter
bound on the convergence rate compared to the condition number, both in theory
and in practice, and explains the fast global linear convergence of the
proposed methods. | [
"Zachary Frangella",
"Pratik Rathore",
"Shipu Zhao",
"Madeleine Udell"
] | 2023-09-05 07:49:10 | http://arxiv.org/abs/2309.02014v2 | http://arxiv.org/pdf/2309.02014v2 | 2309.02014v2 |
iLoRE: Dynamic Graph Representation with Instant Long-term Modeling and Re-occurrence Preservation | Continuous-time dynamic graph modeling is a crucial task for many real-world
applications, such as financial risk management and fraud detection. Though
existing dynamic graph modeling methods have achieved satisfactory results,
they still suffer from three key limitations, hindering their scalability and
further applicability. i) Indiscriminate updating. For incoming edges, existing
methods would indiscriminately deal with them, which may lead to more time
consumption and unexpected noisy information. ii) Ineffective node-wise
long-term modeling. They heavily rely on recurrent neural networks (RNNs) as a
backbone, which has been demonstrated to be incapable of fully capturing
node-wise long-term dependencies in event sequences. iii) Neglect of
re-occurrence patterns. Dynamic graphs involve the repeated occurrence of
neighbors that indicates their importance, which is disappointedly neglected by
existing methods. In this paper, we present iLoRE, a novel dynamic graph
modeling method with instant node-wise Long-term modeling and Re-occurrence
preservation. To overcome the indiscriminate updating issue, we introduce the
Adaptive Short-term Updater module that will automatically discard the useless
or noisy edges, ensuring iLoRE's effectiveness and instant ability. We further
propose the Long-term Updater to realize more effective node-wise long-term
modeling, where we innovatively propose the Identity Attention mechanism to
empower a Transformer-based updater, bypassing the limited effectiveness of
typical RNN-dominated designs. Finally, the crucial re-occurrence patterns are
also encoded into a graph module for informative representation learning, which
will further improve the expressiveness of our method. Our experimental results
on real-world datasets demonstrate the effectiveness of our iLoRE for dynamic
graph modeling. | [
"Siwei Zhang",
"Yun Xiong",
"Yao Zhang",
"Xixi Wu",
"Yiheng Sun",
"Jiawei Zhang"
] | 2023-09-05 07:48:52 | http://arxiv.org/abs/2309.02012v1 | http://arxiv.org/pdf/2309.02012v1 | 2309.02012v1 |
Representation Learning Dynamics of Self-Supervised Models | Self-Supervised Learning (SSL) is an important paradigm for learning
representations from unlabelled data, and SSL with neural networks has been
highly successful in practice. However current theoretical analysis of SSL is
mostly restricted to generalisation error bounds. In contrast, learning
dynamics often provide a precise characterisation of the behaviour of neural
networks based models but, so far, are mainly known in supervised settings. In
this paper, we study the learning dynamics of SSL models, specifically
representations obtained by minimising contrastive and non-contrastive losses.
We show that a naive extension of the dymanics of multivariate regression to
SSL leads to learning trivial scalar representations that demonstrates
dimension collapse in SSL. Consequently, we formulate SSL objectives with
orthogonality constraints on the weights, and derive the exact (network width
independent) learning dynamics of the SSL models trained using gradient descent
on the Grassmannian manifold. We also argue that the infinite width
approximation of SSL models significantly deviate from the neural tangent
kernel approximations of supervised models. We numerically illustrate the
validity of our theoretical findings, and discuss how the presented results
provide a framework for further theoretical analysis of contrastive and
non-contrastive SSL. | [
"Pascal Esser",
"Satyaki Mukherjee",
"Debarghya Ghoshdastidar"
] | 2023-09-05 07:48:45 | http://arxiv.org/abs/2309.02011v1 | http://arxiv.org/pdf/2309.02011v1 | 2309.02011v1 |
Establishing a real-time traffic alarm in the city of Valencia with Deep Learning | Urban traffic emissions represent a significant concern due to their
detrimental impacts on both public health and the environment. Consequently,
decision-makers have flagged their reduction as a crucial goal. In this study,
we first analyze the correlation between traffic flux and pollution in the city
of Valencia, Spain. Our results demonstrate that traffic has a significant
impact on the levels of certain pollutants (especially $\text{NO}_\text{x}$).
Secondly, we develop an alarm system to predict if a street is likely to
experience unusually high traffic in the next 30 minutes, using an independent
three-tier level for each street. To make the predictions, we use traffic data
updated every 10 minutes and Long Short-Term Memory (LSTM) neural networks. We
trained the LSTM using traffic data from 2018, and tested it using traffic data
from 2019. | [
"Miguel Folgado",
"Veronica Sanz",
"Johannes Hirn",
"Edgar Lorenzo-Saez",
"Javier Urchueguia"
] | 2023-09-05 07:47:43 | http://arxiv.org/abs/2309.02010v1 | http://arxiv.org/pdf/2309.02010v1 | 2309.02010v1 |
Aggregating Correlated Estimations with (Almost) no Training | Many decision problems cannot be solved exactly and use several estimation
algorithms that assign scores to the different available options. The
estimation errors can have various correlations, from low (e.g. between two
very different approaches) to high (e.g. when using a given algorithm with
different hyperparameters). Most aggregation rules would suffer from this
diversity of correlations. In this article, we propose different aggregation
rules that take correlations into account, and we compare them to naive rules
in various experiments based on synthetic data. Our results show that when
sufficient information is known about the correlations between errors, a
maximum likelihood aggregation should be preferred. Otherwise, typically with
limited training data, we recommend a method that we call Embedded Voting (EV). | [
"Theo Delemazure",
"François Durand",
"Fabien Mathieu"
] | 2023-09-05 07:39:19 | http://arxiv.org/abs/2309.02005v1 | http://arxiv.org/pdf/2309.02005v1 | 2309.02005v1 |
Analyzing domain shift when using additional data for the MICCAI KiTS23 Challenge | Using additional training data is known to improve the results, especially
for medical image 3D segmentation where there is a lack of training material
and the model needs to generalize well from few available data. However, the
new data could have been acquired using other instruments and preprocessed such
its distribution is significantly different from the original training data.
Therefore, we study techniques which ameliorate domain shift during training so
that the additional data becomes better usable for preprocessing and training
together with the original data. Our results show that transforming the
additional data using histogram matching has better results than using simple
normalization. | [
"George Stoica",
"Mihaela Breaban",
"Vlad Barbu"
] | 2023-09-05 07:31:22 | http://arxiv.org/abs/2309.02001v1 | http://arxiv.org/pdf/2309.02001v1 | 2309.02001v1 |
sasdim: self-adaptive noise scaling diffusion model for spatial time series imputation | Spatial time series imputation is critically important to many real
applications such as intelligent transportation and air quality monitoring.
Although recent transformer and diffusion model based approaches have achieved
significant performance gains compared with conventional statistic based
methods, spatial time series imputation still remains as a challenging issue
due to the complex spatio-temporal dependencies and the noise uncertainty of
the spatial time series data. Especially, recent diffusion process based models
may introduce random noise to the imputations, and thus cause negative impact
on the model performance. To this end, we propose a self-adaptive noise scaling
diffusion model named SaSDim to more effectively perform spatial time series
imputation. Specially, we propose a new loss function that can scale the noise
to the similar intensity, and propose the across spatial-temporal global
convolution module to more effectively capture the dynamic spatial-temporal
dependencies. Extensive experiments conducted on three real world datasets
verify the effectiveness of SaSDim by comparison with current state-of-the-art
baselines. | [
"Shunyang Zhang",
"Senzhang Wang",
"Xianzhen Tan",
"Ruochen Liu",
"Jian Zhang",
"Jianxin Wang"
] | 2023-09-05 06:51:39 | http://arxiv.org/abs/2309.01988v1 | http://arxiv.org/pdf/2309.01988v1 | 2309.01988v1 |
Retail store customer behavior analysis system: Design and Implementation | Understanding customer behavior in retail stores plays a crucial role in
improving customer satisfaction by adding personalized value to services.
Behavior analysis reveals both general and detailed patterns in the interaction
of customers with a store items and other people, providing store managers with
insight into customer preferences. Several solutions aim to utilize this data
by recognizing specific behaviors through statistical visualization. However,
current approaches are limited to the analysis of small customer behavior sets,
utilizing conventional methods to detect behaviors. They do not use deep
learning techniques such as deep neural networks, which are powerful methods in
the field of computer vision. Furthermore, these methods provide limited
figures when visualizing the behavioral data acquired by the system. In this
study, we propose a framework that includes three primary parts: mathematical
modeling of customer behaviors, behavior analysis using an efficient deep
learning based system, and individual and group behavior visualization. Each
module and the entire system were validated using data from actual situations
in a retail store. | [
"Tuan Dinh Nguyen",
"Keisuke Hihara",
"Tung Cao Hoang",
"Yumeka Utada",
"Akihiko Torii",
"Naoki Izumi",
"Nguyen Thanh Thuy",
"Long Quoc Tran"
] | 2023-09-05 06:26:57 | http://arxiv.org/abs/2309.03232v1 | http://arxiv.org/pdf/2309.03232v1 | 2309.03232v1 |
An LSTM-Based Predictive Monitoring Method for Data with Time-varying Variability | The recurrent neural network and its variants have shown great success in
processing sequences in recent years. However, this deep neural network has not
aroused much attention in anomaly detection through predictively process
monitoring. Furthermore, the traditional statistic models work on assumptions
and hypothesis tests, while neural network (NN) models do not need that many
assumptions. This flexibility enables NN models to work efficiently on data
with time-varying variability, a common inherent aspect of data in practice.
This paper explores the ability of the recurrent neural network structure to
monitor processes and proposes a control chart based on long short-term memory
(LSTM) prediction intervals for data with time-varying variability. The
simulation studies provide empirical evidence that the proposed model
outperforms other NN-based predictive monitoring methods for mean shift
detection. The proposed method is also applied to time series sensor data,
which confirms that the proposed method is an effective technique for detecting
abnormalities. | [
"Jiaqi Qiu",
"Yu Lin",
"Inez Zwetsloot"
] | 2023-09-05 06:13:09 | http://arxiv.org/abs/2309.01978v1 | http://arxiv.org/pdf/2309.01978v1 | 2309.01978v1 |
Linear Regression using Heterogeneous Data Batches | In many learning applications, data are collected from multiple sources, each
providing a \emph{batch} of samples that by itself is insufficient to learn its
input-output relationship. A common approach assumes that the sources fall in
one of several unknown subgroups, each with an unknown input distribution and
input-output relationship. We consider one of this setup's most fundamental and
important manifestations where the output is a noisy linear combination of the
inputs, and there are $k$ subgroups, each with its own regression vector. Prior
work~\cite{kong2020meta} showed that with abundant small-batches, the
regression vectors can be learned with only few, $\tilde\Omega( k^{3/2})$,
batches of medium-size with $\tilde\Omega(\sqrt k)$ samples each. However, the
paper requires that the input distribution for all $k$ subgroups be isotropic
Gaussian, and states that removing this assumption is an ``interesting and
challenging problem". We propose a novel gradient-based algorithm that improves
on the existing results in several ways. It extends the applicability of the
algorithm by: (1) allowing the subgroups' underlying input distributions to be
different, unknown, and heavy-tailed; (2) recovering all subgroups followed by
a significant proportion of batches even for infinite $k$; (3) removing the
separation requirement between the regression vectors; (4) reducing the number
of batches and allowing smaller batch sizes. | [
"Ayush Jain",
"Rajat Sen",
"Weihao Kong",
"Abhimanyu Das",
"Alon Orlitsky"
] | 2023-09-05 05:58:23 | http://arxiv.org/abs/2309.01973v1 | http://arxiv.org/pdf/2309.01973v1 | 2309.01973v1 |
AdaPlus: Integrating Nesterov Momentum and Precise Stepsize Adjustment on AdamW Basis | This paper proposes an efficient optimizer called AdaPlus which integrates
Nesterov momentum and precise stepsize adjustment on AdamW basis. AdaPlus
combines the advantages of AdamW, Nadam, and AdaBelief and, in particular, does
not introduce any extra hyper-parameters. We perform extensive experimental
evaluations on three machine learning tasks to validate the effectiveness of
AdaPlus. The experiment results validate that AdaPlus (i) is the best adaptive
method which performs most comparable with (even slightly better than) SGD with
momentum on image classification tasks and (ii) outperforms other
state-of-the-art optimizers on language modeling tasks and illustrates the
highest stability when training GANs. The experiment code of AdaPlus is
available at: https://github.com/guanleics/AdaPlus. | [
"Lei Guan"
] | 2023-09-05 05:39:44 | http://arxiv.org/abs/2309.01966v1 | http://arxiv.org/pdf/2309.01966v1 | 2309.01966v1 |
RADIO: Reference-Agnostic Dubbing Video Synthesis | One of the most challenging problems in audio-driven talking head generation
is achieving high-fidelity detail while ensuring precise synchronization. Given
only a single reference image, extracting meaningful identity attributes
becomes even more challenging, often causing the network to mirror the facial
and lip structures too closely. To address these issues, we introduce RADIO, a
framework engineered to yield high-quality dubbed videos regardless of the pose
or expression in reference images. The key is to modulate the decoder layers
using latent space composed of audio and reference features. Additionally, we
incorporate ViT blocks into the decoder to emphasize high-fidelity details,
especially in the lip region. Our experimental results demonstrate that RADIO
displays high synchronization without the loss of fidelity. Especially in harsh
scenarios where the reference frame deviates significantly from the ground
truth, our method outperforms state-of-the-art methods, highlighting its
robustness. Pre-trained model and codes will be made public after the review. | [
"Dongyeun Lee",
"Chaewon Kim",
"Sangjoon Yu",
"Jaejun Yoo",
"Gyeong-Moon Park"
] | 2023-09-05 04:56:18 | http://arxiv.org/abs/2309.01950v1 | http://arxiv.org/pdf/2309.01950v1 | 2309.01950v1 |
TODM: Train Once Deploy Many Efficient Supernet-Based RNN-T Compression For On-device ASR Models | Automatic Speech Recognition (ASR) models need to be optimized for specific
hardware before they can be deployed on devices. This can be done by tuning the
model's hyperparameters or exploring variations in its architecture.
Re-training and re-validating models after making these changes can be a
resource-intensive task. This paper presents TODM (Train Once Deploy Many), a
new approach to efficiently train many sizes of hardware-friendly on-device ASR
models with comparable GPU-hours to that of a single training job. TODM
leverages insights from prior work on Supernet, where Recurrent Neural Network
Transducer (RNN-T) models share weights within a Supernet. It reduces layer
sizes and widths of the Supernet to obtain subnetworks, making them smaller
models suitable for all hardware types. We introduce a novel combination of
three techniques to improve the outcomes of the TODM Supernet: adaptive
dropouts, an in-place Alpha-divergence knowledge distillation, and the use of
ScaledAdam optimizer. We validate our approach by comparing Supernet-trained
versus individually tuned Multi-Head State Space Model (MH-SSM) RNN-T using
LibriSpeech. Results demonstrate that our TODM Supernet either matches or
surpasses the performance of manually tuned models by up to a relative of 3%
better in word error rate (WER), while efficiently keeping the cost of training
many models at a small constant. | [
"Yuan Shangguan",
"Haichuan Yang",
"Danni Li",
"Chunyang Wu",
"Yassir Fathullah",
"Dilin Wang",
"Ayushi Dalmia",
"Raghuraman Krishnamoorthi",
"Ozlem Kalinli",
"Junteng Jia",
"Jay Mahadeokar",
"Xin Lei",
"Mike Seltzer",
"Vikas Chandra"
] | 2023-09-05 04:47:55 | http://arxiv.org/abs/2309.01947v1 | http://arxiv.org/pdf/2309.01947v1 | 2309.01947v1 |
OHQ: On-chip Hardware-aware Quantization | Quantization emerges as one of the most promising approaches for deploying
advanced deep models on resource-constrained hardware. Mixed-precision
quantization leverages multiple bit-width architectures to unleash the accuracy
and efficiency potential of quantized models. However, existing mixed-precision
quantization suffers exhaustive search space that causes immense computational
overhead. The quantization process thus relies on separate high-performance
devices rather than locally, which also leads to a significant gap between the
considered hardware metrics and the real deployment.In this paper, we propose
an On-chip Hardware-aware Quantization (OHQ) framework that performs
hardware-aware mixed-precision quantization without accessing online devices.
First, we construct the On-chip Quantization Awareness (OQA) pipeline, enabling
perceive the actual efficiency metrics of the quantization operator on the
hardware.Second, we propose Mask-guided Quantization Estimation (MQE) technique
to efficiently estimate the accuracy metrics of operators under the constraints
of on-chip-level computing power.By synthesizing network and hardware insights
through linear programming, we obtain optimized bit-width configurations.
Notably, the quantization process occurs on-chip entirely without any
additional computing devices and data access. We demonstrate accelerated
inference after quantization for various architectures and compression ratios,
achieving 70% and 73% accuracy for ResNet-18 and MobileNetV3, respectively. OHQ
improves latency by 15~30% compared to INT8 on deployment. | [
"Wei Huang",
"Haotong Qin",
"Yangdong Liu",
"Jingzhuo Liang",
"Yifu Ding",
"Ying Li",
"Xianglong Liu"
] | 2023-09-05 04:39:34 | http://arxiv.org/abs/2309.01945v1 | http://arxiv.org/pdf/2309.01945v1 | 2309.01945v1 |
Quantum-AI empowered Intelligent Surveillance: Advancing Public Safety Through Innovative Contraband Detection | Surveillance systems have emerged as crucial elements in upholding peace and
security in the modern world. Their ubiquity aids in monitoring suspicious
activities effectively. However, in densely populated environments, continuous
active monitoring becomes impractical, necessitating the development of
intelligent surveillance systems. AI integration in the surveillance domain was
a big revolution, however, speed issues have prevented its widespread
implementation in the field. It has been observed that quantum artificial
intelligence has led to a great breakthrough. Quantum artificial
intelligence-based surveillance systems have shown to be more accurate as well
as capable of performing well in real-time scenarios, which had never been seen
before. In this research, a RentinaNet model is integrated with Quantum CNN and
termed as Quantum-RetinaNet. By harnessing the Quantum capabilities of QCNN,
Quantum-RetinaNet strikes a balance between accuracy and speed. This innovative
integration positions it as a game-changer, addressing the challenges of active
monitoring in densely populated scenarios. As demand for efficient surveillance
solutions continues to grow, Quantum-RetinaNet offers a compelling alternative
to existing CNN models, upholding accuracy standards without sacrificing
real-time performance. The unique attributes of Quantum-RetinaNet have
far-reaching implications for the future of intelligent surveillance. With its
enhanced processing speed, it is poised to revolutionize the field, catering to
the pressing need for rapid yet precise monitoring. As Quantum-RetinaNet
becomes the new standard, it ensures public safety and security while pushing
the boundaries of AI in surveillance. | [
"Syed Atif Ali Shah",
"Nasir Algeelani",
"Najeeb Al-Sammarraie"
] | 2023-09-05 04:26:26 | http://arxiv.org/abs/2309.03231v1 | http://arxiv.org/pdf/2309.03231v1 | 2309.03231v1 |
Dynamic Brain Transformer with Multi-level Attention for Functional Brain Network Analysis | Recent neuroimaging studies have highlighted the importance of
network-centric brain analysis, particularly with functional magnetic resonance
imaging. The emergence of Deep Neural Networks has fostered a substantial
interest in predicting clinical outcomes and categorizing individuals based on
brain networks. However, the conventional approach involving static brain
network analysis offers limited potential in capturing the dynamism of brain
function. Although recent studies have attempted to harness dynamic brain
networks, their high dimensionality and complexity present substantial
challenges. This paper proposes a novel methodology, Dynamic bRAin Transformer
(DART), which combines static and dynamic brain networks for more effective and
nuanced brain function analysis. Our model uses the static brain network as a
baseline, integrating dynamic brain networks to enhance performance against
traditional methods. We innovatively employ attention mechanisms, enhancing
model explainability and exploiting the dynamic brain network's temporal
variations. The proposed approach offers a robust solution to the low
signal-to-noise ratio of blood-oxygen-level-dependent signals, a recurring
issue in direct DNN modeling. It also provides valuable insights into which
brain circuits or dynamic networks contribute more to final predictions. As
such, DRAT shows a promising direction in neuroimaging studies, contributing to
the comprehensive understanding of brain organization and the role of neural
circuits. | [
"Xuan Kan",
"Antonio Aodong Chen Gu",
"Hejie Cui",
"Ying Guo",
"Carl Yang"
] | 2023-09-05 04:17:37 | http://arxiv.org/abs/2309.01941v1 | http://arxiv.org/pdf/2309.01941v1 | 2309.01941v1 |
Developing A Fair Individualized Polysocial Risk Score (iPsRS) for Identifying Increased Social Risk of Hospitalizations in Patients with Type 2 Diabetes (T2D) | Background: Racial and ethnic minority groups and individuals facing social
disadvantages, which often stem from their social determinants of health
(SDoH), bear a disproportionate burden of type 2 diabetes (T2D) and its
complications. It is therefore crucial to implement effective social risk
management strategies at the point of care. Objective: To develop an EHR-based
machine learning (ML) analytical pipeline to identify the unmet social needs
associated with hospitalization risk in patients with T2D. Methods: We
identified 10,192 T2D patients from the EHR data (from 2012 to 2022) from the
University of Florida Health Integrated Data Repository, including contextual
SDoH (e.g., neighborhood deprivation) and individual-level SDoH (e.g., housing
stability). We developed an electronic health records (EHR)-based machine
learning (ML) analytic pipeline, namely individualized polysocial risk score
(iPsRS), to identify high social risk associated with hospitalizations in T2D
patients, along with explainable AI (XAI) techniques and fairness assessment
and optimization. Results: Our iPsRS achieved a C statistic of 0.72 in
predicting 1-year hospitalization after fairness optimization across
racial-ethnic groups. The iPsRS showed excellent utility for capturing
individuals at high hospitalization risk; the actual 1-year hospitalization
rate in the top 5% of iPsRS was ~13 times as high as the bottom decile.
Conclusion: Our ML pipeline iPsRS can fairly and accurately screen for patients
who have increased social risk leading to hospitalization in T2D patients. | [
"Yu Huang",
"Jingchuan Guo",
"William T Donahoo",
"Zhengkang Fan",
"Ying Lu",
"Wei-Han Chen",
"Huilin Tang",
"Lori Bilello",
"Elizabeth A Shenkman",
"Jiang Bian"
] | 2023-09-05 03:56:21 | http://arxiv.org/abs/2309.02467v1 | http://arxiv.org/pdf/2309.02467v1 | 2309.02467v1 |
Provably safe systems: the only path to controllable AGI | We describe a path to humanity safely thriving with powerful Artificial
General Intelligences (AGIs) by building them to provably satisfy
human-specified requirements. We argue that this will soon be technically
feasible using advanced AI for formal verification and mechanistic
interpretability. We further argue that it is the only path which guarantees
safe controlled AGI. We end with a list of challenge problems whose solution
would contribute to this positive outcome and invite readers to join in this
work. | [
"Max Tegmark",
"Steve Omohundro"
] | 2023-09-05 03:42:46 | http://arxiv.org/abs/2309.01933v1 | http://arxiv.org/pdf/2309.01933v1 | 2309.01933v1 |
Regret Analysis of Policy Gradient Algorithm for Infinite Horizon Average Reward Markov Decision Processes | In this paper, we consider an infinite horizon average reward Markov Decision
Process (MDP). Distinguishing itself from existing works within this context,
our approach harnesses the power of the general policy gradient-based
algorithm, liberating it from the constraints of assuming a linear MDP
structure. We propose a policy gradient-based algorithm and show its global
convergence property. We then prove that the proposed algorithm has
$\tilde{\mathcal{O}}({T}^{3/4})$ regret. Remarkably, this paper marks a
pioneering effort by presenting the first exploration into regret-bound
computation for the general parameterized policy gradient algorithm in the
context of average reward scenarios. | [
"Qinbo Bai",
"Washim Uddin Mondal",
"Vaneet Aggarwal"
] | 2023-09-05 03:22:46 | http://arxiv.org/abs/2309.01922v1 | http://arxiv.org/pdf/2309.01922v1 | 2309.01922v1 |
RoboAgent: Generalization and Efficiency in Robot Manipulation via Semantic Augmentations and Action Chunking | The grand aim of having a single robot that can manipulate arbitrary objects
in diverse settings is at odds with the paucity of robotics datasets. Acquiring
and growing such datasets is strenuous due to manual efforts, operational
costs, and safety challenges. A path toward such an universal agent would
require a structured framework capable of wide generalization but trained
within a reasonable data budget. In this paper, we develop an efficient system
(RoboAgent) for training universal agents capable of multi-task manipulation
skills using (a) semantic augmentations that can rapidly multiply existing
datasets and (b) action representations that can extract performant policies
with small yet diverse multi-modal datasets without overfitting. In addition,
reliable task conditioning and an expressive policy architecture enable our
agent to exhibit a diverse repertoire of skills in novel situations specified
using language commands. Using merely 7500 demonstrations, we are able to train
a single agent capable of 12 unique skills, and demonstrate its generalization
over 38 tasks spread across common daily activities in diverse kitchen scenes.
On average, RoboAgent outperforms prior methods by over 40% in unseen
situations while being more sample efficient and being amenable to capability
improvements and extensions through fine-tuning. Videos at
https://robopen.github.io/ | [
"Homanga Bharadhwaj",
"Jay Vakil",
"Mohit Sharma",
"Abhinav Gupta",
"Shubham Tulsiani",
"Vikash Kumar"
] | 2023-09-05 03:14:39 | http://arxiv.org/abs/2309.01918v1 | http://arxiv.org/pdf/2309.01918v1 | 2309.01918v1 |
A Survey on Physics Informed Reinforcement Learning: Review and Open Problems | The inclusion of physical information in machine learning frameworks has
revolutionized many application areas. This involves enhancing the learning
process by incorporating physical constraints and adhering to physical laws. In
this work we explore their utility for reinforcement learning applications. We
present a thorough review of the literature on incorporating physics
information, as known as physics priors, in reinforcement learning approaches,
commonly referred to as physics-informed reinforcement learning (PIRL). We
introduce a novel taxonomy with the reinforcement learning pipeline as the
backbone to classify existing works, compare and contrast them, and derive
crucial insights. Existing works are analyzed with regard to the
representation/ form of the governing physics modeled for integration, their
specific contribution to the typical reinforcement learning architecture, and
their connection to the underlying reinforcement learning pipeline stages. We
also identify core learning architectures and physics incorporation biases
(i.e., observational, inductive and learning) of existing PIRL approaches and
use them to further categorize the works for better understanding and
adaptation. By providing a comprehensive perspective on the implementation of
the physics-informed capability, the taxonomy presents a cohesive approach to
PIRL. It identifies the areas where this approach has been applied, as well as
the gaps and opportunities that exist. Additionally, the taxonomy sheds light
on unresolved issues and challenges, which can guide future research. This
nascent field holds great potential for enhancing reinforcement learning
algorithms by increasing their physical plausibility, precision, data
efficiency, and applicability in real-world scenarios. | [
"Chayan Banerjee",
"Kien Nguyen",
"Clinton Fookes",
"Maziar Raissi"
] | 2023-09-05 02:45:18 | http://arxiv.org/abs/2309.01909v1 | http://arxiv.org/pdf/2309.01909v1 | 2309.01909v1 |
Inferring Actual Treatment Pathways from Patient Records | Treatment pathways are step-by-step plans outlining the recommended medical
care for specific diseases; they get revised when different treatments are
found to improve patient outcomes. Examining health records is an important
part of this revision process, but inferring patients' actual treatments from
health data is challenging due to complex event-coding schemes and the absence
of pathway-related annotations. This study aims to infer the actual treatment
steps for a particular patient group from administrative health records (AHR) -
a common form of tabular healthcare data - and address several technique- and
methodology-based gaps in treatment pathway-inference research. We introduce
Defrag, a method for examining AHRs to infer the real-world treatment steps for
a particular patient group. Defrag learns the semantic and temporal meaning of
healthcare event sequences, allowing it to reliably infer treatment steps from
complex healthcare data. To our knowledge, Defrag is the first
pathway-inference method to utilise a neural network (NN), an approach made
possible by a novel, self-supervised learning objective. We also developed a
testing and validation framework for pathway inference, which we use to
characterise and evaluate Defrag's pathway inference ability and compare
against baselines. We demonstrate Defrag's effectiveness by identifying
best-practice pathway fragments for breast cancer, lung cancer, and melanoma in
public healthcare records. Additionally, we use synthetic data experiments to
demonstrate the characteristics of the Defrag method, and to compare Defrag to
several baselines where it significantly outperforms non-NN-based methods.
Defrag significantly outperforms several existing pathway-inference methods and
offers an innovative and effective approach for inferring treatment pathways
from AHRs. Open-source code is provided to encourage further research in this
area. | [
"Adrian Wilkins-Caruana",
"Madhushi Bandara",
"Katarzyna Musial",
"Daniel Catchpoole",
"Paul J. Kennedy"
] | 2023-09-05 02:15:08 | http://arxiv.org/abs/2309.01897v1 | http://arxiv.org/pdf/2309.01897v1 | 2309.01897v1 |
Extended Symmetry Preserving Attention Networks for LHC Analysis | Reconstructing unstable heavy particles requires sophisticated techniques to
sift through the large number of possible permutations for assignment of
detector objects to partons. An approach based on a generalized attention
mechanism, symmetry preserving attention networks (SPANet), has been previously
applied to top quark pair decays at the Large Hadron Collider, which produce
six hadronic jets. Here we extend the SPANet architecture to consider multiple
input streams, such as leptons, as well as global event features, such as the
missing transverse momentum. In addition, we provide regression and
classification outputs to supplement the parton assignment. We explore the
performance of the extended capability of SPANet in the context of
semi-leptonic decays of top quark pairs as well as top quark pairs produced in
association with a Higgs boson. We find significant improvements in the power
of three representative studies: search for ttH, measurement of the top quark
mass and a search for a heavy Z' decaying to top quark pairs. We present
ablation studies to provide insight on what the network has learned in each
case. | [
"Michael James Fenton",
"Alexander Shmakov",
"Hideki Okawa",
"Yuji Li",
"Ko-Yang Hsiao",
"Shih-Chieh Hsu",
"Daniel Whiteson",
"Pierre Baldi"
] | 2023-09-05 01:40:01 | http://arxiv.org/abs/2309.01886v1 | http://arxiv.org/pdf/2309.01886v1 | 2309.01886v1 |
QuantEase: Optimization-based Quantization for Language Models -- An Efficient and Intuitive Algorithm | With the rising popularity of Large Language Models (LLMs), there has been an
increasing interest in compression techniques that enable their efficient
deployment. This study focuses on the Post-Training Quantization (PTQ) of LLMs.
Drawing from recent advances, our work introduces QuantEase, a layer-wise
quantization framework where individual layers undergo separate quantization.
The problem is framed as a discrete-structured non-convex optimization,
prompting the development of algorithms rooted in Coordinate Descent (CD)
techniques. These CD-based methods provide high-quality solutions to the
complex non-convex layer-wise quantization problems. Notably, our CD-based
approach features straightforward updates, relying solely on matrix and vector
operations, circumventing the need for matrix inversion or decomposition. We
also explore an outlier-aware variant of our approach, allowing for retaining
significant weights (outliers) with complete precision. Our proposal attains
state-of-the-art performance in terms of perplexity and zero-shot accuracy in
empirical evaluations across various LLMs and datasets, with relative
improvements up to 15% over methods such as GPTQ. Particularly noteworthy is
our outlier-aware algorithm's capability to achieve near or sub-3-bit
quantization of LLMs with an acceptable drop in accuracy, obviating the need
for non-uniform quantization or grouping techniques, improving upon methods
such as SpQR by up to two times in terms of perplexity. | [
"Kayhan Behdin",
"Ayan Acharya",
"Aman Gupta",
"Sathiya Keerthi",
"Rahul Mazumder"
] | 2023-09-05 01:39:09 | http://arxiv.org/abs/2309.01885v1 | http://arxiv.org/pdf/2309.01885v1 | 2309.01885v1 |
Task Generalization with Stability Guarantees via Elastic Dynamical System Motion Policies | Dynamical System (DS) based Learning from Demonstration (LfD) allows learning
of reactive motion policies with stability and convergence guarantees from a
few trajectories. Yet, current DS learning techniques lack the flexibility to
generalize to new task instances as they ignore explicit task parameters that
inherently change the underlying trajectories. In this work, we propose
Elastic-DS, a novel DS learning, and generalization approach that embeds task
parameters into the Gaussian Mixture Model (GMM) based Linear Parameter Varying
(LPV) DS formulation. Central to our approach is the Elastic-GMM, a GMM
constrained to SE(3) task-relevant frames. Given a new task instance/context,
the Elastic-GMM is transformed with Laplacian Editing and used to re-estimate
the LPV-DS policy. Elastic-DS is compositional in nature and can be used to
construct flexible multi-step tasks. We showcase its strength on a myriad of
simulated and real-robot experiments while preserving desirable
control-theoretic guarantees. Supplementary videos can be found at
https://sites.google.com/view/elastic-ds | [
"Tianyu Li",
"Nadia Figueroa"
] | 2023-09-05 01:22:19 | http://arxiv.org/abs/2309.01884v1 | http://arxiv.org/pdf/2309.01884v1 | 2309.01884v1 |
Gradient Domain Diffusion Models for Image Synthesis | Diffusion models are getting popular in generative image and video synthesis.
However, due to the diffusion process, they require a large number of steps to
converge. To tackle this issue, in this paper, we propose to perform the
diffusion process in the gradient domain, where the convergence becomes faster.
There are two reasons. First, thanks to the Poisson equation, the gradient
domain is mathematically equivalent to the original image domain. Therefore,
each diffusion step in the image domain has a unique corresponding gradient
domain representation. Second, the gradient domain is much sparser than the
image domain. As a result, gradient domain diffusion models converge faster.
Several numerical experiments confirm that the gradient domain diffusion models
are more efficient than the original diffusion models. The proposed method can
be applied in a wide range of applications such as image processing, computer
vision and machine learning tasks. | [
"Yuanhao Gong"
] | 2023-09-05 00:58:17 | http://arxiv.org/abs/2309.01875v1 | http://arxiv.org/pdf/2309.01875v1 | 2309.01875v1 |
Efficient Query-Based Attack against ML-Based Android Malware Detection under Zero Knowledge Setting | The widespread adoption of the Android operating system has made malicious
Android applications an appealing target for attackers. Machine learning-based
(ML-based) Android malware detection (AMD) methods are crucial in addressing
this problem; however, their vulnerability to adversarial examples raises
concerns. Current attacks against ML-based AMD methods demonstrate remarkable
performance but rely on strong assumptions that may not be realistic in
real-world scenarios, e.g., the knowledge requirements about feature space,
model parameters, and training dataset. To address this limitation, we
introduce AdvDroidZero, an efficient query-based attack framework against
ML-based AMD methods that operates under the zero knowledge setting. Our
extensive evaluation shows that AdvDroidZero is effective against various
mainstream ML-based AMD methods, in particular, state-of-the-art such methods
and real-world antivirus solutions. | [
"Ping He",
"Yifan Xia",
"Xuhong Zhang",
"Shouling Ji"
] | 2023-09-05 00:14:12 | http://arxiv.org/abs/2309.01866v2 | http://arxiv.org/pdf/2309.01866v2 | 2309.01866v2 |
Attention-Driven Multi-Modal Fusion: Enhancing Sign Language Recognition and Translation | In this paper, we devise a mechanism for the addition of multi-modal
information with an existing pipeline for continuous sign language recognition
and translation. In our procedure, we have incorporated optical flow
information with RGB images to enrich the features with movement-related
information. This work studies the feasibility of such modality inclusion using
a cross-modal encoder. The plugin we have used is very lightweight and doesn't
need to include a separate feature extractor for the new modality in an
end-to-end manner. We have applied the changes in both sign language
recognition and translation, improving the result in each case. We have
evaluated the performance on the RWTH-PHOENIX-2014 dataset for sign language
recognition and the RWTH-PHOENIX-2014T dataset for translation. On the
recognition task, our approach reduced the WER by 0.9, and on the translation
task, our approach increased most of the BLEU scores by ~0.6 on the test set. | [
"Zaber Ibn Abdul Hakim",
"Rasman Mubtasim Swargo",
"Muhammad Abdullah Adnan"
] | 2023-09-04 23:31:29 | http://arxiv.org/abs/2309.01860v2 | http://arxiv.org/pdf/2309.01860v2 | 2309.01860v2 |
Efficient Defense Against Model Stealing Attacks on Convolutional Neural Networks | Model stealing attacks have become a serious concern for deep learning
models, where an attacker can steal a trained model by querying its black-box
API. This can lead to intellectual property theft and other security and
privacy risks. The current state-of-the-art defenses against model stealing
attacks suggest adding perturbations to the prediction probabilities. However,
they suffer from heavy computations and make impracticable assumptions about
the adversary. They often require the training of auxiliary models. This can be
time-consuming and resource-intensive which hinders the deployment of these
defenses in real-world applications. In this paper, we propose a simple yet
effective and efficient defense alternative. We introduce a heuristic approach
to perturb the output probabilities. The proposed defense can be easily
integrated into models without additional training. We show that our defense is
effective in defending against three state-of-the-art stealing attacks. We
evaluate our approach on large and quantized (i.e., compressed) Convolutional
Neural Networks (CNNs) trained on several vision datasets. Our technique
outperforms the state-of-the-art defenses with a $\times37$ faster inference
latency without requiring any additional model and with a low impact on the
model's performance. We validate that our defense is also effective for
quantized CNNs targeting edge devices. | [
"Kacem Khaled",
"Mouna Dhaouadi",
"Felipe Gohring de Magalhães",
"Gabriela Nicolescu"
] | 2023-09-04 22:25:49 | http://arxiv.org/abs/2309.01838v2 | http://arxiv.org/pdf/2309.01838v2 | 2309.01838v2 |
Delegating Data Collection in Decentralized Machine Learning | Motivated by the emergence of decentralized machine learning ecosystems, we
study the delegation of data collection. Taking the field of contract theory as
our starting point, we design optimal and near-optimal contracts that deal with
two fundamental machine learning challenges: lack of certainty in the
assessment of model quality and lack of knowledge regarding the optimal
performance of any model. We show that lack of certainty can be dealt with via
simple linear contracts that achieve 1-1/e fraction of the first-best utility,
even if the principal has a small test set. Furthermore, we give sufficient
conditions on the size of the principal's test set that achieves a vanishing
additive approximation to the optimal utility. To address the lack of a priori
knowledge regarding the optimal performance, we give a convex program that can
adaptively and efficiently compute the optimal contract. | [
"Nivasini Ananthakrishnan",
"Stephen Bates",
"Michael I. Jordan",
"Nika Haghtalab"
] | 2023-09-04 22:16:35 | http://arxiv.org/abs/2309.01837v1 | http://arxiv.org/pdf/2309.01837v1 | 2309.01837v1 |
Smoothing ADMM for Sparse-Penalized Quantile Regression with Non-Convex Penalties | This paper investigates quantile regression in the presence of non-convex and
non-smooth sparse penalties, such as the minimax concave penalty (MCP) and
smoothly clipped absolute deviation (SCAD). The non-smooth and non-convex
nature of these problems often leads to convergence difficulties for many
algorithms. While iterative techniques like coordinate descent and local linear
approximation can facilitate convergence, the process is often slow. This
sluggish pace is primarily due to the need to run these approximation
techniques until full convergence at each step, a requirement we term as a
\emph{secondary convergence iteration}. To accelerate the convergence speed, we
employ the alternating direction method of multipliers (ADMM) and introduce a
novel single-loop smoothing ADMM algorithm with an increasing penalty
parameter, named SIAD, specifically tailored for sparse-penalized quantile
regression. We first delve into the convergence properties of the proposed SIAD
algorithm and establish the necessary conditions for convergence.
Theoretically, we confirm a convergence rate of $o\big({k^{-\frac{1}{4}}}\big)$
for the sub-gradient bound of augmented Lagrangian. Subsequently, we provide
numerical results to showcase the effectiveness of the SIAD algorithm. Our
findings highlight that the SIAD method outperforms existing approaches,
providing a faster and more stable solution for sparse-penalized quantile
regression. | [
"Reza Mirzaeifard",
"Naveen K. D. Venkategowda",
"Vinay Chakravarthi Gogineni",
"Stefan Werner"
] | 2023-09-04 21:48:51 | http://arxiv.org/abs/2309.03094v1 | http://arxiv.org/pdf/2309.03094v1 | 2309.03094v1 |
Soft-Dropout: A Practical Approach for Mitigating Overfitting in Quantum Convolutional Neural Networks | Quantum convolutional neural network (QCNN), an early application for quantum
computers in the NISQ era, has been consistently proven successful as a machine
learning (ML) algorithm for several tasks with significant accuracy. Derived
from its classical counterpart, QCNN is prone to overfitting. Overfitting is a
typical shortcoming of ML models that are trained too closely to the availed
training dataset and perform relatively poorly on unseen datasets for a similar
problem. In this work we study the adaptation of one of the most successful
overfitting mitigation method, knows as the (post-training) dropout method, to
the quantum setting. We find that a straightforward implementation of this
method in the quantum setting leads to a significant and undesirable
consequence: a substantial decrease in success probability of the QCNN. We
argue that this effect exposes the crucial role of entanglement in QCNNs and
the vulnerability of QCNNs to entanglement loss. To handle overfitting, we
proposed a softer version of the dropout method. We find that the proposed
method allows us to handle successfully overfitting in the test cases. | [
"Aakash Ravindra Shinde",
"Charu Jain",
"Amir Kalev"
] | 2023-09-04 21:46:24 | http://arxiv.org/abs/2309.01829v1 | http://arxiv.org/pdf/2309.01829v1 | 2309.01829v1 |
Secure and Efficient Federated Learning in LEO Constellations using Decentralized Key Generation and On-Orbit Model Aggregation | Satellite technologies have advanced drastically in recent years, leading to
a heated interest in launching small satellites into low Earth orbit (LEOs) to
collect massive data such as satellite imagery. Downloading these data to a
ground station (GS) to perform centralized learning to build an AI model is not
practical due to the limited and expensive bandwidth. Federated learning (FL)
offers a potential solution but will incur a very large convergence delay due
to the highly sporadic and irregular connectivity between LEO satellites and
GS. In addition, there are significant security and privacy risks where
eavesdroppers or curious servers/satellites may infer raw data from satellites'
model parameters transmitted over insecure communication channels. To address
these issues, this paper proposes FedSecure, a secure FL approach designed for
LEO constellations, which consists of two novel components: (1) decentralized
key generation that protects satellite data privacy using a functional
encryption scheme, and (2) on-orbit model forwarding and aggregation that
generates a partial global model per orbit to minimize the idle waiting time
for invisible satellites to enter the visible zone of the GS. Our analysis and
results show that FedSecure preserves the privacy of each satellite's data
against eavesdroppers, a curious server, or curious satellites. It is
lightweight with significantly lower communication and computation overheads
than other privacy-preserving FL aggregation approaches. It also reduces
convergence delay drastically from days to only a few hours, yet achieving high
accuracy of up to 85.35% using realistic satellite images. | [
"Mohamed Elmahallawy",
"Tie Luo",
"Mohamed I. Ibrahem"
] | 2023-09-04 21:36:46 | http://arxiv.org/abs/2309.01828v1 | http://arxiv.org/pdf/2309.01828v1 | 2309.01828v1 |
LoopTune: Optimizing Tensor Computations with Reinforcement Learning | Advanced compiler technology is crucial for enabling machine learning
applications to run on novel hardware, but traditional compilers fail to
deliver performance, popular auto-tuners have long search times and
expert-optimized libraries introduce unsustainable costs. To address this, we
developed LoopTune, a deep reinforcement learning compiler that optimizes
tensor computations in deep learning models for the CPU. LoopTune optimizes
tensor traversal order while using the ultra-fast lightweight code generator
LoopNest to perform hardware-specific optimizations. With a novel graph-based
representation and action space, LoopTune speeds up LoopNest by 3.2x,
generating an order of magnitude faster code than TVM, 2.8x faster than
MetaSchedule, and 1.08x faster than AutoTVM, consistently performing at the
level of the hand-tuned library Numpy. Moreover, LoopTune tunes code in order
of seconds. | [
"Dejan Grubisic",
"Bram Wasti",
"Chris Cummins",
"John Mellor-Crummey",
"Aleksandar Zlateski"
] | 2023-09-04 21:30:15 | http://arxiv.org/abs/2309.01825v2 | http://arxiv.org/pdf/2309.01825v2 | 2309.01825v2 |
On the fly Deep Neural Network Optimization Control for Low-Power Computer Vision | Processing visual data on mobile devices has many applications, e.g.,
emergency response and tracking. State-of-the-art computer vision techniques
rely on large Deep Neural Networks (DNNs) that are usually too power-hungry to
be deployed on resource-constrained edge devices. Many techniques improve the
efficiency of DNNs by using sparsity or quantization. However, the accuracy and
efficiency of these techniques cannot be adapted for diverse edge applications
with different hardware constraints and accuracy requirements. This paper
presents a novel technique to allow DNNs to adapt their accuracy and energy
consumption during run-time, without the need for any re-training. Our
technique called AdaptiveActivation introduces a hyper-parameter that controls
the output range of the DNNs' activation function to dynamically adjust the
sparsity and precision in the DNN. AdaptiveActivation can be applied to any
existing pre-trained DNN to improve their deployability in diverse edge
environments. We conduct experiments on popular edge devices and show that the
accuracy is within 1.5% of the baseline. We also show that our approach
requires 10%--38% less memory than the baseline techniques leading to more
accuracy-efficiency tradeoff options | [
"Ishmeet Kaur",
"Adwaita Janardhan Jadhav"
] | 2023-09-04 21:26:26 | http://arxiv.org/abs/2309.01824v1 | http://arxiv.org/pdf/2309.01824v1 | 2309.01824v1 |
Towards Foundational AI Models for Additive Manufacturing: Language Models for G-Code Debugging, Manipulation, and Comprehension | 3D printing or additive manufacturing is a revolutionary technology that
enables the creation of physical objects from digital models. However, the
quality and accuracy of 3D printing depend on the correctness and efficiency of
the G-code, a low-level numerical control programming language that instructs
3D printers how to move and extrude material. Debugging G-code is a challenging
task that requires a syntactic and semantic understanding of the G-code format
and the geometry of the part to be printed. In this paper, we present the first
extensive evaluation of six state-of-the-art foundational large language models
(LLMs) for comprehending and debugging G-code files for 3D printing. We design
effective prompts to enable pre-trained LLMs to understand and manipulate
G-code and test their performance on various aspects of G-code debugging and
manipulation, including detection and correction of common errors and the
ability to perform geometric transformations. We analyze their strengths and
weaknesses for understanding complete G-code files. We also discuss the
implications and limitations of using LLMs for G-code comprehension. | [
"Anushrut Jignasu",
"Kelly Marshall",
"Baskar Ganapathysubramanian",
"Aditya Balu",
"Chinmay Hegde",
"Adarsh Krishnamurthy"
] | 2023-09-04 21:22:28 | http://arxiv.org/abs/2309.02465v1 | http://arxiv.org/pdf/2309.02465v1 | 2309.02465v1 |
Computation and Communication Efficient Federated Learning over Wireless Networks | Federated learning (FL) enables distributed learning across edge devices
while protecting data privacy. However, the learning accuracy decreases due to
the heterogeneity of devices' data, and the computation and communication
latency increase when updating large-scale learning models on devices with
limited computational capability and wireless resources. We consider a novel FL
framework with partial model pruning and personalization to overcome these
challenges. This framework splits the learning model into a global part with
model pruning shared with all devices to learn data representations and a
personalized part to be fine-tuned for a specific device, which adapts the
model size during FL to reduce both computation and communication latency and
increases the learning accuracy for the device with non-independent and
identically distributed (non-IID) data. Then, the computation and communication
latency and the convergence analysis of the proposed FL framework are
mathematically analyzed. To maximize the convergence rate and guarantee
learning accuracy, Karush Kuhn Tucker (KKT) conditions are deployed to jointly
optimize the pruning ratio and bandwidth allocation. Finally, experimental
results demonstrate that the proposed FL framework achieves a remarkable
reduction of approximately 50 percents computation and communication latency
compared with the scheme only with model personalization. | [
"Xiaonan Liu",
"Tharmalingam Ratnarajah"
] | 2023-09-04 21:10:45 | http://arxiv.org/abs/2309.01816v2 | http://arxiv.org/pdf/2309.01816v2 | 2309.01816v2 |
DiscoverPath: A Knowledge Refinement and Retrieval System for Interdisciplinarity on Biomedical Research | The exponential growth in scholarly publications necessitates advanced tools
for efficient article retrieval, especially in interdisciplinary fields where
diverse terminologies are used to describe similar research. Traditional
keyword-based search engines often fall short in assisting users who may not be
familiar with specific terminologies. To address this, we present a knowledge
graph-based paper search engine for biomedical research to enhance the user
experience in discovering relevant queries and articles. The system, dubbed
DiscoverPath, employs Named Entity Recognition (NER) and part-of-speech (POS)
tagging to extract terminologies and relationships from article abstracts to
create a KG. To reduce information overload, DiscoverPath presents users with a
focused subgraph containing the queried entity and its neighboring nodes and
incorporates a query recommendation system, enabling users to iteratively
refine their queries. The system is equipped with an accessible Graphical User
Interface that provides an intuitive visualization of the KG, query
recommendations, and detailed article information, enabling efficient article
retrieval, thus fostering interdisciplinary knowledge exploration. DiscoverPath
is open-sourced at https://github.com/ynchuang/DiscoverPath. | [
"Yu-Neng Chuang",
"Guanchu Wang",
"Chia-Yuan Chang",
"Kwei-Herng Lai",
"Daochen Zha",
"Ruixiang Tang",
"Fan Yang",
"Alfredo Costilla Reyes",
"Kaixiong Zhou",
"Xiaoqian Jiang",
"Xia Hu"
] | 2023-09-04 20:52:33 | http://arxiv.org/abs/2309.01808v2 | http://arxiv.org/pdf/2309.01808v2 | 2309.01808v2 |
Marginalized Importance Sampling for Off-Environment Policy Evaluation | Reinforcement Learning (RL) methods are typically sample-inefficient, making
it challenging to train and deploy RL-policies in real world robots. Even a
robust policy trained in simulation requires a real-world deployment to assess
their performance. This paper proposes a new approach to evaluate the
real-world performance of agent policies prior to deploying them in the real
world. Our approach incorporates a simulator along with real-world offline data
to evaluate the performance of any policy using the framework of Marginalized
Importance Sampling (MIS). Existing MIS methods face two challenges: (1) large
density ratios that deviate from a reasonable range and (2) indirect
supervision, where the ratio needs to be inferred indirectly, thus exacerbating
estimation error. Our approach addresses these challenges by introducing the
target policy's occupancy in the simulator as an intermediate variable and
learning the density ratio as the product of two terms that can be learned
separately. The first term is learned with direct supervision and the second
term has a small magnitude, thus making it computationally efficient. We
analyze the sample complexity as well as error propagation of our two
step-procedure. Furthermore, we empirically evaluate our approach on Sim2Sim
environments such as Cartpole, Reacher, and Half-Cheetah. Our results show that
our method generalizes well across a variety of Sim2Sim gap, target policies
and offline data collection policies. We also demonstrate the performance of
our algorithm on a Sim2Real task of validating the performance of a 7 DoF
robotic arm using offline data along with the Gazebo simulator. | [
"Pulkit Katdare",
"Nan Jiang",
"Katherine Driggs-Campbell"
] | 2023-09-04 20:52:04 | http://arxiv.org/abs/2309.01807v2 | http://arxiv.org/pdf/2309.01807v2 | 2309.01807v2 |
Asymmetric matrix sensing by gradient descent with small random initialization | We study matrix sensing, which is the problem of reconstructing a low-rank
matrix from a few linear measurements. It can be formulated as an
overparameterized regression problem, which can be solved by factorized
gradient descent when starting from a small random initialization.
Linear neural networks, and in particular matrix sensing by factorized
gradient descent, serve as prototypical models of non-convex problems in modern
machine learning, where complex phenomena can be disentangled and studied in
detail. Much research has been devoted to studying special cases of asymmetric
matrix sensing, such as asymmetric matrix factorization and symmetric positive
semi-definite matrix sensing.
Our key contribution is introducing a continuous differential equation that
we call the $\textit{perturbed gradient flow}$. We prove that the perturbed
gradient flow converges quickly to the true target matrix whenever the
perturbation is sufficiently bounded. The dynamics of gradient descent for
matrix sensing can be reduced to this formulation, yielding a novel proof of
asymmetric matrix sensing with factorized gradient descent. Compared to
directly analyzing the dynamics of gradient descent, the continuous formulation
allows bounding key quantities by considering their derivatives, often
simplifying the proofs. We believe the general proof technique may prove useful
in other settings as well. | [
"Johan S. Wind"
] | 2023-09-04 20:23:35 | http://arxiv.org/abs/2309.01796v1 | http://arxiv.org/pdf/2309.01796v1 | 2309.01796v1 |
Composite federated learning with heterogeneous data | We propose a novel algorithm for solving the composite Federated Learning
(FL) problem. This algorithm manages non-smooth regularization by strategically
decoupling the proximal operator and communication, and addresses client drift
without any assumptions about data similarity. Moreover, each worker uses local
updates to reduce the communication frequency with the server and transmits
only a $d$-dimensional vector per communication round. We prove that our
algorithm converges linearly to a neighborhood of the optimal solution and
demonstrate the superiority of our algorithm over state-of-the-art methods in
numerical experiments. | [
"Jiaojiao Zhang",
"Jiang Hu",
"Mikael Johansson"
] | 2023-09-04 20:22:57 | http://arxiv.org/abs/2309.01795v1 | http://arxiv.org/pdf/2309.01795v1 | 2309.01795v1 |
Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property Prediction | The prediction of molecular properties is a crucial task in the field of
material and drug discovery. The potential benefits of using deep learning
techniques are reflected in the wealth of recent literature. Still, these
techniques are faced with a common challenge in practice: Labeled data are
limited by the cost of manual extraction from literature and laborious
experimentation. In this work, we propose a data-efficient property predictor
by utilizing a learnable hierarchical molecular grammar that can generate
molecules from grammar production rules. Such a grammar induces an explicit
geometry of the space of molecular graphs, which provides an informative prior
on molecular structural similarity. The property prediction is performed using
graph neural diffusion over the grammar-induced geometry. On both small and
large datasets, our evaluation shows that this approach outperforms a wide
spectrum of baselines, including supervised and pre-trained graph neural
networks. We include a detailed ablation study and further analysis of our
solution, showing its effectiveness in cases with extremely limited data. Code
is available at https://github.com/gmh14/Geo-DEG. | [
"Minghao Guo",
"Veronika Thost",
"Samuel W Song",
"Adithya Balachandran",
"Payel Das",
"Jie Chen",
"Wojciech Matusik"
] | 2023-09-04 19:59:51 | http://arxiv.org/abs/2309.01788v1 | http://arxiv.org/pdf/2309.01788v1 | 2309.01788v1 |
ATMS: Algorithmic Trading-Guided Market Simulation | The effective construction of an Algorithmic Trading (AT) strategy often
relies on market simulators, which remains challenging due to existing methods'
inability to adapt to the sequential and dynamic nature of trading activities.
This work fills this gap by proposing a metric to quantify market discrepancy.
This metric measures the difference between a causal effect from underlying
market unique characteristics and it is evaluated through the interaction
between the AT agent and the market. Most importantly, we introduce Algorithmic
Trading-guided Market Simulation (ATMS) by optimizing our proposed metric.
Inspired by SeqGAN, ATMS formulates the simulator as a stochastic policy in
reinforcement learning (RL) to account for the sequential nature of trading.
Moreover, ATMS utilizes the policy gradient update to bypass differentiating
the proposed metric, which involves non-differentiable operations such as order
deletion from the market. Through extensive experiments on semi-real market
data, we demonstrate the effectiveness of our metric and show that ATMS
generates market data with improved similarity to reality compared to the
state-of-the-art conditional Wasserstein Generative Adversarial Network (cWGAN)
approach. Furthermore, ATMS produces market data with more balanced BUY and
SELL volumes, mitigating the bias of the cWGAN baseline approach, where a
simple strategy can exploit the BUY/SELL imbalance for profit. | [
"Song Wei",
"Andrea Coletta",
"Svitlana Vyetrenko",
"Tucker Balch"
] | 2023-09-04 19:56:18 | http://arxiv.org/abs/2309.01784v1 | http://arxiv.org/pdf/2309.01784v1 | 2309.01784v1 |
Survival Prediction from Imbalance colorectal cancer dataset using hybrid sampling methods and tree-based classifiers | Background and Objective: Colorectal cancer is a high mortality cancer.
Clinical data analysis plays a crucial role in predicting the survival of
colorectal cancer patients, enabling clinicians to make informed treatment
decisions. However, utilizing clinical data can be challenging, especially when
dealing with imbalanced outcomes. This paper focuses on developing algorithms
to predict 1-, 3-, and 5-year survival of colorectal cancer patients using
clinical datasets, with particular emphasis on the highly imbalanced 1-year
survival prediction task. To address this issue, we propose a method that
creates a pipeline of some of standard balancing techniques to increase the
true positive rate. Evaluation is conducted on a colorectal cancer dataset from
the SEER database. Methods: The pre-processing step consists of removing
records with missing values and merging categories. The minority class of
1-year and 3-year survival tasks consists of 10% and 20% of the data,
respectively. Edited Nearest Neighbor, Repeated edited nearest neighbor (RENN),
Synthetic Minority Over-sampling Techniques (SMOTE), and pipelines of SMOTE and
RENN approaches were used and compared for balancing the data with tree-based
classifiers. Decision Trees, Random Forest, Extra Tree, eXtreme Gradient
Boosting, and Light Gradient Boosting (LGBM) are used in this article. Method.
Results: The performance evaluation utilizes a 5-fold cross-validation
approach. In the case of highly imbalanced datasets (1-year), our proposed
method with LGBM outperforms other sampling methods with the sensitivity of
72.30%. For the task of imbalance (3-year survival), the combination of RENN
and LGBM achieves a sensitivity of 80.81%, indicating that our proposed method
works best for highly imbalanced datasets. Conclusions: Our proposed method
significantly improves mortality prediction for the minority class of
colorectal cancer patients. | [
"Sadegh Soleimani",
"Mahsa Bahrami",
"Mansour Vali"
] | 2023-09-04 19:48:56 | http://arxiv.org/abs/2309.01783v1 | http://arxiv.org/pdf/2309.01783v1 | 2309.01783v1 |
3D View Prediction Models of the Dorsal Visual Stream | Deep neural network representations align well with brain activity in the
ventral visual stream. However, the primate visual system has a distinct dorsal
processing stream with different functional properties. To test if a model
trained to perceive 3D scene geometry aligns better with neural responses in
dorsal visual areas, we trained a self-supervised geometry-aware recurrent
neural network (GRNN) to predict novel camera views using a 3D feature memory.
We compared GRNN to self-supervised baseline models that have been shown to
align well with ventral regions using the large-scale fMRI Natural Scenes
Dataset (NSD). We found that while the baseline models accounted better for
ventral brain regions, GRNN accounted for a greater proportion of variance in
dorsal brain regions. Our findings demonstrate the potential for using
task-relevant models to probe representational differences across visual
streams. | [
"Gabriel Sarch",
"Hsiao-Yu Fish Tung",
"Aria Wang",
"Jacob Prince",
"Michael Tarr"
] | 2023-09-04 19:48:17 | http://arxiv.org/abs/2309.01782v1 | http://arxiv.org/pdf/2309.01782v1 | 2309.01782v1 |
Self-concordant Smoothing for Convex Composite Optimization | We introduce the notion of self-concordant smoothing for minimizing the sum
of two convex functions: the first is smooth and the second may be nonsmooth.
Our framework results naturally from the smoothing approximation technique
referred to as partial smoothing in which only a part of the nonsmooth function
is smoothed. The key highlight of our approach is in a natural property of the
resulting problem's structure which provides us with a variable-metric
selection method and a step-length selection rule particularly suitable for
proximal Newton-type algorithms. In addition, we efficiently handle specific
structures promoted by the nonsmooth function, such as $\ell_1$-regularization
and group-lasso penalties. We prove local quadratic convergence rates for two
resulting algorithms: Prox-N-SCORE, a proximal Newton algorithm and
Prox-GGN-SCORE, a proximal generalized Gauss-Newton (GGN) algorithm. The
Prox-GGN-SCORE algorithm highlights an important approximation procedure which
helps to significantly reduce most of the computational overhead associated
with the inverse Hessian. This approximation is essentially useful for
overparameterized machine learning models and in the mini-batch settings.
Numerical examples on both synthetic and real datasets demonstrate the
efficiency of our approach and its superiority over existing approaches. | [
"Adeyemi D. Adeoye",
"Alberto Bemporad"
] | 2023-09-04 19:47:04 | http://arxiv.org/abs/2309.01781v1 | http://arxiv.org/pdf/2309.01781v1 | 2309.01781v1 |
Measuring, Interpreting, and Improving Fairness of Algorithms using Causal Inference and Randomized Experiments | Algorithm fairness has become a central problem for the broad adoption of
artificial intelligence. Although the past decade has witnessed an explosion of
excellent work studying algorithm biases, achieving fairness in real-world AI
production systems has remained a challenging task. Most existing works fail to
excel in practical applications since either they have conflicting measurement
techniques and/ or heavy assumptions, or require code-access of the production
models, whereas real systems demand an easy-to-implement measurement framework
and a systematic way to correct the detected sources of bias.
In this paper, we leverage recent advances in causal inference and
interpretable machine learning to present an algorithm-agnostic framework
(MIIF) to Measure, Interpret, and Improve the Fairness of an algorithmic
decision. We measure the algorithm bias using randomized experiments, which
enables the simultaneous measurement of disparate treatment, disparate impact,
and economic value. Furthermore, using modern interpretability techniques, we
develop an explainable machine learning model which accurately interprets and
distills the beliefs of a blackbox algorithm. Altogether, these techniques
create a simple and powerful toolset for studying algorithm fairness,
especially for understanding the cost of fairness in practical applications
like e-commerce and targeted advertising, where industry A/B testing is already
abundant. | [
"James Enouen",
"Tianshu Sun",
"Yan Liu"
] | 2023-09-04 19:45:18 | http://arxiv.org/abs/2309.01780v1 | http://arxiv.org/pdf/2309.01780v1 | 2309.01780v1 |
DRAG: Divergence-based Adaptive Aggregation in Federated learning on Non-IID Data | Local stochastic gradient descent (SGD) is a fundamental approach in
achieving communication efficiency in Federated Learning (FL) by allowing
individual workers to perform local updates. However, the presence of
heterogeneous data distributions across working nodes causes each worker to
update its local model towards a local optimum, leading to the phenomenon known
as ``client-drift" and resulting in slowed convergence. To address this issue,
previous works have explored methods that either introduce communication
overhead or suffer from unsteady performance. In this work, we introduce a
novel metric called ``degree of divergence," quantifying the angle between the
local gradient and the global reference direction. Leveraging this metric, we
propose the divergence-based adaptive aggregation (DRAG) algorithm, which
dynamically ``drags" the received local updates toward the reference direction
in each round without requiring extra communication overhead. Furthermore, we
establish a rigorous convergence analysis for DRAG, proving its ability to
achieve a sublinear convergence rate. Compelling experimental results are
presented to illustrate DRAG's superior performance compared to
state-of-the-art algorithms in effectively managing the client-drift
phenomenon. Additionally, DRAG exhibits remarkable resilience against certain
Byzantine attacks. By securely sharing a small sample of the client's data with
the FL server, DRAG effectively counters these attacks, as demonstrated through
comprehensive experiments. | [
"Feng Zhu",
"Jingjing Zhang",
"Shengyun Liu",
"Xin Wang"
] | 2023-09-04 19:40:58 | http://arxiv.org/abs/2309.01779v1 | http://arxiv.org/pdf/2309.01779v1 | 2309.01779v1 |
CONFIDERAI: a novel CONFormal Interpretable-by-Design score function for Explainable and Reliable Artificial Intelligence | Everyday life is increasingly influenced by artificial intelligence, and
there is no question that machine learning algorithms must be designed to be
reliable and trustworthy for everyone. Specifically, computer scientists
consider an artificial intelligence system safe and trustworthy if it fulfills
five pillars: explainability, robustness, transparency, fairness, and privacy.
In addition to these five, we propose a sixth fundamental aspect: conformity,
that is, the probabilistic assurance that the system will behave as the machine
learner expects. In this paper, we propose a methodology to link conformal
prediction with explainable machine learning by defining CONFIDERAI, a new
score function for rule-based models that leverages both rules predictive
ability and points geometrical position within rules boundaries. We also
address the problem of defining regions in the feature space where conformal
guarantees are satisfied by exploiting techniques to control the number of
non-conformal samples in conformal regions based on support vector data
description (SVDD). The overall methodology is tested with promising results on
benchmark and real datasets, such as DNS tunneling detection or cardiovascular
disease prediction. | [
"Alberto Carlevaro",
"Sara Narteni",
"Fabrizio Dabbene",
"Marco Muselli",
"Maurizio Mongelli"
] | 2023-09-04 19:39:21 | http://arxiv.org/abs/2309.01778v2 | http://arxiv.org/pdf/2309.01778v2 | 2309.01778v2 |
Gated recurrent neural networks discover attention | Recent architectural developments have enabled recurrent neural networks
(RNNs) to reach and even surpass the performance of Transformers on certain
sequence modeling tasks. These modern RNNs feature a prominent design pattern:
linear recurrent layers interconnected by feedforward paths with multiplicative
gating. Here, we show how RNNs equipped with these two design elements can
exactly implement (linear) self-attention, the main building block of
Transformers. By reverse-engineering a set of trained RNNs, we find that
gradient descent in practice discovers our construction. In particular, we
examine RNNs trained to solve simple in-context learning tasks on which
Transformers are known to excel and find that gradient descent instills in our
RNNs the same attention-based in-context learning algorithm used by
Transformers. Our findings highlight the importance of multiplicative
interactions in neural networks and suggest that certain RNNs might be
unexpectedly implementing attention under the hood. | [
"Nicolas Zucchet",
"Seijin Kobayashi",
"Yassir Akram",
"Johannes von Oswald",
"Maxime Larcher",
"Angelika Steger",
"João Sacramento"
] | 2023-09-04 19:28:54 | http://arxiv.org/abs/2309.01775v1 | http://arxiv.org/pdf/2309.01775v1 | 2309.01775v1 |
ADC/DAC-Free Analog Acceleration of Deep Neural Networks with Frequency Transformation | The edge processing of deep neural networks (DNNs) is becoming increasingly
important due to its ability to extract valuable information directly at the
data source to minimize latency and energy consumption. Frequency-domain model
compression, such as with the Walsh-Hadamard transform (WHT), has been
identified as an efficient alternative. However, the benefits of
frequency-domain processing are often offset by the increased
multiply-accumulate (MAC) operations required. This paper proposes a novel
approach to an energy-efficient acceleration of frequency-domain neural
networks by utilizing analog-domain frequency-based tensor transformations. Our
approach offers unique opportunities to enhance computational efficiency,
resulting in several high-level advantages, including array micro-architecture
with parallelism, ADC/DAC-free analog computations, and increased output
sparsity. Our approach achieves more compact cells by eliminating the need for
trainable parameters in the transformation matrix. Moreover, our novel array
micro-architecture enables adaptive stitching of cells column-wise and
row-wise, thereby facilitating perfect parallelism in computations.
Additionally, our scheme enables ADC/DAC-free computations by training against
highly quantized matrix-vector products, leveraging the parameter-free nature
of matrix multiplications. Another crucial aspect of our design is its ability
to handle signed-bit processing for frequency-based transformations. This leads
to increased output sparsity and reduced digitization workload. On a
16$\times$16 crossbars, for 8-bit input processing, the proposed approach
achieves the energy efficiency of 1602 tera operations per second per Watt
(TOPS/W) without early termination strategy and 5311 TOPS/W with early
termination strategy at VDD = 0.8 V. | [
"Nastaran Darabi",
"Maeesha Binte Hashem",
"Hongyi Pan",
"Ahmet Cetin",
"Wilfred Gomes",
"Amit Ranjan Trivedi"
] | 2023-09-04 19:19:39 | http://arxiv.org/abs/2309.01771v1 | http://arxiv.org/pdf/2309.01771v1 | 2309.01771v1 |
On Penalty Methods for Nonconvex Bilevel Optimization and First-Order Stochastic Approximation | In this work, we study first-order algorithms for solving Bilevel
Optimization (BO) where the objective functions are smooth but possibly
nonconvex in both levels and the variables are restricted to closed convex
sets. As a first step, we study the landscape of BO through the lens of penalty
methods, in which the upper- and lower-level objectives are combined in a
weighted sum with penalty parameter $\sigma > 0$. In particular, we establish a
strong connection between the penalty function and the hyper-objective by
explicitly characterizing the conditions under which the values and derivatives
of the two must be $O(\sigma)$-close. A by-product of our analysis is the
explicit formula for the gradient of hyper-objective when the lower-level
problem has multiple solutions under minimal conditions, which could be of
independent interest. Next, viewing the penalty formulation as
$O(\sigma)$-approximation of the original BO, we propose first-order algorithms
that find an $\epsilon$-stationary solution by optimizing the penalty
formulation with $\sigma = O(\epsilon)$. When the perturbed lower-level problem
uniformly satisfies the small-error proximal error-bound (EB) condition, we
propose a first-order algorithm that converges to an $\epsilon$-stationary
point of the penalty function, using in total $O(\epsilon^{-3})$ and
$O(\epsilon^{-7})$ accesses to first-order (stochastic) gradient oracles when
the oracle is deterministic and oracles are noisy, respectively. Under an
additional assumption on stochastic oracles, we show that the algorithm can be
implemented in a fully {\it single-loop} manner, i.e., with $O(1)$ samples per
iteration, and achieves the improved oracle-complexity of $O(\epsilon^{-3})$
and $O(\epsilon^{-5})$, respectively. | [
"Jeongyeol Kwon",
"Dohyun Kwon",
"Steve Wright",
"Robert Nowak"
] | 2023-09-04 18:25:43 | http://arxiv.org/abs/2309.01753v1 | http://arxiv.org/pdf/2309.01753v1 | 2309.01753v1 |
Turbulent Flow Simulation using Autoregressive Conditional Diffusion Models | Simulating turbulent flows is crucial for a wide range of applications, and
machine learning-based solvers are gaining increasing relevance. However,
achieving stability when generalizing to longer rollout horizons remains a
persistent challenge for learned PDE solvers. We address this challenge by
introducing a fully data-driven fluid solver that utilizes an autoregressive
rollout based on conditional diffusion models. We show that this approach
offers clear advantages in terms of rollout stability compared to other learned
baselines. Remarkably, these improvements in stability are achieved without
compromising the quality of generated samples, and our model successfully
generalizes to flow parameters beyond the training regime. Additionally, the
probabilistic nature of the diffusion approach allows for inferring predictions
that align with the statistics of the underlying physics. We quantitatively and
qualitatively evaluate the performance of our method on a range of challenging
scenarios, including incompressible and transonic flows, as well as isotropic
turbulence. | [
"Georg Kohl",
"Li-Wei Chen",
"Nils Thuerey"
] | 2023-09-04 18:01:42 | http://arxiv.org/abs/2309.01745v1 | http://arxiv.org/pdf/2309.01745v1 | 2309.01745v1 |
An Empirical Analysis for Zero-Shot Multi-Label Classification on COVID-19 CT Scans and Uncurated Reports | The pandemic resulted in vast repositories of unstructured data, including
radiology reports, due to increased medical examinations. Previous research on
automated diagnosis of COVID-19 primarily focuses on X-ray images, despite
their lower precision compared to computed tomography (CT) scans. In this work,
we leverage unstructured data from a hospital and harness the fine-grained
details offered by CT scans to perform zero-shot multi-label classification
based on contrastive visual language learning. In collaboration with human
experts, we investigate the effectiveness of multiple zero-shot models that aid
radiologists in detecting pulmonary embolisms and identifying intricate lung
details like ground glass opacities and consolidations. Our empirical analysis
provides an overview of the possible solutions to target such fine-grained
tasks, so far overlooked in the medical multimodal pretraining literature. Our
investigation promises future advancements in the medical image analysis
community by addressing some challenges associated with unstructured data and
fine-grained multi-label classification. | [
"Ethan Dack",
"Lorenzo Brigato",
"Matthew McMurray",
"Matthias Fontanellaz",
"Thomas Frauenfelder",
"Hanno Hoppe",
"Aristomenis Exadaktylos",
"Thomas Geiser",
"Manuela Funke-Chambour",
"Andreas Christe",
"Lukas Ebner",
"Stavroula Mougiakakou"
] | 2023-09-04 17:58:01 | http://arxiv.org/abs/2309.01740v2 | http://arxiv.org/pdf/2309.01740v2 | 2309.01740v2 |
Hybrid data driven/thermal simulation model for comfort assessment | Machine learning models improve the speed and quality of physical models.
However, they require a large amount of data, which is often difficult and
costly to acquire. Predicting thermal comfort, for example, requires a
controlled environment, with participants presenting various characteristics
(age, gender, ...). This paper proposes a method for hybridizing real data with
simulated data for thermal comfort prediction. The simulations are performed
using Modelica Language. A benchmarking study is realized to compare different
machine learning methods. Obtained results look promising with an F1 score of
0.999 obtained using the random forest model. | [
"Romain Barbedienne",
"Sara Yasmine Ouerk",
"Mouadh Yagoubi",
"Hassan Bouia",
"Aurelie Kaemmerlen",
"Benoit Charrier"
] | 2023-09-04 17:39:07 | http://arxiv.org/abs/2309.01734v1 | http://arxiv.org/pdf/2309.01734v1 | 2309.01734v1 |
Adaptive Resource Allocation for Virtualized Base Stations in O-RAN with Online Learning | Open Radio Access Network systems, with their virtualized base stations
(vBSs), offer operators the benefits of increased flexibility, reduced costs,
vendor diversity, and interoperability. Optimizing the allocation of resources
in a vBS is challenging since it requires knowledge of the environment, (i.e.,
"external'' information), such as traffic demands and channel quality, which is
difficult to acquire precisely over short intervals of a few seconds. To tackle
this problem, we propose an online learning algorithm that balances the
effective throughput and vBS energy consumption, even under unforeseeable and
"challenging'' environments; for instance, non-stationary or adversarial
traffic demands. We also develop a meta-learning scheme, which leverages the
power of other algorithmic approaches, tailored for more "easy'' environments,
and dynamically chooses the best performing one, thus enhancing the overall
system's versatility and effectiveness. We prove the proposed solutions achieve
sub-linear regret, providing zero average optimality gap even in challenging
environments. The performance of the algorithms is evaluated with real-world
data and various trace-driven evaluations, indicating savings of up to 64.5% in
the power consumption of a vBS compared with state-of-the-art benchmarks. | [
"Michail Kalntis",
"George Iosifidis",
"Fernando A. Kuipers"
] | 2023-09-04 17:30:21 | http://arxiv.org/abs/2309.01730v1 | http://arxiv.org/pdf/2309.01730v1 | 2309.01730v1 |
Softmax Bias Correction for Quantized Generative Models | Post-training quantization (PTQ) is the go-to compression technique for large
generative models, such as stable diffusion or large language models. PTQ
methods commonly keep the softmax activation in higher precision as it has been
shown to be very sensitive to quantization noise. However, this can lead to a
significant runtime and power overhead during inference on resource-constraint
edge devices. In this work, we investigate the source of the softmax
sensitivity to quantization and show that the quantization operation leads to a
large bias in the softmax output, causing accuracy degradation. To overcome
this issue, we propose an offline bias correction technique that improves the
quantizability of softmax without additional compute during deployment, as it
can be readily absorbed into the quantization parameters. We demonstrate the
effectiveness of our method on stable diffusion v1.5 and 125M-size OPT language
model, achieving significant accuracy improvement for 8-bit quantized softmax. | [
"Nilesh Prasad Pandey",
"Marios Fournarakis",
"Chirag Patel",
"Markus Nagel"
] | 2023-09-04 17:29:31 | http://arxiv.org/abs/2309.01729v1 | http://arxiv.org/pdf/2309.01729v1 | 2309.01729v1 |
Prompting or Fine-tuning? A Comparative Study of Large Language Models for Taxonomy Construction | Taxonomies represent hierarchical relations between entities, frequently
applied in various software modeling and natural language processing (NLP)
activities. They are typically subject to a set of structural constraints
restricting their content. However, manual taxonomy construction can be
time-consuming, incomplete, and costly to maintain. Recent studies of large
language models (LLMs) have demonstrated that appropriate user inputs (called
prompting) can effectively guide LLMs, such as GPT-3, in diverse NLP tasks
without explicit (re-)training. However, existing approaches for automated
taxonomy construction typically involve fine-tuning a language model by
adjusting model parameters. In this paper, we present a general framework for
taxonomy construction that takes into account structural constraints. We
subsequently conduct a systematic comparison between the prompting and
fine-tuning approaches performed on a hypernym taxonomy and a novel computer
science taxonomy dataset. Our result reveals the following: (1) Even without
explicit training on the dataset, the prompting approach outperforms
fine-tuning-based approaches. Moreover, the performance gap between prompting
and fine-tuning widens when the training dataset is small. However, (2)
taxonomies generated by the fine-tuning approach can be easily post-processed
to satisfy all the constraints, whereas handling violations of the taxonomies
produced by the prompting approach can be challenging. These evaluation
findings provide guidance on selecting the appropriate method for taxonomy
construction and highlight potential enhancements for both approaches. | [
"Boqi Chen",
"Fandi Yi",
"Dániel Varró"
] | 2023-09-04 16:53:17 | http://arxiv.org/abs/2309.01715v1 | http://arxiv.org/pdf/2309.01715v1 | 2309.01715v1 |
On the Robustness of Post-hoc GNN Explainers to Label Noise | Proposed as a solution to the inherent black-box limitations of graph neural
networks (GNNs), post-hoc GNN explainers aim to provide precise and insightful
explanations of the behaviours exhibited by trained GNNs. Despite their recent
notable advancements in academic and industrial contexts, the robustness of
post-hoc GNN explainers remains unexplored when confronted with label noise. To
bridge this gap, we conduct a systematic empirical investigation to evaluate
the efficacy of diverse post-hoc GNN explainers under varying degrees of label
noise. Our results reveal several key insights: Firstly, post-hoc GNN
explainers are susceptible to label perturbations. Secondly, even minor levels
of label noise, inconsequential to GNN performance, harm the quality of
generated explanations substantially. Lastly, we engage in a discourse
regarding the progressive recovery of explanation effectiveness with escalating
noise levels. | [
"Zhiqiang Zhong",
"Yangqianzi Jiang",
"Davide Mottin"
] | 2023-09-04 16:35:04 | http://arxiv.org/abs/2309.01706v2 | http://arxiv.org/pdf/2309.01706v2 | 2309.01706v2 |
Robust Online Classification: From Estimation to Denoising | We study online classification in the presence of noisy labels. The noise
mechanism is modeled by a general kernel that specifies, for any feature-label
pair, a (known) set of distributions over noisy labels. At each time step, an
adversary selects an unknown distribution from the distribution set specified
by the kernel based on the actual feature-label pair, and generates the noisy
label from the selected distribution. The learner then makes a prediction based
on the actual features and noisy labels observed thus far, and incurs loss $1$
if the prediction differs from the underlying truth (and $0$ otherwise). The
prediction quality is quantified through minimax risk, which computes the
cumulative loss over a finite horizon $T$. We show that for a wide range of
natural noise kernels, adversarially selected features, and finite class of
labeling functions, minimax risk can be upper bounded independent of the time
horizon and logarithmic in the size of labeling function class. We then extend
these results to inifinite classes and stochastically generated features via
the concept of stochastic sequential covering. Our results extend and encompass
findings of Ben-David et al. (2009) through substantial generality, and provide
intuitive understanding through a novel reduction to online conditional
distribution estimation. | [
"Changlong Wu",
"Ananth Grama",
"Wojciech Szpankowski"
] | 2023-09-04 16:17:39 | http://arxiv.org/abs/2309.01698v1 | http://arxiv.org/pdf/2309.01698v1 | 2309.01698v1 |
Physics-Informed Polynomial Chaos Expansions | Surrogate modeling of costly mathematical models representing physical
systems is challenging since it is typically not possible to create a large
experimental design. Thus, it is beneficial to constrain the approximation to
adhere to the known physics of the model. This paper presents a novel
methodology for the construction of physics-informed polynomial chaos
expansions (PCE) that combines the conventional experimental design with
additional constraints from the physics of the model. Physical constraints
investigated in this paper are represented by a set of differential equations
and specified boundary conditions. A computationally efficient means for
construction of physically constrained PCE is proposed and compared to standard
sparse PCE. It is shown that the proposed algorithms lead to superior accuracy
of the approximation and does not add significant computational burden.
Although the main purpose of the proposed method lies in combining data and
physical constraints, we show that physically constrained PCEs can be
constructed from differential equations and boundary conditions alone without
requiring evaluations of the original model. We further show that the
constrained PCEs can be easily applied for uncertainty quantification through
analytical post-processing of a reduced PCE filtering out the influence of all
deterministic space-time variables. Several deterministic examples of
increasing complexity are provided and the proposed method is applied for
uncertainty quantification. | [
"Lukáš Novák",
"Himanshu Sharma",
"Michael D. Shields"
] | 2023-09-04 16:16:34 | http://arxiv.org/abs/2309.01697v1 | http://arxiv.org/pdf/2309.01697v1 | 2309.01697v1 |
No Data Augmentation? Alternative Regularizations for Effective Training on Small Datasets | Solving image classification tasks given small training datasets remains an
open challenge for modern computer vision. Aggressive data augmentation and
generative models are among the most straightforward approaches to overcoming
the lack of data. However, the first fails to be agnostic to varying image
domains, while the latter requires additional compute and careful design. In
this work, we study alternative regularization strategies to push the limits of
supervised learning on small image classification datasets. In particular,
along with the model size and training schedule scaling, we employ a heuristic
to select (semi) optimal learning rate and weight decay couples via the norm of
model parameters. By training on only 1% of the original CIFAR-10 training set
(i.e., 50 images per class) and testing on ciFAIR-10, a variant of the original
CIFAR without duplicated images, we reach a test accuracy of 66.5%, on par with
the best state-of-the-art methods. | [
"Lorenzo Brigato",
"Stavroula Mougiakakou"
] | 2023-09-04 16:13:59 | http://arxiv.org/abs/2309.01694v1 | http://arxiv.org/pdf/2309.01694v1 | 2309.01694v1 |
Artificial Empathy Classification: A Survey of Deep Learning Techniques, Datasets, and Evaluation Scales | From the last decade, researchers in the field of machine learning (ML) and
assistive developmental robotics (ADR) have taken an interest in artificial
empathy (AE) as a possible future paradigm for human-robot interaction (HRI).
Humans learn empathy since birth, therefore, it is challenging to instill this
sense in robots and intelligent machines. Nevertheless, by training over a vast
amount of data and time, imitating empathy, to a certain extent, can be
possible for robots. Training techniques for AE, along with findings from the
field of empathetic AI research, are ever-evolving. The standard workflow for
artificial empathy consists of three stages: 1) Emotion Recognition (ER) using
the retrieved features from video or textual data, 2) analyzing the perceived
emotion or degree of empathy to choose the best course of action, and 3)
carrying out a response action. Recent studies that show AE being used with
virtual agents or robots often include Deep Learning (DL) techniques. For
instance, models like VGGFace are used to conduct ER. Semi-supervised models
like Autoencoders generate the corresponding emotional states and behavioral
responses. However, there has not been any study that presents an independent
approach for evaluating AE, or the degree to which a reaction was empathetic.
This paper aims to investigate and evaluate existing works for measuring and
evaluating empathy, as well as the datasets that have been collected and used
so far. Our goal is to highlight and facilitate the use of state-of-the-art
methods in the area of AE by comparing their performance. This will aid
researchers in the area of AE in selecting their approaches with precision. | [
"Sharjeel Tahir",
"Syed Afaq Shah",
"Jumana Abu-Khalaf"
] | 2023-09-04 16:02:59 | http://arxiv.org/abs/2310.00010v1 | http://arxiv.org/pdf/2310.00010v1 | 2310.00010v1 |
Blind Biological Sequence Denoising with Self-Supervised Set Learning | Biological sequence analysis relies on the ability to denoise the imprecise
output of sequencing platforms. We consider a common setting where a short
sequence is read out repeatedly using a high-throughput long-read platform to
generate multiple subreads, or noisy observations of the same sequence.
Denoising these subreads with alignment-based approaches often fails when too
few subreads are available or error rates are too high. In this paper, we
propose a novel method for blindly denoising sets of sequences without directly
observing clean source sequence labels. Our method, Self-Supervised Set
Learning (SSSL), gathers subreads together in an embedding space and estimates
a single set embedding as the midpoint of the subreads in both the latent and
sequence spaces. This set embedding represents the "average" of the subreads
and can be decoded into a prediction of the clean sequence. In experiments on
simulated long-read DNA data, SSSL methods denoise small reads of $\leq 6$
subreads with 17% fewer errors and large reads of $>6$ subreads with 8% fewer
errors compared to the best baseline. On a real dataset of antibody sequences,
SSSL improves over baselines on two self-supervised metrics, with a significant
improvement on difficult small reads that comprise over 60% of the test set. By
accurately denoising these reads, SSSL promises to better realize the potential
of high-throughput DNA sequencing data for downstream scientific applications. | [
"Nathan Ng",
"Ji Won Park",
"Jae Hyeon Lee",
"Ryan Lewis Kelly",
"Stephen Ra",
"Kyunghyun Cho"
] | 2023-09-04 15:35:04 | http://arxiv.org/abs/2309.01670v1 | http://arxiv.org/pdf/2309.01670v1 | 2309.01670v1 |
Robust penalized least squares of depth trimmed residuals regression for high-dimensional data | Challenges with data in the big-data era include (i) the dimension $p$ is
often larger than the sample size $n$ (ii) outliers or contaminated points are
frequently hidden and more difficult to detect. Challenge (i) renders most
conventional methods inapplicable. Thus, it attracts tremendous attention from
statistics, computer science, and bio-medical communities. Numerous penalized
regression methods have been introduced as modern methods for analyzing
high-dimensional data. Disproportionate attention has been paid to the
challenge (ii) though. Penalized regression methods can do their job very well
and are expected to handle the challenge (ii) simultaneously. Most of them,
however, can break down by a single outlier (or single adversary contaminated
point) as revealed in this article.
The latter systematically examines leading penalized regression methods in
the literature in terms of their robustness, provides quantitative assessment,
and reveals that most of them can break down by a single outlier. Consequently,
a novel robust penalized regression method based on the least sum of squares of
depth trimmed residuals is proposed and studied carefully. Experiments with
simulated and real data reveal that the newly proposed method can outperform
some leading competitors in estimation and prediction accuracy in the cases
considered. | [
"Yijun Zuo"
] | 2023-09-04 15:33:29 | http://arxiv.org/abs/2309.01666v1 | http://arxiv.org/pdf/2309.01666v1 | 2309.01666v1 |
Locally Stationary Graph Processes | Stationary graph process models are commonly used in the analysis and
inference of data sets collected on irregular network topologies. While most of
the existing methods represent graph signals with a single stationary process
model that is globally valid on the entire graph, in many practical problems,
the characteristics of the process may be subject to local variations in
different regions of the graph. In this work, we propose a locally stationary
graph process (LSGP) model that aims to extend the classical concept of local
stationarity to irregular graph domains. We characterize local stationarity by
expressing the overall process as the combination of a set of component
processes such that the extent to which the process adheres to each component
varies smoothly over the graph. We propose an algorithm for computing LSGP
models from realizations of the process, and also study the approximation of
LSGPs locally with WSS processes. Experiments on signal interpolation problems
show that the proposed process model provides accurate signal representations
competitive with the state of the art. | [
"Abdullah Canbolat",
"Elif Vural"
] | 2023-09-04 15:16:55 | http://arxiv.org/abs/2309.01657v1 | http://arxiv.org/pdf/2309.01657v1 | 2309.01657v1 |
Which algorithm to select in sports timetabling? | Any sports competition needs a timetable, specifying when and where teams
meet each other. The recent International Timetabling Competition (ITC2021) on
sports timetabling showed that, although it is possible to develop general
algorithms, the performance of each algorithm varies considerably over the
problem instances. This paper provides an instance space analysis for sports
timetabling, resulting in powerful insights into the strengths and weaknesses
of eight state-of-the-art algorithms. Based on machine learning techniques, we
propose an algorithm selection system that predicts which algorithm is likely
to perform best when given the characteristics of a sports timetabling problem
instance. Furthermore, we identify which characteristics are important in
making that prediction, providing insights in the performance of the
algorithms, and suggestions to further improve them. Finally, we assess the
empirical hardness of the instances. Our results are based on large
computational experiments involving about 50 years of CPU time on more than 500
newly generated problem instances. | [
"David Van Bulck",
"Dries Goossens",
"Jan-Patrick Clarner",
"Angelos Dimitsas",
"George H. G. Fonseca",
"Carlos Lamas-Fernandez",
"Martin Mariusz Lester",
"Jaap Pedersen",
"Antony E. Phillips",
"Roberto Maria Rosati"
] | 2023-09-04 15:13:56 | http://arxiv.org/abs/2309.03229v1 | http://arxiv.org/pdf/2309.03229v1 | 2309.03229v1 |
Relay Diffusion: Unifying diffusion process across resolutions for image synthesis | Diffusion models achieved great success in image synthesis, but still face
challenges in high-resolution generation. Through the lens of discrete cosine
transformation, we find the main reason is that \emph{the same noise level on a
higher resolution results in a higher Signal-to-Noise Ratio in the frequency
domain}. In this work, we present Relay Diffusion Model (RDM), which transfers
a low-resolution image or noise into an equivalent high-resolution one for
diffusion model via blurring diffusion and block noise. Therefore, the
diffusion process can continue seamlessly in any new resolution or model
without restarting from pure noise or low-resolution conditioning. RDM achieves
state-of-the-art FID on CelebA-HQ and sFID on ImageNet 256$\times$256,
surpassing previous works such as ADM, LDM and DiT by a large margin. All the
codes and checkpoints are open-sourced at
\url{https://github.com/THUDM/RelayDiffusion}. | [
"Jiayan Teng",
"Wendi Zheng",
"Ming Ding",
"Wenyi Hong",
"Jianqiao Wangni",
"Zhuoyi Yang",
"Jie Tang"
] | 2023-09-04 15:00:33 | http://arxiv.org/abs/2309.03350v1 | http://arxiv.org/pdf/2309.03350v1 | 2309.03350v1 |
Design of Recognition and Evaluation System for Table Tennis Players' Motor Skills Based on Artificial Intelligence | With the rapid development of electronic science and technology, the research
on wearable devices is constantly updated, but for now, it is not comprehensive
for wearable devices to recognize and analyze the movement of specific sports.
Based on this, this paper improves wearable devices of table tennis sport, and
realizes the pattern recognition and evaluation of table tennis players' motor
skills through artificial intelligence. Firstly, a device is designed to
collect the movement information of table tennis players and the actual
movement data is processed. Secondly, a sliding window is made to divide the
collected motion data into a characteristic database of six table tennis
benchmark movements. Thirdly, motion features were constructed based on feature
engineering, and motor skills were identified for different models after
dimensionality reduction. Finally, the hierarchical evaluation system of motor
skills is established with the loss functions of different evaluation indexes.
The results show that in the recognition of table tennis players' motor skills,
the feature-based BP neural network proposed in this paper has higher
recognition accuracy and stronger generalization ability than the traditional
convolutional neural network. | [
"Zhuo-yong Shi",
"Ye-tao Jia",
"Ke-xin Zhang",
"Ding-han Wang",
"Long-meng Ji",
"Yong Wu"
] | 2023-09-04 14:58:56 | http://arxiv.org/abs/2309.07141v1 | http://arxiv.org/pdf/2309.07141v1 | 2309.07141v1 |
Corgi^2: A Hybrid Offline-Online Approach To Storage-Aware Data Shuffling For SGD | When using Stochastic Gradient Descent (SGD) for training machine learning
models, it is often crucial to provide the model with examples sampled at
random from the dataset. However, for large datasets stored in the cloud,
random access to individual examples is often costly and inefficient. A recent
work \cite{corgi}, proposed an online shuffling algorithm called CorgiPile,
which greatly improves efficiency of data access, at the cost some performance
loss, which is particularly apparent for large datasets stored in homogeneous
shards (e.g., video datasets). In this paper, we introduce a novel two-step
partial data shuffling strategy for SGD which combines an offline iteration of
the CorgiPile method with a subsequent online iteration. Our approach enjoys
the best of both worlds: it performs similarly to SGD with random access (even
for homogenous data) without compromising the data access efficiency of
CorgiPile. We provide a comprehensive theoretical analysis of the convergence
properties of our method and demonstrate its practical advantages through
experimental results. | [
"Etay Livne",
"Gal Kaplun",
"Eran Malach",
"Shai Shalev-Schwatz"
] | 2023-09-04 14:49:27 | http://arxiv.org/abs/2309.01640v1 | http://arxiv.org/pdf/2309.01640v1 | 2309.01640v1 |
Representing Edge Flows on Graphs via Sparse Cell Complexes | Obtaining sparse, interpretable representations of observable data is crucial
in many machine learning and signal processing tasks. For data representing
flows along the edges of a graph, an intuitively interpretable way to obtain
such representations is to lift the graph structure to a simplicial complex:
The eigenvectors of the associated Hodge-Laplacian, respectively the incidence
matrices of the corresponding simplicial complex then induce a Hodge
decomposition, which can be used to represent the observed data in terms of
gradient, curl, and harmonic flows. In this paper, we generalize this approach
to cellular complexes and introduce the cell inference optimization problem,
i.e., the problem of augmenting the observed graph by a set of cells, such that
the eigenvectors of the associated Hodge Laplacian provide a sparse,
interpretable representation of the observed edge flows on the graph. We show
that this problem is NP-hard and introduce an efficient approximation algorithm
for its solution. Experiments on real-world and synthetic data demonstrate that
our algorithm outperforms current state-of-the-art methods while being
computationally efficient. | [
"Josef Hoppe",
"Michael T. Schaub"
] | 2023-09-04 14:30:20 | http://arxiv.org/abs/2309.01632v2 | http://arxiv.org/pdf/2309.01632v2 | 2309.01632v2 |
DeViL: Decoding Vision features into Language | Post-hoc explanation methods have often been criticised for abstracting away
the decision-making process of deep neural networks. In this work, we would
like to provide natural language descriptions for what different layers of a
vision backbone have learned. Our DeViL method decodes vision features into
language, not only highlighting the attribution locations but also generating
textual descriptions of visual features at different layers of the network. We
train a transformer network to translate individual image features of any
vision layer into a prompt that a separate off-the-shelf language model decodes
into natural language. By employing dropout both per-layer and
per-spatial-location, our model can generalize training on image-text pairs to
generate localized explanations. As it uses a pre-trained language model, our
approach is fast to train, can be applied to any vision backbone, and produces
textual descriptions at different layers of the vision network. Moreover, DeViL
can create open-vocabulary attribution maps corresponding to words or phrases
even outside the training scope of the vision model. We demonstrate that DeViL
generates textual descriptions relevant to the image content on CC3M surpassing
previous lightweight captioning models and attribution maps uncovering the
learned concepts of the vision backbone. Finally, we show DeViL also
outperforms the current state-of-the-art on the neuron-wise descriptions of the
MILANNOTATIONS dataset. Code available at
https://github.com/ExplainableML/DeViL | [
"Meghal Dani",
"Isabel Rio-Torto",
"Stephan Alaniz",
"Zeynep Akata"
] | 2023-09-04 13:59:55 | http://arxiv.org/abs/2309.01617v1 | http://arxiv.org/pdf/2309.01617v1 | 2309.01617v1 |
Dropout Attacks | Dropout is a common operator in deep learning, aiming to prevent overfitting
by randomly dropping neurons during training. This paper introduces a new
family of poisoning attacks against neural networks named DROPOUTATTACK.
DROPOUTATTACK attacks the dropout operator by manipulating the selection of
neurons to drop instead of selecting them uniformly at random. We design,
implement, and evaluate four DROPOUTATTACK variants that cover a broad range of
scenarios. These attacks can slow or stop training, destroy prediction accuracy
of target classes, and sabotage either precision or recall of a target class.
In our experiments of training a VGG-16 model on CIFAR-100, our attack can
reduce the precision of the victim class by 34.6% (from 81.7% to 47.1%) without
incurring any degradation in model accuracy | [
"Andrew Yuan",
"Alina Oprea",
"Cheng Tan"
] | 2023-09-04 13:55:03 | http://arxiv.org/abs/2309.01614v1 | http://arxiv.org/pdf/2309.01614v1 | 2309.01614v1 |
On the Query Strategies for Efficient Online Active Distillation | Deep Learning (DL) requires lots of time and data, resulting in high
computational demands. Recently, researchers employ Active Learning (AL) and
online distillation to enhance training efficiency and real-time model
adaptation. This paper evaluates a set of query strategies to achieve the best
training results. It focuses on Human Pose Estimation (HPE) applications,
assessing the impact of selected frames during training using two approaches: a
classical offline method and a online evaluation through a continual learning
approach employing knowledge distillation, on a popular state-of-the-art HPE
dataset. The paper demonstrates the possibility of enabling training at the
edge lightweight models, adapting them effectively to new contexts in
real-time. | [
"Michele Boldo",
"Enrico Martini",
"Mirco De Marchi",
"Stefano Aldegheri",
"Nicola Bombieri"
] | 2023-09-04 13:53:20 | http://arxiv.org/abs/2309.01612v1 | http://arxiv.org/pdf/2309.01612v1 | 2309.01612v1 |
Fair Ranking under Disparate Uncertainty | Ranking is a ubiquitous method for focusing the attention of human evaluators
on a manageable subset of options. Its use ranges from surfacing potentially
relevant products on an e-commerce site to prioritizing college applications
for human review. While ranking can make human evaluation far more effective by
focusing attention on the most promising options, we argue that it can
introduce unfairness if the uncertainty of the underlying relevance model
differs between groups of options. Unfortunately, such disparity in uncertainty
appears widespread, since the relevance estimates for minority groups tend to
have higher uncertainty due to a lack of data or appropriate features. To
overcome this fairness issue, we propose Equal-Opportunity Ranking (EOR) as a
new fairness criterion for ranking that provably corrects for the disparity in
uncertainty between groups. Furthermore, we present a practical algorithm for
computing EOR rankings in time $O(n \log(n))$ and prove its close approximation
guarantee to the globally optimal solution. In a comprehensive empirical
evaluation on synthetic data, a US Census dataset, and a real-world case study
of Amazon search queries, we find that the algorithm reliably guarantees EOR
fairness while providing effective rankings. | [
"Richa Rastogi",
"Thorsten Joachims"
] | 2023-09-04 13:49:48 | http://arxiv.org/abs/2309.01610v1 | http://arxiv.org/pdf/2309.01610v1 | 2309.01610v1 |
Drifter: Efficient Online Feature Monitoring for Improved Data Integrity in Large-Scale Recommendation Systems | Real-world production systems often grapple with maintaining data quality in
large-scale, dynamic streams. We introduce Drifter, an efficient and
lightweight system for online feature monitoring and verification in
recommendation use cases. Drifter addresses limitations of existing methods by
delivering agile, responsive, and adaptable data quality monitoring, enabling
real-time root cause analysis, drift detection and insights into problematic
production events. Integrating state-of-the-art online feature ranking for
sparse data and anomaly detection ideas, Drifter is highly scalable and
resource-efficient, requiring only two threads and less than a gigabyte of RAM
per production deployments that handle millions of instances per minute.
Evaluation on real-world data sets demonstrates Drifter's effectiveness in
alerting and mitigating data quality issues, substantially improving
reliability and performance of real-time live recommender systems. | [
"Blaž Škrlj",
"Nir Ki-Tov",
"Lee Edelist",
"Natalia Silberstein",
"Hila Weisman-Zohar",
"Blaž Mramor",
"Davorin Kopič",
"Naama Ziporin"
] | 2023-09-04 13:33:36 | http://arxiv.org/abs/2309.08617v2 | http://arxiv.org/pdf/2309.08617v2 | 2309.08617v2 |
Efficient Social Choice via NLP and Sampling | Attention-Aware Social Choice tackles the fundamental conflict faced by some
agent communities between their desire to include all members in the decision
making processes and the limited time and attention that are at the disposal of
the community members. Here, we investigate a combination of two techniques for
attention-aware social choice, namely Natural Language Processing (NLP) and
Sampling. Essentially, we propose a system in which each governance proposal to
change the status quo is first sent to a trained NLP model that estimates the
probability that the proposal would pass if all community members directly vote
on it; then, based on such an estimation, a population sample of a certain size
is being selected and the proposal is decided upon by taking the sample
majority. We develop several concrete algorithms following the scheme described
above and evaluate them using various data, including such from several
Decentralized Autonomous Organizations (DAOs). | [
"Lior Ashkenazy",
"Nimrod Talmon"
] | 2023-09-04 13:30:31 | http://arxiv.org/abs/2309.12360v1 | http://arxiv.org/pdf/2309.12360v1 | 2309.12360v1 |
Active flow control for three-dimensional cylinders through deep reinforcement learning | This paper presents for the first time successful results of active flow
control with multiple independently controlled zero-net-mass-flux synthetic
jets. The jets are placed on a three-dimensional cylinder along its span with
the aim of reducing the drag coefficient. The method is based on a
deep-reinforcement-learning framework that couples a
computational-fluid-dynamics solver with an agent using the
proximal-policy-optimization algorithm. We implement a multi-agent
reinforcement-learning framework which offers numerous advantages: it exploits
local invariants, makes the control adaptable to different geometries,
facilitates transfer learning and cross-application of agents and results in
significant training speedup. In this contribution we report significant drag
reduction after applying the DRL-based control in three different
configurations of the problem. | [
"Pol Suárez",
"Francisco Alcántara-Ávila",
"Arnau Miró",
"Jean Rabault",
"Bernat Font",
"Oriol Lehmkuhl",
"R. Vinuesa"
] | 2023-09-04 13:30:29 | http://arxiv.org/abs/2309.02462v1 | http://arxiv.org/pdf/2309.02462v1 | 2309.02462v1 |
Subsets and Splits