title stringlengths 9 208 | abstract stringlengths 280 2.36k | authors sequence | published stringlengths 19 19 | url stringlengths 33 33 | pdf_url stringlengths 33 33 | arxiv_id stringlengths 12 12 |
|---|---|---|---|---|---|---|
Ghost on the Shell: An Expressive Representation of General 3D Shapes | The creation of photorealistic virtual worlds requires the accurate modeling
of 3D surface geometry for a wide range of objects. For this, meshes are
appealing since they 1) enable fast physics-based rendering with realistic
material and lighting, 2) support physical simulation, and 3) are
memory-efficient for modern g... | [
"Zhen Liu",
"Yao Feng",
"Yuliang Xiu",
"Weiyang Liu",
"Liam Paull",
"Michael J. Black",
"Bernhard Schölkopf"
] | 2023-10-23 17:59:52 | http://arxiv.org/abs/2310.15168v1 | http://arxiv.org/pdf/2310.15168v1 | 2310.15168v1 |
Handling Data Heterogeneity via Architectural Design for Federated Visual Recognition | Federated Learning (FL) is a promising research paradigm that enables the
collaborative training of machine learning models among various parties without
the need for sensitive information exchange. Nonetheless, retaining data in
individual clients introduces fundamental challenges to achieving performance
on par with ... | [
"Sara Pieri",
"Jose Renato Restom",
"Samuel Horvath",
"Hisham Cholakkal"
] | 2023-10-23 17:59:16 | http://arxiv.org/abs/2310.15165v1 | http://arxiv.org/pdf/2310.15165v1 | 2310.15165v1 |
Linear Representations of Sentiment in Large Language Models | Sentiment is a pervasive feature in natural language text, yet it is an open
question how sentiment is represented within Large Language Models (LLMs). In
this study, we reveal that across a range of models, sentiment is represented
linearly: a single direction in activation space mostly captures the feature
across a r... | [
"Curt Tigges",
"Oskar John Hollinsworth",
"Atticus Geiger",
"Neel Nanda"
] | 2023-10-23 17:55:31 | http://arxiv.org/abs/2310.15154v1 | http://arxiv.org/pdf/2310.15154v1 | 2310.15154v1 |
Verb Conjugation in Transformers Is Determined by Linear Encodings of Subject Number | Deep architectures such as Transformers are sometimes criticized for having
uninterpretable "black-box" representations. We use causal intervention
analysis to show that, in fact, some linguistic features are represented in a
linear, interpretable format. Specifically, we show that BERT's ability to
conjugate verbs rel... | [
"Sophie Hao",
"Tal Linzen"
] | 2023-10-23 17:53:47 | http://arxiv.org/abs/2310.15151v1 | http://arxiv.org/pdf/2310.15151v1 | 2310.15151v1 |
Online Detection of AI-Generated Images | With advancements in AI-generated images coming on a continuous basis, it is
increasingly difficult to distinguish traditionally-sourced images (e.g.,
photos, artwork) from AI-generated ones. Previous detection methods study the
generalization from a single generator to another in isolation. However, in
reality, new ge... | [
"David C. Epstein",
"Ishan Jain",
"Oliver Wang",
"Richard Zhang"
] | 2023-10-23 17:53:14 | http://arxiv.org/abs/2310.15150v1 | http://arxiv.org/pdf/2310.15150v1 | 2310.15150v1 |
Unlocking the Transferability of Tokens in Deep Models for Tabular Data | Fine-tuning a pre-trained deep neural network has become a successful
paradigm in various machine learning tasks. However, such a paradigm becomes
particularly challenging with tabular data when there are discrepancies between
the feature sets of pre-trained models and the target tasks. In this paper, we
propose TabTok... | [
"Qi-Le Zhou",
"Han-Jia Ye",
"Le-Ye Wang",
"De-Chuan Zhan"
] | 2023-10-23 17:53:09 | http://arxiv.org/abs/2310.15149v1 | http://arxiv.org/pdf/2310.15149v1 | 2310.15149v1 |
Robot Fine-Tuning Made Easy: Pre-Training Rewards and Policies for Autonomous Real-World Reinforcement Learning | The pre-train and fine-tune paradigm in machine learning has had dramatic
success in a wide range of domains because the use of existing data or
pre-trained models on the internet enables quick and easy learning of new
tasks. We aim to enable this paradigm in robotic reinforcement learning,
allowing a robot to learn a ... | [
"Jingyun Yang",
"Max Sobol Mark",
"Brandon Vu",
"Archit Sharma",
"Jeannette Bohg",
"Chelsea Finn"
] | 2023-10-23 17:50:08 | http://arxiv.org/abs/2310.15145v1 | http://arxiv.org/pdf/2310.15145v1 | 2310.15145v1 |
Hyperparameter optimization of hp-greedy reduced basis for gravitational wave surrogates | In a previous work we introduced, in the context of gravitational wave
science, an initial study on an automated domain-decomposition approach for
reduced basis through hp-greedy refinement. The approach constructs local
reduced bases of lower dimensionality than global ones, with the same or higher
accuracy. These ``l... | [
"Franco Cerino",
"Andrés Diaz-Pace",
"Emmanuel Tassone",
"Manuel Tiglio",
"Atuel Villegas"
] | 2023-10-23 17:48:11 | http://arxiv.org/abs/2310.15143v1 | http://arxiv.org/pdf/2310.15143v1 | 2310.15143v1 |
SpecTr: Fast Speculative Decoding via Optimal Transport | Autoregressive sampling from large language models has led to
state-of-the-art results in several natural language tasks. However,
autoregressive sampling generates tokens one at a time making it slow, and even
prohibitive in certain tasks. One way to speed up sampling is
$\textit{speculative decoding}$: use a small mo... | [
"Ziteng Sun",
"Ananda Theertha Suresh",
"Jae Hun Ro",
"Ahmad Beirami",
"Himanshu Jain",
"Felix Yu"
] | 2023-10-23 17:47:34 | http://arxiv.org/abs/2310.15141v1 | http://arxiv.org/pdf/2310.15141v1 | 2310.15141v1 |
AutoDAN: Automatic and Interpretable Adversarial Attacks on Large Language Models | Safety alignment of Large Language Models (LLMs) can be compromised with
manual jailbreak attacks and (automatic) adversarial attacks. Recent work
suggests that patching LLMs against these attacks is possible: manual jailbreak
attacks are human-readable but often limited and public, making them easy to
block; adversari... | [
"Sicheng Zhu",
"Ruiyi Zhang",
"Bang An",
"Gang Wu",
"Joe Barrow",
"Zichao Wang",
"Furong Huang",
"Ani Nenkova",
"Tong Sun"
] | 2023-10-23 17:46:07 | http://arxiv.org/abs/2310.15140v1 | http://arxiv.org/pdf/2310.15140v1 | 2310.15140v1 |
Quantifying the Dialect Gap and its Correlates Across Languages | Historically, researchers and consumers have noticed a decrease in quality
when applying NLP tools to minority variants of languages (i.e. Puerto Rican
Spanish or Swiss German), but studies exploring this have been limited to a
select few languages. Additionally, past studies have mainly been conducted in
a monolingual... | [
"Anjali Kantharuban",
"Ivan Vulić",
"Anna Korhonen"
] | 2023-10-23 17:42:01 | http://arxiv.org/abs/2310.15135v1 | http://arxiv.org/pdf/2310.15135v1 | 2310.15135v1 |
Location-Aware Visual Question Generation with Lightweight Models | This work introduces a novel task, location-aware visual question generation
(LocaVQG), which aims to generate engaging questions from data relevant to a
particular geographical location. Specifically, we represent such
location-aware information with surrounding images and a GPS coordinate. To
tackle this task, we pre... | [
"Nicholas Collin Suwono",
"Justin Chih-Yao Chen",
"Tun Min Hung",
"Ting-Hao Kenneth Huang",
"I-Bin Liao",
"Yung-Hui Li",
"Lun-Wei Ku",
"Shao-Hua Sun"
] | 2023-10-23 17:33:31 | http://arxiv.org/abs/2310.15129v1 | http://arxiv.org/pdf/2310.15129v1 | 2310.15129v1 |
Projected Stochastic Gradient Descent with Quantum Annealed Binary Gradients | We present, QP-SBGD, a novel layer-wise stochastic optimiser tailored towards
training neural networks with binary weights, known as binary neural networks
(BNNs), on quantum hardware. BNNs reduce the computational requirements and
energy consumption of deep learning models with minimal loss in accuracy.
However, train... | [
"Maximilian Krahn",
"Michelle Sasdelli",
"Fengyi Yang",
"Vladislav Golyanik",
"Juho Kannala",
"Tat-Jun Chin",
"Tolga Birdal"
] | 2023-10-23 17:32:38 | http://arxiv.org/abs/2310.15128v1 | http://arxiv.org/pdf/2310.15128v1 | 2310.15128v1 |
Open-Ended Instructable Embodied Agents with Memory-Augmented Large Language Models | Pre-trained and frozen LLMs can effectively map simple scene re-arrangement
instructions to programs over a robot's visuomotor functions through
appropriate few-shot example prompting. To parse open-domain natural language
and adapt to a user's idiosyncratic procedures, not known during prompt
engineering time, fixed p... | [
"Gabriel Sarch",
"Yue Wu",
"Michael J. Tarr",
"Katerina Fragkiadaki"
] | 2023-10-23 17:31:55 | http://arxiv.org/abs/2310.15127v1 | http://arxiv.org/pdf/2310.15127v1 | 2310.15127v1 |
Mixed-Variable Global Sensitivity Analysis For Knowledge Discovery And Efficient Combinatorial Materials Design | Global Sensitivity Analysis (GSA) is the study of the influence of any given
inputs on the outputs of a model. In the context of engineering design, GSA has
been widely used to understand both individual and collective contributions of
design variables on the design objectives. So far, global sensitivity studies
have o... | [
"Yigitcan Comlek",
"Liwei Wang",
"Wei Chen"
] | 2023-10-23 17:29:53 | http://arxiv.org/abs/2310.15124v1 | http://arxiv.org/pdf/2310.15124v1 | 2310.15124v1 |
Branch-Solve-Merge Improves Large Language Model Evaluation and Generation | Large Language Models (LLMs) are frequently used for multi-faceted language
generation and evaluation tasks that involve satisfying intricate user
constraints or taking into account multiple aspects and criteria. However,
their performance can fall short, due to the model's lack of coherence and
inability to plan and d... | [
"Swarnadeep Saha",
"Omer Levy",
"Asli Celikyilmaz",
"Mohit Bansal",
"Jason Weston",
"Xian Li"
] | 2023-10-23 17:29:48 | http://arxiv.org/abs/2310.15123v1 | http://arxiv.org/pdf/2310.15123v1 | 2310.15123v1 |
Matryoshka Diffusion Models | Diffusion models are the de facto approach for generating high-quality images
and videos, but learning high-dimensional models remains a formidable task due
to computational and optimization challenges. Existing methods often resort to
training cascaded models in pixel space or using a downsampled latent space of
a sep... | [
"Jiatao Gu",
"Shuangfei Zhai",
"Yizhe Zhang",
"Josh Susskind",
"Navdeep Jaitly"
] | 2023-10-23 17:20:01 | http://arxiv.org/abs/2310.15111v1 | http://arxiv.org/pdf/2310.15111v1 | 2310.15111v1 |
Evaluating machine learning models in non-standard settings: An overview and new findings | Estimating the generalization error (GE) of machine learning models is
fundamental, with resampling methods being the most common approach. However,
in non-standard settings, particularly those where observations are not
independently and identically distributed, resampling using simple random data
divisions may lead t... | [
"Roman Hornung",
"Malte Nalenz",
"Lennart Schneider",
"Andreas Bender",
"Ludwig Bothmann",
"Bernd Bischl",
"Thomas Augustin",
"Anne-Laure Boulesteix"
] | 2023-10-23 17:15:11 | http://arxiv.org/abs/2310.15108v1 | http://arxiv.org/pdf/2310.15108v1 | 2310.15108v1 |
Dual-path convolutional neural network using micro-FTIR imaging to predict breast cancer subtypes and biomarkers levels: estrogen receptor, progesterone receptor, HER2 and Ki67 | Breast cancer molecular subtypes classification plays an import role to sort
patients with divergent prognosis. The biomarkers used are Estrogen Receptor
(ER), Progesterone Receptor (PR), HER2, and Ki67. Based on these biomarkers
expression levels, subtypes are classified as Luminal A (LA), Luminal B (LB),
HER2 subtype... | [
"Matheus del-Valle",
"Emerson Soares Bernardes",
"Denise Maria Zezell"
] | 2023-10-23 17:05:53 | http://arxiv.org/abs/2310.15099v1 | http://arxiv.org/pdf/2310.15099v1 | 2310.15099v1 |
A Canonical Data Transformation for Achieving Inter- and Within-group Fairness | Increases in the deployment of machine learning algorithms for applications
that deal with sensitive data have brought attention to the issue of fairness
in machine learning. Many works have been devoted to applications that require
different demographic groups to be treated fairly. However, algorithms that aim
to sati... | [
"Zachary McBride Lazri",
"Ivan Brugere",
"Xin Tian",
"Dana Dachman-Soled",
"Antigoni Polychroniadou",
"Danial Dervovic",
"Min Wu"
] | 2023-10-23 17:00:20 | http://arxiv.org/abs/2310.15097v1 | http://arxiv.org/pdf/2310.15097v1 | 2310.15097v1 |
One-dimensional convolutional neural network model for breast cancer subtypes classification and biochemical content evaluation using micro-FTIR hyperspectral images | Breast cancer treatment still remains a challenge, where molecular subtypes
classification plays a crucial role in selecting appropriate and specific
therapy. The four subtypes are Luminal A (LA), Luminal B (LB), HER2 subtype,
and Triple-Negative Breast Cancer (TNBC). Immunohistochemistry is the
gold-standard evaluatio... | [
"Matheus del-Valle",
"Emerson Soares Bernardes",
"Denise Maria Zezell"
] | 2023-10-23 16:58:34 | http://arxiv.org/abs/2310.15094v1 | http://arxiv.org/pdf/2310.15094v1 | 2310.15094v1 |
On the Detection of Image-Scaling Attacks in Machine Learning | Image scaling is an integral part of machine learning and computer vision
systems. Unfortunately, this preprocessing step is vulnerable to so-called
image-scaling attacks where an attacker makes unnoticeable changes to an image
so that it becomes a new image after scaling. This opens up new ways for
attackers to contro... | [
"Erwin Quiring",
"Andreas Müller",
"Konrad Rieck"
] | 2023-10-23 16:46:28 | http://arxiv.org/abs/2310.15085v1 | http://arxiv.org/pdf/2310.15085v1 | 2310.15085v1 |
Quantum Federated Learning With Quantum Networks | A major concern of deep learning models is the large amount of data that is
required to build and train them, much of which is reliant on sensitive and
personally identifiable information that is vulnerable to access by third
parties. Ideas of using the quantum internet to address this issue have been
previously propos... | [
"Tyler Wang",
"Huan-Hsin Tseng",
"Shinjae Yoo"
] | 2023-10-23 16:45:29 | http://arxiv.org/abs/2310.15084v1 | http://arxiv.org/pdf/2310.15084v1 | 2310.15084v1 |
Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization | Federated learning (FL) is a promising paradigm to enable collaborative model
training with decentralized data. However, the training process of Large
Language Models (LLMs) generally incurs the update of significant parameters,
which limits the applicability of FL techniques to tackle the LLMs in real
scenarios. Promp... | [
"Tianshi Che",
"Ji Liu",
"Yang Zhou",
"Jiaxiang Ren",
"Jiwen Zhou",
"Victor S. Sheng",
"Huaiyu Dai",
"Dejing Dou"
] | 2023-10-23 16:37:59 | http://arxiv.org/abs/2310.15080v1 | http://arxiv.org/pdf/2310.15080v1 | 2310.15080v1 |
MGAS: Multi-Granularity Architecture Search for Effective and Efficient Neural Networks | Differentiable architecture search (DAS) has become the prominent approach in
the field of neural architecture search (NAS) due to its time-efficient
automation of neural network design. It shifts the traditional paradigm of
discrete architecture sampling and evaluation to differentiable super-net
optimization and disc... | [
"Xiaoyun Liu",
"Divya Saxena",
"Jiannong Cao",
"Yuqing Zhao",
"Penghui Ruan"
] | 2023-10-23 16:32:18 | http://arxiv.org/abs/2310.15074v1 | http://arxiv.org/pdf/2310.15074v1 | 2310.15074v1 |
Robot Skill Generalization via Keypoint Integrated Soft Actor-Critic Gaussian Mixture Models | A long-standing challenge for a robotic manipulation system operating in
real-world scenarios is adapting and generalizing its acquired motor skills to
unseen environments. We tackle this challenge employing hybrid skill models
that integrate imitation and reinforcement paradigms, to explore how the
learning and adapta... | [
"Iman Nematollahi",
"Kirill Yankov",
"Wolfram Burgard",
"Tim Welschehold"
] | 2023-10-23 16:03:23 | http://arxiv.org/abs/2310.15059v1 | http://arxiv.org/pdf/2310.15059v1 | 2310.15059v1 |
Coordinated Replay Sample Selection for Continual Federated Learning | Continual Federated Learning (CFL) combines Federated Learning (FL), the
decentralized learning of a central model on a number of client devices that
may not communicate their data, and Continual Learning (CL), the learning of a
model from a continual stream of data without keeping the entire history. In
CL, the main c... | [
"Jack Good",
"Jimit Majmudar",
"Christophe Dupuy",
"Jixuan Wang",
"Charith Peris",
"Clement Chung",
"Richard Zemel",
"Rahul Gupta"
] | 2023-10-23 15:56:39 | http://arxiv.org/abs/2310.15054v1 | http://arxiv.org/pdf/2310.15054v1 | 2310.15054v1 |
TeleQnA: A Benchmark Dataset to Assess Large Language Models Telecommunications Knowledge | We introduce TeleQnA, the first benchmark dataset designed to evaluate the
knowledge of Large Language Models (LLMs) in telecommunications. Comprising
10,000 questions and answers, this dataset draws from diverse sources,
including standards and research articles. This paper outlines the automated
question generation f... | [
"Ali Maatouk",
"Fadhel Ayed",
"Nicola Piovesan",
"Antonio De Domenico",
"Merouane Debbah",
"Zhi-Quan Luo"
] | 2023-10-23 15:55:15 | http://arxiv.org/abs/2310.15051v1 | http://arxiv.org/pdf/2310.15051v1 | 2310.15051v1 |
Meta- (out-of-context) learning in neural networks | Brown et al. (2020) famously introduced the phenomenon of in-context learning
in large language models (LLMs). We establish the existence of a phenomenon we
call $\textbf{meta-out-of-context learning (meta-OCL)}$ via carefully designed
synthetic experiments with LLMs. Our results suggest that meta-OCL leads LLMs
to mor... | [
"Dmitrii Krasheninnikov",
"Egor Krasheninnikov",
"Bruno Mlodozeniec",
"David Krueger"
] | 2023-10-23 15:50:08 | http://arxiv.org/abs/2310.15047v1 | http://arxiv.org/pdf/2310.15047v1 | 2310.15047v1 |
Deep Autoencoder-based Z-Interference Channels with Perfect and Imperfect CSI | A deep autoencoder (DAE)-based structure for endto-end communication over the
two-user Z-interference channel (ZIC) with finite-alphabet inputs is designed
in this paper. The proposed structure jointly optimizes the two encoder/decoder
pairs and generates interference-aware constellations that dynamically adapt
their s... | [
"Xinliang Zhang",
"Mojtaba Vaezi"
] | 2023-10-23 15:23:42 | http://arxiv.org/abs/2310.15027v1 | http://arxiv.org/pdf/2310.15027v1 | 2310.15027v1 |
Fast 2D Bicephalous Convolutional Autoencoder for Compressing 3D Time Projection Chamber Data | High-energy large-scale particle colliders produce data at high speed in the
order of 1 terabytes per second in nuclear physics and petabytes per second in
high-energy physics. Developing real-time data compression algorithms to reduce
such data at high throughput to fit permanent storage has drawn increasing
attention... | [
"Yi Huang",
"Yihui Ren",
"Shinjae Yoo",
"Jin Huang"
] | 2023-10-23 15:23:32 | http://arxiv.org/abs/2310.15026v1 | http://arxiv.org/pdf/2310.15026v1 | 2310.15026v1 |
Invariance is Key to Generalization: Examining the Role of Representation in Sim-to-Real Transfer for Visual Navigation | The data-driven approach to robot control has been gathering pace rapidly,
yet generalization to unseen task domains remains a critical challenge. We
argue that the key to generalization is representations that are (i) rich
enough to capture all task-relevant information and (ii) invariant to
superfluous variability be... | [
"Bo Ai",
"Zhanxin Wu",
"David Hsu"
] | 2023-10-23 15:15:19 | http://arxiv.org/abs/2310.15020v1 | http://arxiv.org/pdf/2310.15020v1 | 2310.15020v1 |
Meta learning with language models: Challenges and opportunities in the classification of imbalanced text | Detecting out of policy speech (OOPS) content is important but difficult.
While machine learning is a powerful tool to tackle this challenging task, it
is hard to break the performance ceiling due to factors like quantity and
quality limitations on training data and inconsistencies in OOPS definition and
data labeling.... | [
"Apostol Vassilev",
"Honglan Jin",
"Munawar Hasan"
] | 2023-10-23 15:14:55 | http://arxiv.org/abs/2310.15019v1 | http://arxiv.org/pdf/2310.15019v1 | 2310.15019v1 |
The primacy bias in Model-based RL | The primacy bias in deep reinforcement learning (DRL), which refers to the
agent's tendency to overfit early data and lose the ability to learn from new
data, can significantly decrease the performance of DRL algorithms. Previous
studies have shown that employing simple techniques, such as resetting the
agent's paramet... | [
"Zhongjian Qiao",
"Jiafei Lyu",
"Xiu Li"
] | 2023-10-23 15:12:20 | http://arxiv.org/abs/2310.15017v1 | http://arxiv.org/pdf/2310.15017v1 | 2310.15017v1 |
Leveraging Deep Learning for Abstractive Code Summarization of Unofficial Documentation | Usually, programming languages have official documentation to guide
developers with APIs, methods, and classes. However, researchers identified
insufficient or inadequate documentation examples and flaws with the API's
complex structure as barriers to learning an API. As a result, developers may
consult other sources (... | [
"AmirHossein Naghshzan",
"Latifa Guerrouj",
"Olga Baysal"
] | 2023-10-23 15:10:37 | http://arxiv.org/abs/2310.15015v1 | http://arxiv.org/pdf/2310.15015v1 | 2310.15015v1 |
Did the Neurons Read your Book? Document-level Membership Inference for Large Language Models | With large language models (LLMs) poised to become embedded in our daily
lives, questions are starting to be raised about the dataset(s) they learned
from. These questions range from potential bias or misinformation LLMs could
retain from their training data to questions of copyright and fair use of
human-generated tex... | [
"Matthieu Meeus",
"Shubham Jain",
"Marek Rei",
"Yves-Alexandre de Montjoye"
] | 2023-10-23 15:00:46 | http://arxiv.org/abs/2310.15007v1 | http://arxiv.org/pdf/2310.15007v1 | 2310.15007v1 |
Neural Snowflakes: Universal Latent Graph Inference via Trainable Latent Geometries | The inductive bias of a graph neural network (GNN) is largely encoded in its
specified graph. Latent graph inference relies on latent geometric
representations to dynamically rewire or infer a GNN's graph to maximize the
GNN's predictive downstream performance, but it lacks solid theoretical
foundations in terms of emb... | [
"Haitz Sáez de Ocáriz Borde",
"Anastasis Kratsios"
] | 2023-10-23 14:57:26 | http://arxiv.org/abs/2310.15003v1 | http://arxiv.org/pdf/2310.15003v1 | 2310.15003v1 |
Simple Hardware-Efficient PCFGs with Independent Left and Right Productions | Scaling dense PCFGs to thousands of nonterminals via a low-rank
parameterization of the rule probability tensor has been shown to be beneficial
for unsupervised parsing. However, PCFGs scaled this way still perform poorly
as a language model, and even underperform similarly-sized HMMs. This work
introduces \emph{Simple... | [
"Wei Liu",
"Songlin Yang",
"Yoon Kim",
"Kewei Tu"
] | 2023-10-23 14:48:51 | http://arxiv.org/abs/2310.14997v1 | http://arxiv.org/pdf/2310.14997v1 | 2310.14997v1 |
Understanding the Inner Workings of Language Models Through Representation Dissimilarity | As language models are applied to an increasing number of real-world
applications, understanding their inner workings has become an important issue
in model trust, interpretability, and transparency. In this work we show that
representation dissimilarity measures, which are functions that measure the
extent to which tw... | [
"Davis Brown",
"Charles Godfrey",
"Nicholas Konz",
"Jonathan Tu",
"Henry Kvinge"
] | 2023-10-23 14:46:20 | http://arxiv.org/abs/2310.14993v1 | http://arxiv.org/pdf/2310.14993v1 | 2310.14993v1 |
Bayesian Regression Markets | Machine learning tasks are vulnerable to the quality of data used as input.
Yet, it is often challenging for firms to obtain adequate datasets, with them
being naturally distributed amongst owners, that in practice, may be
competitors in a downstream market and reluctant to share information. Focusing
on supervised lea... | [
"Thomas Falconer",
"Jalal Kazempour",
"Pierre Pinson"
] | 2023-10-23 14:45:51 | http://arxiv.org/abs/2310.14992v1 | http://arxiv.org/pdf/2310.14992v1 | 2310.14992v1 |
Delayed Memory Unit: Modelling Temporal Dependency Through Delay Gate | Recurrent Neural Networks (RNNs) are renowned for their adeptness in modeling
temporal dependencies, a trait that has driven their widespread adoption for
sequential data processing. Nevertheless, vanilla RNNs are confronted with the
well-known issue of gradient vanishing and exploding, posing a significant
challenge f... | [
"Pengfei Sun",
"Jibin Wu",
"Malu Zhang",
"Paul Devos",
"Dick Botteldooren"
] | 2023-10-23 14:29:48 | http://arxiv.org/abs/2310.14982v1 | http://arxiv.org/pdf/2310.14982v1 | 2310.14982v1 |
ACTOR: Active Learning with Annotator-specific Classification Heads to Embrace Human Label Variation | Label aggregation such as majority voting is commonly used to resolve
annotator disagreement in dataset creation. However, this may disregard
minority values and opinions. Recent studies indicate that learning from
individual annotations outperforms learning from aggregated labels, though they
require a considerable am... | [
"Xinpeng Wang",
"Barbara Plank"
] | 2023-10-23 14:26:43 | http://arxiv.org/abs/2310.14979v1 | http://arxiv.org/pdf/2310.14979v1 | 2310.14979v1 |
Reinforcement learning in large, structured action spaces: A simulation study of decision support for spinal cord injury rehabilitation | Reinforcement learning (RL) has helped improve decision-making in several
applications. However, applying traditional RL is challenging in some
applications, such as rehabilitation of people with a spinal cord injury (SCI).
Among other factors, using RL in this domain is difficult because there are
many possible treatm... | [
"Nathan Phelps",
"Stephanie Marrocco",
"Stephanie Cornell",
"Dalton L. Wolfe",
"Daniel J. Lizotte"
] | 2023-10-23 14:25:55 | http://arxiv.org/abs/2310.14976v1 | http://arxiv.org/pdf/2310.14976v1 | 2310.14976v1 |
The Fundamental Dilemma of Bayesian Active Meta-learning | Many applications involve estimation of parameters that generalize across
multiple diverse, but related, data-scarce task environments. Bayesian active
meta-learning, a form of sequential optimal experimental design, provides a
framework for solving such problems. The active meta-learner's goal is to gain
transferable ... | [
"Sabina J. Sloman",
"Ayush Bharti",
"Samuel Kaski"
] | 2023-10-23 14:13:27 | http://arxiv.org/abs/2310.14968v1 | http://arxiv.org/pdf/2310.14968v1 | 2310.14968v1 |
Adam through a Second-Order Lens | Research into optimisation for deep learning is characterised by a tension
between the computational efficiency of first-order, gradient-based methods
(such as SGD and Adam) and the theoretical efficiency of second-order,
curvature-based methods (such as quasi-Newton methods and K-FAC). We seek to
combine the benefits ... | [
"Ross M. Clarke",
"Baiyu Su",
"José Miguel Hernández-Lobato"
] | 2023-10-23 14:06:46 | http://arxiv.org/abs/2310.14963v1 | http://arxiv.org/pdf/2310.14963v1 | 2310.14963v1 |
StenUNet: Automatic Stenosis Detection from X-ray Coronary Angiography | Coronary angiography continues to serve as the primary method for diagnosing
coronary artery disease (CAD), which is the leading global cause of mortality.
The severity of CAD is quantified by the location, degree of narrowing
(stenosis), and number of arteries involved. In current practice, this
quantification is perf... | [
"Hui Lin",
"Tom Liu",
"Aggelos Katsaggelos",
"Adrienne Kline"
] | 2023-10-23 14:04:18 | http://arxiv.org/abs/2310.14961v1 | http://arxiv.org/pdf/2310.14961v1 | 2310.14961v1 |
XTSC-Bench: Quantitative Benchmarking for Explainers on Time Series Classification | Despite the growing body of work on explainable machine learning in time
series classification (TSC), it remains unclear how to evaluate different
explainability methods. Resorting to qualitative assessment and user studies to
evaluate explainers for TSC is difficult since humans have difficulties
understanding the und... | [
"Jacqueline Höllig",
"Steffen Thoma",
"Florian Grimm"
] | 2023-10-23 14:00:02 | http://arxiv.org/abs/2310.14957v1 | http://arxiv.org/pdf/2310.14957v1 | 2310.14957v1 |
Causal machine learning for single-cell genomics | Advances in single-cell omics allow for unprecedented insights into the
transcription profiles of individual cells. When combined with large-scale
perturbation screens, through which specific biological mechanisms can be
targeted, these technologies allow for measuring the effect of targeted
perturbations on the whole ... | [
"Alejandro Tejada-Lapuerta",
"Paul Bertin",
"Stefan Bauer",
"Hananeh Aliee",
"Yoshua Bengio",
"Fabian J. Theis"
] | 2023-10-23 13:35:24 | http://arxiv.org/abs/2310.14935v1 | http://arxiv.org/pdf/2310.14935v1 | 2310.14935v1 |
Robust Depth Linear Error Decomposition with Double Total Variation and Nuclear Norm for Dynamic MRI Reconstruction | Compressed Sensing (CS) significantly speeds up Magnetic Resonance Image
(MRI) processing and achieves accurate MRI reconstruction from under-sampled
k-space data. According to the current research, there are still several
problems with dynamic MRI k-space reconstruction based on CS. 1) There are
differences between th... | [
"Junpeng Tan",
"Chunmei Qing",
"Xiangmin Xu"
] | 2023-10-23 13:34:59 | http://arxiv.org/abs/2310.14934v1 | http://arxiv.org/pdf/2310.14934v1 | 2310.14934v1 |
Linking Surface Facts to Large-Scale Knowledge Graphs | Open Information Extraction (OIE) methods extract facts from natural language
text in the form of ("subject"; "relation"; "object") triples. These facts are,
however, merely surface forms, the ambiguity of which impedes their downstream
usage; e.g., the surface phrase "Michael Jordan" may refer to either the former
bas... | [
"Gorjan Radevski",
"Kiril Gashteovski",
"Chia-Chien Hung",
"Carolin Lawrence",
"Goran Glavaš"
] | 2023-10-23 13:18:49 | http://arxiv.org/abs/2310.14909v1 | http://arxiv.org/pdf/2310.14909v1 | 2310.14909v1 |
Series of Hessian-Vector Products for Tractable Saddle-Free Newton Optimisation of Neural Networks | Despite their popularity in the field of continuous optimisation,
second-order quasi-Newton methods are challenging to apply in machine learning,
as the Hessian matrix is intractably large. This computational burden is
exacerbated by the need to address non-convexity, for instance by modifying the
Hessian's eigenvalues... | [
"Elre T. Oldewage",
"Ross M. Clarke",
"José Miguel Hernández-Lobato"
] | 2023-10-23 13:11:30 | http://arxiv.org/abs/2310.14901v1 | http://arxiv.org/pdf/2310.14901v1 | 2310.14901v1 |
Local Universal Rule-based Explanations | Explainable artificial intelligence (XAI) is one of the most intensively
developed are of AI in recent years. It is also one of the most fragmented one
with multiple methods that focus on different aspects of explanations. This
makes difficult to obtain the full spectrum of explanation at once in a compact
and consiste... | [
"Szymon Bobek",
"Grzegorz J. Nalepa"
] | 2023-10-23 13:04:15 | http://arxiv.org/abs/2310.14894v1 | http://arxiv.org/pdf/2310.14894v1 | 2310.14894v1 |
Beyond Bayesian Model Averaging over Paths in Probabilistic Programs with Stochastic Support | The posterior in probabilistic programs with stochastic support decomposes as
a weighted sum of the local posterior distributions associated with each
possible program path. We show that making predictions with this full posterior
implicitly performs a Bayesian model averaging (BMA) over paths. This is
potentially prob... | [
"Tim Reichelt",
"Luke Ong",
"Tom Rainforth"
] | 2023-10-23 12:57:03 | http://arxiv.org/abs/2310.14888v1 | http://arxiv.org/pdf/2310.14888v1 | 2310.14888v1 |
A Study on Knowledge Graph Embeddings and Graph Neural Networks for Web Of Things | Graph data structures are widely used to store relational information between
several entities. With data being generated worldwide on a large scale, we see
a significant growth in the generation of knowledge graphs. Thing in the future
is Orange's take on a knowledge graph in the domain of the Web Of Things (WoT),
whe... | [
"Rohith Teja Mittakola",
"Thomas Hassan"
] | 2023-10-23 12:36:33 | http://arxiv.org/abs/2310.14866v1 | http://arxiv.org/pdf/2310.14866v1 | 2310.14866v1 |
Diverse Priors for Deep Reinforcement Learning | In Reinforcement Learning (RL), agents aim at maximizing cumulative rewards
in a given environment. During the learning process, RL agents face the dilemma
of exploitation and exploration: leveraging existing knowledge to acquire
rewards or seeking potentially higher ones. Using uncertainty as a guiding
principle provi... | [
"Chenfan Weng",
"Zhongguo Li"
] | 2023-10-23 12:33:59 | http://arxiv.org/abs/2310.14864v1 | http://arxiv.org/pdf/2310.14864v1 | 2310.14864v1 |
Dynamically Weighted Federated k-Means | Federated clustering is an important part of the field of federated machine
learning, that allows multiple data sources to collaboratively cluster their
data while keeping it decentralized and preserving privacy. In this paper, we
introduce a novel federated clustering algorithm, named Dynamically Weighted
Federated k-... | [
"Patrick Holzer",
"Tania Jacob",
"Shubham Kavane"
] | 2023-10-23 12:28:21 | http://arxiv.org/abs/2310.14858v1 | http://arxiv.org/pdf/2310.14858v1 | 2310.14858v1 |
Zero-knowledge Proof Meets Machine Learning in Verifiability: A Survey | With the rapid advancement of artificial intelligence technology, the usage
of machine learning models is gradually becoming part of our daily lives.
High-quality models rely not only on efficient optimization algorithms but also
on the training and learning processes built upon vast amounts of data and
computational p... | [
"Zhibo Xing",
"Zijian Zhang",
"Jiamou Liu",
"Ziang Zhang",
"Meng Li",
"Liehuang Zhu",
"Giovanni Russello"
] | 2023-10-23 12:15:23 | http://arxiv.org/abs/2310.14848v1 | http://arxiv.org/pdf/2310.14848v1 | 2310.14848v1 |
ULTRA-DP: Unifying Graph Pre-training with Multi-task Graph Dual Prompt | Recent research has demonstrated the efficacy of pre-training graph neural
networks (GNNs) to capture the transferable graph semantics and enhance the
performance of various downstream tasks. However, the semantic knowledge
learned from pretext tasks might be unrelated to the downstream task, leading
to a semantic gap ... | [
"Mouxiang Chen",
"Zemin Liu",
"Chenghao Liu",
"Jundong Li",
"Qiheng Mao",
"Jianling Sun"
] | 2023-10-23 12:11:13 | http://arxiv.org/abs/2310.14845v1 | http://arxiv.org/pdf/2310.14845v1 | 2310.14845v1 |
Calibration of Time-Series Forecasting Transformers: Detecting and Adapting Context-Driven Distribution Shift | Recent years have witnessed the success of introducing Transformers to time
series forecasting. From a data generation perspective, we illustrate that
existing Transformers are susceptible to distribution shifts driven by temporal
contexts, whether observed or unobserved. Such context-driven distribution
shift (CDS) in... | [
"Mouxiang Chen",
"Lefei Shen",
"Han Fu",
"Zhuo Li",
"Jianling Sun",
"Chenghao Liu"
] | 2023-10-23 11:58:01 | http://arxiv.org/abs/2310.14838v1 | http://arxiv.org/pdf/2310.14838v1 | 2310.14838v1 |
Harnessing Attention Mechanisms: Efficient Sequence Reduction using Attention-based Autoencoders | Many machine learning models use the manipulation of dimensions as a driving
force to enable models to identify and learn important features in data. In the
case of sequential data this manipulation usually happens on the token
dimension level. Despite the fact that many tasks require a change in sequence
length itself... | [
"Daniel Biermann",
"Fabrizio Palumbo",
"Morten Goodwin",
"Ole-Christoffer Granmo"
] | 2023-10-23 11:57:44 | http://arxiv.org/abs/2310.14837v1 | http://arxiv.org/pdf/2310.14837v1 | 2310.14837v1 |
Sharp error bounds for imbalanced classification: how many examples in the minority class? | When dealing with imbalanced classification data, reweighting the loss
function is a standard procedure allowing to equilibrate between the true
positive and true negative rates within the risk measure. Despite significant
theoretical work in this area, existing results do not adequately address a
main challenge within... | [
"Anass Aghbalou",
"François Portier",
"Anne Sabourin"
] | 2023-10-23 11:45:34 | http://arxiv.org/abs/2310.14826v1 | http://arxiv.org/pdf/2310.14826v1 | 2310.14826v1 |
Text2Topic: Multi-Label Text Classification System for Efficient Topic Detection in User Generated Content with Zero-Shot Capabilities | Multi-label text classification is a critical task in the industry. It helps
to extract structured information from large amount of textual data. We propose
Text to Topic (Text2Topic), which achieves high multi-label classification
performance by employing a Bi-Encoder Transformer architecture that utilizes
concatenati... | [
"Fengjun Wang",
"Moran Beladev",
"Ofri Kleinfeld",
"Elina Frayerman",
"Tal Shachar",
"Eran Fainman",
"Karen Lastmann Assaraf",
"Sarai Mizrachi",
"Benjamin Wang"
] | 2023-10-23 11:33:24 | http://arxiv.org/abs/2310.14817v1 | http://arxiv.org/pdf/2310.14817v1 | 2310.14817v1 |
Leveraging Ensemble Diversity for Robust Self-Training in the Presence of Sample Selection Bias | Self-training is a well-known approach for semi-supervised learning. It
consists of iteratively assigning pseudo-labels to unlabeled data for which the
model is confident and treating them as labeled examples. For neural networks,
softmax prediction probabilities are often used as a confidence measure,
despite the fact... | [
"Ambroise Odonnat",
"Vasilii Feofanov",
"Ievgen Redko"
] | 2023-10-23 11:30:06 | http://arxiv.org/abs/2310.14814v1 | http://arxiv.org/pdf/2310.14814v1 | 2310.14814v1 |
Learning spatio-temporal patterns with Neural Cellular Automata | Neural Cellular Automata (NCA) are a powerful combination of machine learning
and mechanistic modelling. We train NCA to learn complex dynamics from time
series of images and PDE trajectories. Our method is designed to identify
underlying local rules that govern large scale dynamic emergent behaviours.
Previous work on... | [
"Alex D. Richardson",
"Tibor Antal",
"Richard A. Blythe",
"Linus J. Schumacher"
] | 2023-10-23 11:16:32 | http://arxiv.org/abs/2310.14809v1 | http://arxiv.org/pdf/2310.14809v1 | 2310.14809v1 |
What do Deck Chairs and Sun Hats Have in Common? Uncovering Shared Properties in Large Concept Vocabularies | Concepts play a central role in many applications. This includes settings
where concepts have to be modelled in the absence of sentence context. Previous
work has therefore focused on distilling decontextualised concept embeddings
from language models. But concepts can be modelled from different perspectives,
whereas c... | [
"Amit Gajbhiye",
"Zied Bouraoui",
"Na Li",
"Usashi Chatterjee",
"Luis Espinosa Anke",
"Steven Schockaert"
] | 2023-10-23 10:53:25 | http://arxiv.org/abs/2310.14793v1 | http://arxiv.org/pdf/2310.14793v1 | 2310.14793v1 |
An Efficient Imbalance-Aware Federated Learning Approach for Wearable Healthcare with Autoregressive Ratio Observation | Widely available healthcare services are now getting popular because of
advancements in wearable sensing techniques and mobile edge computing. People's
health information is collected by edge devices such as smartphones and
wearable bands for further analysis on servers, then send back suggestions and
alerts for abnorm... | [
"Wenhao Yan",
"He Li",
"Kaoru Ota",
"Mianxiong Dong"
] | 2023-10-23 10:36:52 | http://arxiv.org/abs/2310.14784v1 | http://arxiv.org/pdf/2310.14784v1 | 2310.14784v1 |
Geographical Erasure in Language Generation | Large language models (LLMs) encode vast amounts of world knowledge. However,
since these models are trained on large swaths of internet data, they are at
risk of inordinately capturing information about dominant groups. This
imbalance can propagate into generated language. In this work, we study and
operationalise a f... | [
"Pola Schwöbel",
"Jacek Golebiowski",
"Michele Donini",
"Cédric Archambeau",
"Danish Pruthi"
] | 2023-10-23 10:26:14 | http://arxiv.org/abs/2310.14777v1 | http://arxiv.org/pdf/2310.14777v1 | 2310.14777v1 |
Principled Approaches for Learning to Defer with Multiple Experts | We present a study of surrogate losses and algorithms for the general problem
of learning to defer with multiple experts. We first introduce a new family of
surrogate losses specifically tailored for the multiple-expert setting, where
the prediction and deferral functions are learned simultaneously. We then prove
that ... | [
"Anqi Mao",
"Mehryar Mohri",
"Yutao Zhong"
] | 2023-10-23 10:19:09 | http://arxiv.org/abs/2310.14774v1 | http://arxiv.org/pdf/2310.14774v1 | 2310.14774v1 |
Predictor-Rejector Multi-Class Abstention: Theoretical Analysis and Algorithms | We study the key framework of learning with abstention in the multi-class
classification setting. In this setting, the learner can choose to abstain from
making a prediction with some pre-defined cost. We present a series of new
theoretical and algorithmic results for this learning problem in the
predictor-rejector fra... | [
"Anqi Mao",
"Mehryar Mohri",
"Yutao Zhong"
] | 2023-10-23 10:16:27 | http://arxiv.org/abs/2310.14772v1 | http://arxiv.org/pdf/2310.14772v1 | 2310.14772v1 |
Theoretically Grounded Loss Functions and Algorithms for Score-Based Multi-Class Abstention | Learning with abstention is a key scenario where the learner can abstain from
making a prediction at some cost. In this paper, we analyze the score-based
formulation of learning with abstention in the multi-class classification
setting. We introduce new families of surrogate losses for the abstention loss
function, whi... | [
"Anqi Mao",
"Mehryar Mohri",
"Yutao Zhong"
] | 2023-10-23 10:13:35 | http://arxiv.org/abs/2310.14770v1 | http://arxiv.org/pdf/2310.14770v1 | 2310.14770v1 |
Policy Gradient with Kernel Quadrature | Reward evaluation of episodes becomes a bottleneck in a broad range of
reinforcement learning tasks. Our aim in this paper is to select a small but
representative subset of a large batch of episodes, only on which we actually
compute rewards for more efficient policy gradient iterations. We build a
Gaussian process mod... | [
"Satoshi Hayakawa",
"Tetsuro Morimura"
] | 2023-10-23 10:12:23 | http://arxiv.org/abs/2310.14768v1 | http://arxiv.org/pdf/2310.14768v1 | 2310.14768v1 |
Improved K-mer Based Prediction of Protein-Protein Interactions With Chaos Game Representation, Deep Learning and Reduced Representation Bias | Protein-protein interactions drive many biological processes, including the
detection of phytopathogens by plants' R-Proteins and cell surface receptors.
Many machine learning studies have attempted to predict protein-protein
interactions but performance is highly dependent on training data; models have
been shown to a... | [
"Ruth Veevers",
"Dan MacLean"
] | 2023-10-23 10:02:23 | http://arxiv.org/abs/2310.14764v1 | http://arxiv.org/pdf/2310.14764v1 | 2310.14764v1 |
Externally Valid Policy Evaluation Combining Trial and Observational Data | Randomized trials are widely considered as the gold standard for evaluating
the effects of decision policies. Trial data is, however, drawn from a
population which may differ from the intended target population and this raises
a problem of external validity (aka. generalizability). In this paper we seek
to use trial da... | [
"Sofia Ek",
"Dave Zachariah"
] | 2023-10-23 10:01:50 | http://arxiv.org/abs/2310.14763v1 | http://arxiv.org/pdf/2310.14763v1 | 2310.14763v1 |
Rethinking Tokenizer and Decoder in Masked Graph Modeling for Molecules | Masked graph modeling excels in the self-supervised representation learning
of molecular graphs. Scrutinizing previous studies, we can reveal a common
scheme consisting of three key components: (1) graph tokenizer, which breaks a
molecular graph into smaller fragments (i.e., subgraphs) and converts them into
tokens; (2... | [
"Zhiyuan Liu",
"Yaorui Shi",
"An Zhang",
"Enzhi Zhang",
"Kenji Kawaguchi",
"Xiang Wang",
"Tat-Seng Chua"
] | 2023-10-23 09:40:30 | http://arxiv.org/abs/2310.14753v1 | http://arxiv.org/pdf/2310.14753v1 | 2310.14753v1 |
Efficient and Interpretable Bandit Algorithms | Motivated by the importance of explainability in modern machine learning, we
design bandit algorithms that are \emph{efficient} and \emph{interpretable}. A
bandit algorithm is interpretable if it explores with the objective of reducing
uncertainty in the unknown model parameter. To quantify the interpretability,
we int... | [
"Subhojyoti Mukherjee",
"Ruihao Zhu",
"Branislav Kveton"
] | 2023-10-23 09:36:13 | http://arxiv.org/abs/2310.14751v1 | http://arxiv.org/pdf/2310.14751v1 | 2310.14751v1 |
The Safety Challenges of Deep Learning in Real-World Type 1 Diabetes Management | Blood glucose simulation allows the effectiveness of type 1 diabetes (T1D)
management strategies to be evaluated without patient harm. Deep learning
algorithms provide a promising avenue for extending simulator capabilities;
however, these algorithms are limited in that they do not necessarily learn
physiologically cor... | [
"Harry Emerson",
"Ryan McConville",
"Matthew Guy"
] | 2023-10-23 09:25:50 | http://arxiv.org/abs/2310.14743v1 | http://arxiv.org/pdf/2310.14743v1 | 2310.14743v1 |
Extended Deep Adaptive Input Normalization for Preprocessing Time Series Data for Neural Networks | Data preprocessing is a crucial part of any machine learning pipeline, and it
can have a significant impact on both performance and training efficiency. This
is especially evident when using deep neural networks for time series
prediction and classification: real-world time series data often exhibit
irregularities such... | [
"Marcus A. K. September",
"Francesco Sanna Passino",
"Leonie Goldmann",
"Anton Hinel"
] | 2023-10-23 08:56:01 | http://arxiv.org/abs/2310.14720v1 | http://arxiv.org/pdf/2310.14720v1 | 2310.14720v1 |
BatteryML:An Open-source platform for Machine Learning on Battery Degradation | Battery degradation remains a pivotal concern in the energy storage domain,
with machine learning emerging as a potent tool to drive forward insights and
solutions. However, this intersection of electrochemical science and machine
learning poses complex challenges. Machine learning experts often grapple with
the intric... | [
"Han Zhang",
"Xiaofan Gui",
"Shun Zheng",
"Ziheng Lu",
"Yuqi Li",
"Jiang Bian"
] | 2023-10-23 08:51:05 | http://arxiv.org/abs/2310.14714v1 | http://arxiv.org/pdf/2310.14714v1 | 2310.14714v1 |
Random Forest Dissimilarity for High-Dimension Low Sample Size Classification | High dimension, low sample size (HDLSS) problems are numerous among
real-world applications of machine learning. From medical images to text
processing, traditional machine learning algorithms are usually unsuccessful in
learning the best possible concept from such data. In a previous work, we
proposed a dissimilarity-... | [
"Lucca Portes Cavalheiro",
"Simon Bernard",
"Jean Paul Barddal",
"Laurent Heutte"
] | 2023-10-23 08:49:39 | http://arxiv.org/abs/2310.14710v1 | http://arxiv.org/pdf/2310.14710v1 | 2310.14710v1 |
A Hybrid GNN approach for predicting node data for 3D meshes | Metal forging is used to manufacture dies. We require the best set of input
parameters for the process to be efficient. Currently, we predict the best
parameters using the finite element method by generating simulations for the
different initial conditions, which is a time-consuming process. In this paper,
introduce a ... | [
"Shwetha Salimath",
"Francesca Bugiotti",
"Frederic Magoules"
] | 2023-10-23 08:47:27 | http://arxiv.org/abs/2310.14707v1 | http://arxiv.org/pdf/2310.14707v1 | 2310.14707v1 |
Federated learning compression designed for lightweight communications | Federated Learning (FL) is a promising distributed method for edge-level
machine learning, particularly for privacysensitive applications such as those
in military and medical domains, where client data cannot be shared or
transferred to a cloud computing server. In many use-cases, communication cost
is a major challen... | [
"Lucas Grativol Ribeiro",
"Mathieu Leonardon",
"Guillaume Muller",
"Virginie Fresse",
"Matthieu Arzel"
] | 2023-10-23 08:36:21 | http://arxiv.org/abs/2310.14693v1 | http://arxiv.org/pdf/2310.14693v1 | 2310.14693v1 |
Population Descent: A Natural-Selection Based Hyper-Parameter Tuning Framework | First-order gradient descent has been the base of the most successful
optimization algorithms ever implemented. On supervised learning problems with
very high dimensionality, such as neural network optimization, it is almost
always the algorithm of choice, mainly due to its memory and computational
efficiency. However,... | [
"Abhinav Pomalapally",
"Bassel El Mabsout",
"Renato Mansuco"
] | 2023-10-23 08:11:17 | http://arxiv.org/abs/2310.14671v1 | http://arxiv.org/pdf/2310.14671v1 | 2310.14671v1 |
Dataset Bias Mitigation in Multiple-Choice Visual Question Answering and Beyond | Vision-language (VL) understanding tasks evaluate models' comprehension of
complex visual scenes through multiple-choice questions. However, we have
identified two dataset biases that models can exploit as shortcuts to resolve
various VL tasks correctly without proper understanding. The first type of
dataset bias is \e... | [
"Zhecan Wang",
"Long Chen",
"Haoxuan You",
"Keyang Xu",
"Yicheng He",
"Wenhao Li",
"Noal Codella",
"Kai-Wei Chang",
"Shih-Fu Chang"
] | 2023-10-23 08:09:42 | http://arxiv.org/abs/2310.14670v1 | http://arxiv.org/pdf/2310.14670v1 | 2310.14670v1 |
Data Pruning via Moving-one-Sample-out | In this paper, we propose a novel data-pruning approach called
moving-one-sample-out (MoSo), which aims to identify and remove the least
informative samples from the training set. The core insight behind MoSo is to
determine the importance of each sample by assessing its impact on the optimal
empirical risk. This is ac... | [
"Haoru Tan",
"Sitong Wu",
"Fei Du",
"Yukang Chen",
"Zhibin Wang",
"Fan Wang",
"Xiaojuan Qi"
] | 2023-10-23 08:00:03 | http://arxiv.org/abs/2310.14664v1 | http://arxiv.org/pdf/2310.14664v1 | 2310.14664v1 |
Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy | Posterior sampling, i.e., exponential mechanism to sample from the posterior
distribution, provides $\varepsilon$-pure differential privacy (DP) guarantees
and does not suffer from potentially unbounded privacy breach introduced by
$(\varepsilon,\delta)$-approximate DP. In practice, however, one needs to apply
approxim... | [
"Yingyu Lin",
"Yian Ma",
"Yu-Xiang Wang",
"Rachel Redberg"
] | 2023-10-23 07:54:39 | http://arxiv.org/abs/2310.14661v1 | http://arxiv.org/pdf/2310.14661v1 | 2310.14661v1 |
Predicting Accurate Lagrangian Multipliers for Mixed Integer Linear Programs | Lagrangian relaxation stands among the most efficient approaches for solving
a Mixed Integer Linear Programs (MILP) with difficult constraints. Given any
duals for these constraints, called Lagrangian Multipliers (LMs), it returns a
bound on the optimal value of the MILP, and Lagrangian methods seek the LMs
giving the ... | [
"Francesco Demelas",
"Joseph Le Roux",
"Mathieu Lacroix",
"Axel Parmentier"
] | 2023-10-23 07:53:47 | http://arxiv.org/abs/2310.14659v1 | http://arxiv.org/pdf/2310.14659v1 | 2310.14659v1 |
$Λ$-Split: A Privacy-Preserving Split Computing Framework for Cloud-Powered Generative AI | In the wake of the burgeoning expansion of generative artificial intelligence
(AI) services, the computational demands inherent to these technologies
frequently necessitate cloud-powered computational offloading, particularly for
resource-constrained mobile devices. These services commonly employ prompts to
steer the g... | [
"Shoki Ohta",
"Takayuki Nishio"
] | 2023-10-23 07:44:04 | http://arxiv.org/abs/2310.14651v1 | http://arxiv.org/pdf/2310.14651v1 | 2310.14651v1 |
Semantic-Aware Adversarial Training for Reliable Deep Hashing Retrieval | Deep hashing has been intensively studied and successfully applied in
large-scale image retrieval systems due to its efficiency and effectiveness.
Recent studies have recognized that the existence of adversarial examples poses
a security threat to deep hashing models, that is, adversarial vulnerability.
Notably, it is ... | [
"Xu Yuan",
"Zheng Zhang",
"Xunguang Wang",
"Lin Wu"
] | 2023-10-23 07:21:40 | http://arxiv.org/abs/2310.14637v1 | http://arxiv.org/pdf/2310.14637v1 | 2310.14637v1 |
Extending Input Contexts of Language Models through Training on Segmented Sequences | Effectively training language models on long inputs poses many technical
challenges. As a cost consideration, languages models are pretrained on a fixed
sequence length before being adapted to longer sequences. We explore various
methods for adapting models to longer inputs by training on segmented sequences
and an int... | [
"Petros Karypis",
"Julian McAuley",
"George Karypis"
] | 2023-10-23 07:13:31 | http://arxiv.org/abs/2310.14633v1 | http://arxiv.org/pdf/2310.14633v1 | 2310.14633v1 |
Making informed decisions in cutting tool maintenance in milling: A KNN based model agnostic approach | In machining processes, monitoring the condition of the tool is a crucial
aspect to ensure high productivity and quality of the product. Using different
machine learning techniques in Tool Condition Monitoring TCM enables a better
analysis of the large amount of data of different signals acquired during the
machining p... | [
"Aditya M. Rahalkar",
"Om M. Khare",
"Abhishek D. Patange"
] | 2023-10-23 07:02:30 | http://arxiv.org/abs/2310.14629v1 | http://arxiv.org/pdf/2310.14629v1 | 2310.14629v1 |
CrisisMatch: Semi-Supervised Few-Shot Learning for Fine-Grained Disaster Tweet Classification | The shared real-time information about natural disasters on social media
platforms like Twitter and Facebook plays a critical role in informing
volunteers, emergency managers, and response organizations. However, supervised
learning models for monitoring disaster events require large amounts of
annotated data, making t... | [
"Henry Peng Zou",
"Yue Zhou",
"Cornelia Caragea",
"Doina Caragea"
] | 2023-10-23 07:01:09 | http://arxiv.org/abs/2310.14627v1 | http://arxiv.org/pdf/2310.14627v1 | 2310.14627v1 |
CoF-CoT: Enhancing Large Language Models with Coarse-to-Fine Chain-of-Thought Prompting for Multi-domain NLU Tasks | While Chain-of-Thought prompting is popular in reasoning tasks, its
application to Large Language Models (LLMs) in Natural Language Understanding
(NLU) is under-explored. Motivated by multi-step reasoning of LLMs, we propose
Coarse-to-Fine Chain-of-Thought (CoF-CoT) approach that breaks down NLU tasks
into multiple rea... | [
"Hoang H. Nguyen",
"Ye Liu",
"Chenwei Zhang",
"Tao Zhang",
"Philip S. Yu"
] | 2023-10-23 06:54:51 | http://arxiv.org/abs/2310.14623v1 | http://arxiv.org/pdf/2310.14623v1 | 2310.14623v1 |
Rethinking SIGN Training: Provable Nonconvex Acceleration without First- and Second-Order Gradient Lipschitz | Sign-based stochastic methods have gained attention due to their ability to
achieve robust performance despite using only the sign information for
parameter updates. However, the current convergence analysis of sign-based
methods relies on the strong assumptions of first-order gradient Lipschitz and
second-order gradie... | [
"Tao Sun",
"Congliang Chen",
"Peng Qiao",
"Li Shen",
"Xinwang Liu",
"Dongsheng Li"
] | 2023-10-23 06:48:43 | http://arxiv.org/abs/2310.14616v1 | http://arxiv.org/pdf/2310.14616v1 | 2310.14616v1 |
CAD-DA: Controllable Anomaly Detection after Domain Adaptation by Statistical Inference | We propose a novel statistical method for testing the results of anomaly
detection (AD) under domain adaptation (DA), which we call CAD-DA --
controllable AD under DA. The distinct advantage of the CAD-DA lies in its
ability to control the probability of misidentifying anomalies under a
pre-specified level $\alpha$ (e.... | [
"Vo Nguyen Le Duy",
"Hsuan-Tien Lin",
"Ichiro Takeuchi"
] | 2023-10-23 06:34:33 | http://arxiv.org/abs/2310.14608v1 | http://arxiv.org/pdf/2310.14608v1 | 2310.14608v1 |
Investigating the Fairness of Large Language Models for Predictions on Tabular Data | Recent literature has suggested the potential of using large language models
(LLMs) to make predictions for tabular tasks. However, LLMs have been shown to
exhibit harmful social biases that reflect the stereotypes and inequalities
present in the society. To this end, as well as the widespread use of tabular
data in ma... | [
"Yanchen Liu",
"Srishti Gautam",
"Jiaqi Ma",
"Himabindu Lakkaraju"
] | 2023-10-23 06:31:28 | http://arxiv.org/abs/2310.14607v1 | http://arxiv.org/pdf/2310.14607v1 | 2310.14607v1 |
Online Auditing of Information Flow | Modern social media platforms play an important role in facilitating rapid
dissemination of information through their massive user networks. Fake news,
misinformation, and unverifiable facts on social media platforms propagate
disharmony and affect society. In this paper, we consider the problem of online
auditing of i... | [
"Mor Oren-Loberman",
"Vered Azar",
"Wasim Huleihel"
] | 2023-10-23 06:03:55 | http://arxiv.org/abs/2310.14595v1 | http://arxiv.org/pdf/2310.14595v1 | 2310.14595v1 |
Pre-Training LiDAR-Based 3D Object Detectors Through Colorization | Accurate 3D object detection and understanding for self-driving cars heavily
relies on LiDAR point clouds, necessitating large amounts of labeled data to
train. In this work, we introduce an innovative pre-training approach, Grounded
Point Colorization (GPC), to bridge the gap between data and labels by teaching
the mo... | [
"Tai-Yu Pan",
"Chenyang Ma",
"Tianle Chen",
"Cheng Perng Phoo",
"Katie Z Luo",
"Yurong You",
"Mark Campbell",
"Kilian Q. Weinberger",
"Bharath Hariharan",
"Wei-Lun Chao"
] | 2023-10-23 06:00:24 | http://arxiv.org/abs/2310.14592v1 | http://arxiv.org/pdf/2310.14592v1 | 2310.14592v1 |
GNNEvaluator: Evaluating GNN Performance On Unseen Graphs Without Labels | Evaluating the performance of graph neural networks (GNNs) is an essential
task for practical GNN model deployment and serving, as deployed GNNs face
significant performance uncertainty when inferring on unseen and unlabeled test
graphs, due to mismatched training-test graph distributions. In this paper, we
study a new... | [
"Xin Zheng",
"Miao Zhang",
"Chunyang Chen",
"Soheila Molaei",
"Chuan Zhou",
"Shirui Pan"
] | 2023-10-23 05:51:59 | http://arxiv.org/abs/2310.14586v1 | http://arxiv.org/pdf/2310.14586v1 | 2310.14586v1 |
JointMatch: A Unified Approach for Diverse and Collaborative Pseudo-Labeling to Semi-Supervised Text Classification | Semi-supervised text classification (SSTC) has gained increasing attention
due to its ability to leverage unlabeled data. However, existing approaches
based on pseudo-labeling suffer from the issues of pseudo-label bias and error
accumulation. In this paper, we propose JointMatch, a holistic approach for
SSTC that addr... | [
"Henry Peng Zou",
"Cornelia Caragea"
] | 2023-10-23 05:43:35 | http://arxiv.org/abs/2310.14583v1 | http://arxiv.org/pdf/2310.14583v1 | 2310.14583v1 |
FedSplitX: Federated Split Learning for Computationally-Constrained Heterogeneous Clients | Foundation models (FMs) have demonstrated remarkable performance in machine
learning but demand extensive training data and computational resources.
Federated learning (FL) addresses the challenges posed by FMs, especially
related to data privacy and computational burdens. However, FL on FMs faces
challenges in situati... | [
"Jiyun Shin",
"Jinhyun Ahn",
"Honggu Kang",
"Joonhyuk Kang"
] | 2023-10-23 05:34:31 | http://arxiv.org/abs/2310.14579v1 | http://arxiv.org/pdf/2310.14579v1 | 2310.14579v1 |
End of preview. Expand in Data Studio
Dataset Card for "arxiv_cs_papers"
This dataset contains the subset of ArXiv papers with the "cs.LG" tag to indicate the paper is about Machine Learning.
The core dataset is filtered from the full ArXiv dataset hosted on Kaggle: https://www.kaggle.com/datasets/Cornell-University/arxiv. The original dataset contains roughly 2 million papers. This dataset contains roughly 100,000 papers following the category filtering.
The dataset is maintained with requests to the ArXiv API.
The ArXiv dataset contains features:
- title
- abstract
- authors
- published
- url
- pdf_url
- arxiv_id
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