id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
|---|---|---|---|
2502.03654 | Gompertz Linear Units: Leveraging Asymmetry for Enhanced Learning
Dynamics | cs.LG cs.AI cs.CV | Activation functions are fundamental elements of deep learning architectures
as they significantly influence training dynamics. ReLU, while widely used, is
prone to the dying neuron problem, which has been mitigated by variants such as
LeakyReLU, PReLU, and ELU that better handle negative neuron outputs. Recently,
se... |
2502.03656 | A Study in Dataset Distillation for Image Super-Resolution | cs.CV cs.AI cs.LG | Dataset distillation is the concept of condensing large datasets into smaller
but highly representative synthetic samples. While previous research has
primarily focused on image classification, its application to image
Super-Resolution (SR) remains underexplored. This exploratory work studies
multiple dataset distill... |
2502.03658 | Advancing Weight and Channel Sparsification with Enhanced Saliency | cs.LG cs.CV | Pruning aims to accelerate and compress models by removing redundant
parameters, identified by specifically designed importance scores which are
usually imperfect. This removal is irreversible, often leading to subpar
performance in pruned models. Dynamic sparse training, while attempting to
adjust sparse structures ... |
2502.03660 | Energy & Force Regression on DFT Trajectories is Not Enough for
Universal Machine Learning Interatomic Potentials | cond-mat.mtrl-sci cs.AI cs.LG | Universal Machine Learning Interactomic Potentials (MLIPs) enable accelerated
simulations for materials discovery. However, current research efforts fail to
impactfully utilize MLIPs due to: 1. Overreliance on Density Functional Theory
(DFT) for MLIP training data creation; 2. MLIPs' inability to reliably and
accurat... |
2502.03662 | EC-SBM Synthetic Network Generator | cs.SI | Generating high-quality synthetic networks with realistic community structure
is vital to effectively evaluate community detection algorithms. In this study,
we propose a new synthetic network generator called the Edge-Connected
Stochastic Block Model (EC-SBM). The goal of EC-SBM is to take a given
clustered real-wor... |
2502.03664 | Contrastive Learning for Cold Start Recommendation with Adaptive Feature
Fusion | cs.IR cs.LG | This paper proposes a cold start recommendation model that integrates
contrastive learning, aiming to solve the problem of performance degradation of
recommendation systems in cold start scenarios due to the scarcity of user and
item interaction data. The model dynamically adjusts the weights of key
features through ... |
2502.03668 | Privacy-Preserving Generative Models: A Comprehensive Survey | cs.LG cs.CR | Despite the generative model's groundbreaking success, the need to study its
implications for privacy and utility becomes more urgent. Although many studies
have demonstrated the privacy threats brought by GANs, no existing survey has
systematically categorized the privacy and utility perspectives of GANs and
VAEs. I... |
2502.03669 | Unrealized Expectations: Comparing AI Methods vs Classical Algorithms
for Maximum Independent Set | cs.LG cs.AI cs.DM math.OC stat.ML | AI methods, such as generative models and reinforcement learning, have
recently been applied to combinatorial optimization (CO) problems, especially
NP-hard ones. This paper compares such GPU-based methods with classical
CPU-based methods on Maximum Independent Set (MIS). Experiments on standard
graph families show t... |
2502.03670 | Chaos into Order: Neural Framework for Expected Value Estimation of
Stochastic Partial Differential Equations | cs.LG | Stochastic Partial Differential Equations (SPDEs) are fundamental to modeling
complex systems in physics, finance, and engineering, yet their numerical
estimation remains a formidable challenge. Traditional methods rely on
discretization, introducing computational inefficiencies, and limiting
applicability in high-di... |
2502.03671 | Advancing Reasoning in Large Language Models: Promising Methods and
Approaches | cs.CL cs.AI | Large Language Models (LLMs) have succeeded remarkably in various natural
language processing (NLP) tasks, yet their reasoning capabilities remain a
fundamental challenge. While LLMs exhibit impressive fluency and factual
recall, their ability to perform complex reasoning-spanning logical deduction,
mathematical prob... |
2502.03672 | Physically consistent predictive reduced-order modeling by enhancing
Operator Inference with state constraints | physics.comp-ph cs.LG cs.NA math.NA | Numerical simulations of complex multiphysics systems, such as char
combustion considered herein, yield numerous state variables that inherently
exhibit physical constraints. This paper presents a new approach to augment
Operator Inference -- a methodology within scientific machine learning that
enables learning from... |
2502.03674 | An Empirical Study of Methods for Small Object Detection from Satellite
Imagery | cs.CV cs.AI | This paper reviews object detection methods for finding small objects from
remote sensing imagery and provides an empirical evaluation of four
state-of-the-art methods to gain insights into method performance and technical
challenges. In particular, we use car detection from urban satellite images and
bee box detecti... |
2502.03676 | Anytime Planning for End-Effector Trajectory Tracking | cs.RO | End-effector trajectory tracking algorithms find joint motions that drive
robot manipulators to track reference trajectories. In practical scenarios,
anytime algorithms are preferred for their ability to quickly generate initial
motions and continuously refine them over time. In this paper, we present an
algorithmic ... |
2502.03678 | Reflection-Window Decoding: Text Generation with Selective Refinement | cs.CL cs.AI cs.LG | The autoregressive decoding for text generation in large language models
(LLMs), while widely used, is inherently suboptimal due to the lack of a
built-in mechanism to perform refinement and/or correction of the generated
content. In this paper, we consider optimality in terms of the joint
probability over the genera... |
2502.03681 | On the effects of angular acceleration in orientation estimation using
inertial measurement units | eess.SY cs.SY | Determining the orientation of a rigid body using an inertial measurement
unit is a common problem in many engineering applications. However, sensor
fusion algorithms suffer from performance loss when other motions besides the
gravitational acceleration affect the accelerometer. In this paper, we show
that linear acc... |
2502.03685 | Controlled LLM Decoding via Discrete Auto-regressive Biasing | cs.CL cs.LG stat.ML | Controlled text generation allows for enforcing user-defined constraints on
large language model outputs, an increasingly important field as LLMs become
more prevalent in everyday life. One common approach uses energy-based
decoding, which defines a target distribution through an energy function that
combines multipl... |
2502.03686 | Variational Control for Guidance in Diffusion Models | cs.LG cs.AI cs.CV stat.ML | Diffusion models exhibit excellent sample quality, but existing guidance
methods often require additional model training or are limited to specific
tasks. We revisit guidance in diffusion models from the perspective of
variational inference and control, introducing Diffusion Trajectory Matching
(DTM) that enables gui... |
2502.03687 | Conditional Diffusion Models are Medical Image Classifiers that Provide
Explainability and Uncertainty for Free | cs.CV cs.LG | Discriminative classifiers have become a foundational tool in deep learning
for medical imaging, excelling at learning separable features of complex data
distributions. However, these models often need careful design, augmentation,
and training techniques to ensure safe and reliable deployment. Recently,
diffusion mo... |
2502.03688 | A Comparison of DeepSeek and Other LLMs | cs.CL cs.AI | Recently, DeepSeek has been the focus of attention in and beyond the AI
community. An interesting problem is how DeepSeek compares to other large
language models (LLMs). There are many tasks an LLM can do, and in this paper,
we use the task of predicting an outcome using a short text for comparison. We
consider two s... |
2502.03692 | DocMIA: Document-Level Membership Inference Attacks against DocVQA
Models | cs.LG cs.CL cs.CR | Document Visual Question Answering (DocVQA) has introduced a new paradigm for
end-to-end document understanding, and quickly became one of the standard
benchmarks for multimodal LLMs. Automating document processing workflows,
driven by DocVQA models, presents significant potential for many business
sectors. However, ... |
2502.03695 | Reduce Lap Time for Autonomous Racing with Curvature-Integrated MPCC
Local Trajectory Planning Method | cs.RO cs.SY eess.SY | The widespread application of autonomous driving technology has significantly
advanced the field of autonomous racing. Model Predictive Contouring Control
(MPCC) is a highly effective local trajectory planning method for autonomous
racing. However, the traditional MPCC method struggles with racetracks that
have signi... |
2502.03696 | Cascaded Learned Bloom Filter for Optimal Model-Filter Size Balance and
Fast Rejection | cs.DS cs.CC cs.LG | Recent studies have demonstrated that learned Bloom filters, which combine
machine learning with the classical Bloom filter, can achieve superior memory
efficiency. However, existing learned Bloom filters face two critical
unresolved challenges: the balance between the machine learning model size and
the Bloom filter... |
2502.03698 | How vulnerable is my policy? Adversarial attacks on modern behavior
cloning policies | cs.LG cs.CR cs.RO | Learning from Demonstration (LfD) algorithms have shown promising results in
robotic manipulation tasks, but their vulnerability to adversarial attacks
remains underexplored. This paper presents a comprehensive study of adversarial
attacks on both classic and recently proposed algorithms, including Behavior
Cloning (... |
2502.03699 | LLM Alignment as Retriever Optimization: An Information Retrieval
Perspective | cs.CL cs.AI cs.IR | Large Language Models (LLMs) have revolutionized artificial intelligence with
capabilities in reasoning, coding, and communication, driving innovation across
industries. Their true potential depends on effective alignment to ensure
correct, trustworthy and ethical behavior, addressing challenges like
misinformation, ... |
2502.03701 | First-ish Order Methods: Hessian-aware Scalings of Gradient Descent | math.OC cs.LG | Gradient descent is the primary workhorse for optimizing large-scale problems
in machine learning. However, its performance is highly sensitive to the choice
of the learning rate. A key limitation of gradient descent is its lack of
natural scaling, which often necessitates expensive line searches or heuristic
tuning ... |
2502.03703 | On the Expressive Power of Subgraph Graph Neural Networks for Graphs
with Bounded Cycles | cs.LG | Graph neural networks (GNNs) have been widely used in graph-related contexts.
It is known that the separation power of GNNs is equivalent to that of the
Weisfeiler-Lehman (WL) test; hence, GNNs are imperfect at identifying all
non-isomorphic graphs, which severely limits their expressive power. This work
investigates... |
2502.03708 | Aggregate and conquer: detecting and steering LLM concepts by combining
nonlinear predictors over multiple layers | cs.CL cs.AI stat.ML | A trained Large Language Model (LLM) contains much of human knowledge. Yet,
it is difficult to gauge the extent or accuracy of that knowledge, as LLMs do
not always ``know what they know'' and may even be actively misleading. In this
work, we give a general method for detecting semantic concepts in the internal
activ... |
2502.03711 | MultiQ&A: An Analysis in Measuring Robustness via Automated
Crowdsourcing of Question Perturbations and Answers | cs.CL cs.AI cs.LG | One critical challenge in the institutional adoption journey of Large
Language Models (LLMs) stems from their propensity to hallucinate in generated
responses. To address this, we propose MultiQ&A, a systematic approach for
evaluating the robustness and consistency of LLM-generated answers. We
demonstrate MultiQ&A's ... |
2502.03714 | Universal Sparse Autoencoders: Interpretable Cross-Model Concept
Alignment | cs.CV cs.LG | We present Universal Sparse Autoencoders (USAEs), a framework for uncovering
and aligning interpretable concepts spanning multiple pretrained deep neural
networks. Unlike existing concept-based interpretability methods, which focus
on a single model, USAEs jointly learn a universal concept space that can
reconstruct ... |
2502.03715 | Boosting Knowledge Graph-based Recommendations through Confidence-Aware
Augmentation with Large Language Models | cs.IR cs.AI | Knowledge Graph-based recommendations have gained significant attention due
to their ability to leverage rich semantic relationships. However, constructing
and maintaining Knowledge Graphs (KGs) is resource-intensive, and the accuracy
of KGs can suffer from noisy, outdated, or irrelevant triplets. Recent
advancements... |
2502.03717 | Efficiently Generating Expressive Quadruped Behaviors via
Language-Guided Preference Learning | cs.RO cs.AI | Expressive robotic behavior is essential for the widespread acceptance of
robots in social environments. Recent advancements in learned legged locomotion
controllers have enabled more dynamic and versatile robot behaviors. However,
determining the optimal behavior for interactions with different users across
varied s... |
2502.03721 | Detecting Backdoor Attacks via Similarity in Semantic Communication
Systems | cs.CR cs.LG | Semantic communication systems, which leverage Generative AI (GAI) to
transmit semantic meaning rather than raw data, are poised to revolutionize
modern communications. However, they are vulnerable to backdoor attacks, a type
of poisoning manipulation that embeds malicious triggers into training
datasets. As a result... |
2502.03723 | Speaking the Language of Teamwork: LLM-Guided Credit Assignment in
Multi-Agent Reinforcement Learning | cs.MA | Credit assignment, the process of attributing credit or blame to individual
agents for their contributions to a team's success or failure, remains a
fundamental challenge in multi-agent reinforcement learning (MARL),
particularly in environments with sparse rewards. Commonly-used approaches such
as value decompositio... |
2502.03724 | MD-BERT: Action Recognition in Dark Videos via Dynamic Multi-Stream
Fusion and Temporal Modeling | cs.CV cs.AI cs.HC cs.LG cs.MM | Action recognition in dark, low-light (under-exposed) or noisy videos is a
challenging task due to visibility degradation, which can hinder critical
spatiotemporal details. This paper proposes MD-BERT, a novel multi-stream
approach that integrates complementary pre-processing techniques such as gamma
correction and h... |
2502.03725 | Optimal Control of Fluid Restless Multi-armed Bandits: A Machine
Learning Approach | cs.LG | We propose a machine learning approach to the optimal control of fluid
restless multi-armed bandits (FRMABs) with state equations that are either
affine or quadratic in the state variables. By deriving fundamental properties
of FRMAB problems, we design an efficient machine learning based algorithm.
Using this algori... |
2502.03726 | DICE: Distilling Classifier-Free Guidance into Text Embeddings | cs.CV | Text-to-image diffusion models are capable of generating high-quality images,
but these images often fail to align closely with the given text prompts.
Classifier-free guidance (CFG) is a popular and effective technique for
improving text-image alignment in the generative process. However, using CFG
introduces signif... |
2502.03729 | Action-Free Reasoning for Policy Generalization | cs.RO cs.AI | End-to-end imitation learning offers a promising approach for training robot
policies. However, generalizing to new settings remains a significant
challenge. Although large-scale robot demonstration datasets have shown
potential for inducing generalization, they are resource-intensive to scale. In
contrast, human vid... |
2502.03737 | Mitigating the Participation Bias by Balancing Extreme Ratings | cs.LG cs.GT | Rating aggregation plays a crucial role in various fields, such as product
recommendations, hotel rankings, and teaching evaluations. However, traditional
averaging methods can be affected by participation bias, where some raters do
not participate in the rating process, leading to potential distortions. In
this pape... |
2502.03738 | Scaling Laws in Patchification: An Image Is Worth 50,176 Tokens And More | cs.CV | Since the introduction of Vision Transformer (ViT), patchification has long
been regarded as a de facto image tokenization approach for plain visual
architectures. By compressing the spatial size of images, this approach can
effectively shorten the token sequence and reduce the computational cost of
ViT-like plain ar... |
2502.03740 | Multiple Invertible and Partial-Equivariant Function for Latent Vector
Transformation to Enhance Disentanglement in VAEs | cs.LG cs.AI | Disentanglement learning is a core issue for understanding and re-using
trained information in Variational AutoEncoder (VAE), and effective inductive
bias has been reported as a key factor. However, the actual implementation of
such bias is still vague. In this paper, we propose a novel method, called
Multiple Invert... |
2502.03746 | Brain Tumor Identification using Improved YOLOv8 | cs.CV cs.LG | Identifying the extent of brain tumors is a significant challenge in brain
cancer treatment. The main difficulty is in the approximate detection of tumor
size. Magnetic resonance imaging (MRI) has become a critical diagnostic tool.
However, manually detecting the boundaries of brain tumors from MRI scans is a
labor-i... |
2502.03748 | Rethinking the Residual Distribution of Locate-then-Editing Methods in
Model Editing | cs.CL | Model editing is a powerful technique for updating the knowledge of Large
Language Models (LLMs). Locate-then-edit methods are a popular class of
approaches that first identify the critical layers storing knowledge, then
compute the residual of the last critical layer based on the edited knowledge,
and finally perfor... |
2502.03749 | PINS: Proximal Iterations with Sparse Newton and Sinkhorn for Optimal
Transport | cs.LG math.OC | Optimal transport (OT) is a critical problem in optimization and machine
learning, where accuracy and efficiency are paramount. Although entropic
regularization and the Sinkhorn algorithm improve scalability, they frequently
encounter numerical instability and slow convergence, especially when the
regularization para... |
2502.03750 | Principal Curvatures Estimation with Applications to Single Cell Data | cs.LG cs.AI | The rapidly growing field of single-cell transcriptomic sequencing (scRNAseq)
presents challenges for data analysis due to its massive datasets. A common
method in manifold learning consists in hypothesizing that datasets lie on a
lower dimensional manifold. This allows to study the geometry of point clouds
by extrac... |
2502.03752 | PRISM: A Robust Framework for Skill-based Meta-Reinforcement Learning
with Noisy Demonstrations | cs.LG cs.AI | Meta-reinforcement learning (Meta-RL) facilitates rapid adaptation to unseen
tasks but faces challenges in long-horizon environments. Skill-based approaches
tackle this by decomposing state-action sequences into reusable skills and
employing hierarchical decision-making. However, these methods are highly
susceptible ... |
2502.03755 | Regularization via f-Divergence: An Application to Multi-Oxide
Spectroscopic Analysis | cs.LG | In this paper, we address the task of characterizing the chemical composition
of planetary surfaces using convolutional neural networks (CNNs). Specifically,
we seek to predict the multi-oxide weights of rock samples based on
spectroscopic data collected under Martian conditions. We frame this problem as
a multi-targ... |
2502.03758 | Improving Adversarial Robustness via Phase and Amplitude-aware Prompting | cs.CV | Deep neural networks are found to be vulnerable to adversarial noises. The
prompt-based defense has been increasingly studied due to its high efficiency.
However, existing prompt-based defenses mainly exploited mixed prompt patterns,
where critical patterns closely related to object semantics lack sufficient
focus. T... |
2502.03760 | RAMOTS: A Real-Time System for Aerial Multi-Object Tracking based on
Deep Learning and Big Data Technology | cs.CV | Multi-object tracking (MOT) in UAV-based video is challenging due to
variations in viewpoint, low resolution, and the presence of small objects.
While other research on MOT dedicated to aerial videos primarily focuses on the
academic aspect by developing sophisticated algorithms, there is a lack of
attention to the p... |
2502.03762 | Learning Reward Machines from Partially Observed Optimal Policies | cs.LG cs.FL | Inverse reinforcement learning is the problem of inferring a reward function
from an optimal policy. In this work, it is assumed that the reward is
expressed as a reward machine whose transitions depend on atomic propositions
associated with the state of a Markov Decision Process (MDP). Our goal is to
identify the tr... |
2502.03765 | Replacing K-infinity Function with Leaky ReLU in Barrier Function
Design: A Union of Invariant Sets Approach for ReLU-Based Dynamical Systems | eess.SY cs.SY | In this paper, a systematic framework is presented for determining piecewise
affine PWA barrier functions and their corresponding invariant sets for
dynamical systems identified via Rectified Linear Unit (ReLU) neural networks
or their equivalent PWA representations. A common approach to determining the
invariant set... |
2502.03766 | Hierarchical Contextual Manifold Alignment for Structuring Latent
Representations in Large Language Models | cs.CL | The organization of latent token representations plays a crucial role in
determining the stability, generalization, and contextual consistency of
language models, yet conventional approaches to embedding refinement often rely
on parameter modifications that introduce additional computational overhead. A
hierarchical ... |
2502.03771 | Adaptive Semantic Prompt Caching with VectorQ | cs.LG cs.CL | Semantic prompt caches reduce the latency and cost of large language model
(LLM) inference by reusing cached LLM-generated responses for semantically
similar prompts. Vector similarity metrics assign a numerical score to quantify
the similarity between an embedded prompt and its nearest neighbor in the
cache. Existin... |
2502.03772 | A Retrospective Systematic Study on Hierarchical Sparse Query
Transformer-assisted Ultrasound Screening for Early Hepatocellular Carcinoma | cs.CV cs.AI | Hepatocellular carcinoma (HCC) ranks as the third leading cause of
cancer-related mortality worldwide, with early detection being crucial for
improving patient survival rates. However, early screening for HCC using
ultrasound suffers from insufficient sensitivity and is highly dependent on the
expertise of radiologis... |
2502.03773 | ExpProof : Operationalizing Explanations for Confidential Models with
ZKPs | cs.LG cs.AI cs.CR | In principle, explanations are intended as a way to increase trust in machine
learning models and are often obligated by regulations. However, many
circumstances where these are demanded are adversarial in nature, meaning the
involved parties have misaligned interests and are incentivized to manipulate
explanations f... |
2502.03774 | High-Rate Spatially Coupled LDPC Codes Based on Massey's Convolutional
Self-Orthogonal Codes | cs.IT math.IT | In this paper, we study a new class of high-rate spatially coupled LDPC
(SC-LDPC) codes based on the convolutional self-orthogonal codes (CSOCs) first
introduced by Massey. The SC-LDPC codes are constructed by treating the
irregular graph corresponding to the parity-check matrix of a systematic rate R
= (n - 1)/n CSO... |
2502.03776 | StarMAP: Global Neighbor Embedding for Faithful Data Visualization | cs.LG | Neighbor embedding is widely employed to visualize high-dimensional data;
however, it frequently overlooks the global structure, e.g., intercluster
similarities, thereby impeding accurate visualization. To address this problem,
this paper presents Star-attracted Manifold Approximation and Projection
(StarMAP), which ... |
2502.03777 | Multi-Label Test-Time Adaptation with Bound Entropy Minimization | cs.CV | Mainstream test-time adaptation (TTA) techniques endeavor to mitigate
distribution shifts via entropy minimization for multi-class classification,
inherently increasing the probability of the most confident class. However,
when encountering multi-label instances, the primary challenge stems from the
varying number of... |
2502.03781 | Gaze-Assisted Human-Centric Domain Adaptation for Cardiac Ultrasound
Image Segmentation | cs.CV eess.IV | Domain adaptation (DA) for cardiac ultrasound image segmentation is
clinically significant and valuable. However, previous domain adaptation
methods are prone to be affected by the incomplete pseudo-label and low-quality
target to source images. Human-centric domain adaptation has great advantages
of human cognitive ... |
2502.03783 | UltraBones100k: An Ultrasound Image Dataset with CT-Derived Labels for
Lower Extremity Long Bone Surface Segmentation | eess.IV cs.CV | Ultrasound-based bone surface segmentation is crucial in computer-assisted
orthopedic surgery. However, ultrasound images have limitations, including a
low signal-to-noise ratio, and acoustic shadowing, which make interpretation
difficult. Existing deep learning models for bone segmentation rely primarily
on costly m... |
2502.03785 | Reed-Muller Codes on CQ Channels via a New Correlation Bound for Quantum
Observables | cs.IT math.IT quant-ph | The question of whether Reed-Muller (RM) codes achieve capacity on binary
memoryless symmetric (BMS) channels has drawn attention since it was resolved
positively for the binary erasure channel by Kudekar et al. in 2016. In 2021,
Reeves and Pfister extended this to prove the bit-error probability vanishes on
BMS chan... |
2502.03787 | Iterate to Accelerate: A Unified Framework for Iterative Reasoning and
Feedback Convergence | cs.LG | We introduce a unified framework for iterative reasoning that leverages
non-Euclidean geometry via Bregman divergences, higher-order operator
averaging, and adaptive feedback mechanisms. Our analysis establishes that,
under mild smoothness and contractivity assumptions, a generalized update
scheme not only unifies cl... |
2502.03792 | Guiding Two-Layer Neural Network Lipschitzness via Gradient Descent
Learning Rate Constraints | stat.ML cs.LG | We demonstrate that applying an eventual decay to the learning rate (LR) in
empirical risk minimization (ERM), where the mean-squared-error loss is
minimized using standard gradient descent (GD) for training a two-layer neural
network with Lipschitz activation functions, ensures that the resulting network
exhibits a ... |
2502.03793 | It's All in The [MASK]: Simple Instruction-Tuning Enables BERT-like
Masked Language Models As Generative Classifiers | cs.CL cs.AI | While encoder-only models such as BERT and ModernBERT are ubiquitous in
real-world NLP applications, their conventional reliance on task-specific
classification heads can limit their applicability compared to decoder-based
large language models (LLMs). In this work, we introduce
ModernBERT-Large-Instruct, a 0.4B-para... |
2502.03795 | Distribution learning via neural differential equations: minimal energy
regularization and approximation theory | cs.LG math.CA stat.ME stat.ML | Neural ordinary differential equations (ODEs) provide expressive
representations of invertible transport maps that can be used to approximate
complex probability distributions, e.g., for generative modeling, density
estimation, and Bayesian inference. We show that for a large class of transport
maps $T$, there exists... |
2502.03798 | Network-Wide Traffic Flow Estimation Across Multiple Cities with Global
Open Multi-Source Data: A Large-Scale Case Study in Europe and North America | cs.LG | Network-wide traffic flow, which captures dynamic traffic volume on each link
of a general network, is fundamental to smart mobility applications. However,
the observed traffic flow from sensors is usually limited across the entire
network due to the associated high installation and maintenance costs. To
address this... |
2502.03799 | Enhancing Hallucination Detection through Noise Injection | cs.CL cs.SY eess.SY | Large Language Models (LLMs) are prone to generating plausible yet incorrect
responses, known as hallucinations. Effectively detecting hallucinations is
therefore crucial for the safe deployment of LLMs. Recent research has linked
hallucinations to model uncertainty, suggesting that hallucinations can be
detected by ... |
2502.03801 | SoK: Benchmarking Poisoning Attacks and Defenses in Federated Learning | cs.CR cs.AI cs.LG | Federated learning (FL) enables collaborative model training while preserving
data privacy, but its decentralized nature exposes it to client-side data
poisoning attacks (DPAs) and model poisoning attacks (MPAs) that degrade global
model performance. While numerous proposed defenses claim substantial
effectiveness, t... |
2502.03802 | MXMap: A Multivariate Cross Mapping Framework for Causal Discovery in
Dynamical Systems | cs.LG math.DS stat.ME | Convergent Cross Mapping (CCM) is a powerful method for detecting causality
in coupled nonlinear dynamical systems, providing a model-free approach to
capture dynamic causal interactions. Partial Cross Mapping (PCM) was introduced
as an extension of CCM to address indirect causality in three-variable systems
by compa... |
2502.03803 | Graph Neural Network-Driven Hierarchical Mining for Complex Imbalanced
Data | cs.LG | This study presents a hierarchical mining framework for high-dimensional
imbalanced data, leveraging a depth graph model to address the inherent
performance limitations of conventional approaches in handling complex,
high-dimensional data distributions with imbalanced sample representations. By
constructing a structu... |
2502.03804 | Understanding and Supporting Formal Email Exchange by Answering
AI-Generated Questions | cs.HC cs.AI | Replying to formal emails is time-consuming and cognitively demanding, as it
requires crafting polite phrasing and providing an adequate response to the
sender's demands. Although systems with Large Language Models (LLMs) were
designed to simplify the email replying process, users still need to provide
detailed promp... |
2502.03805 | Identify Critical KV Cache in LLM Inference from an Output Perturbation
Perspective | cs.CL | Large language models have revolutionized natural language processing but
face significant challenges of high storage and runtime costs, due to the
transformer architecture's reliance on self-attention, particularly the large
Key-Value (KV) cache for long-sequence inference. Recent efforts to reduce KV
cache size by ... |
2502.03806 | Should Code Models Learn Pedagogically? A Preliminary Evaluation of
Curriculum Learning for Real-World Software Engineering Tasks | cs.SE cs.LG | Learning-based techniques, especially advanced pre-trained models for code
have demonstrated capabilities in code understanding and generation, solving
diverse software engineering (SE) tasks. Despite the promising results, current
training approaches may not fully optimize model performance, as they typically
involv... |
2502.03810 | DeblurDiff: Real-World Image Deblurring with Generative Diffusion Models | cs.CV | Diffusion models have achieved significant progress in image generation. The
pre-trained Stable Diffusion (SD) models are helpful for image deblurring by
providing clear image priors. However, directly using a blurry image or
pre-deblurred one as a conditional control for SD will either hinder accurate
structure extr... |
2502.03813 | Optimized Unet with Attention Mechanism for Multi-Scale Semantic
Segmentation | cs.CV | Semantic segmentation is one of the core tasks in the field of computer
vision, and its goal is to accurately classify each pixel in an image. The
traditional Unet model achieves efficient feature extraction and fusion through
an encoder-decoder structure, but it still has certain limitations when dealing
with comple... |
2502.03814 | Large Language Models for Multi-Robot Systems: A Survey | cs.RO cs.AI | The rapid advancement of Large Language Models (LLMs) has opened new
possibilities in Multi-Robot Systems (MRS), enabling enhanced communication,
task planning, and human-robot interaction. Unlike traditional single-robot and
multi-agent systems, MRS poses unique challenges, including coordination,
scalability, and r... |
2502.03817 | Knowing When to Stop Matters: A Unified Algorithm for Online Conversion
under Horizon Uncertainty | cs.DS cs.LG | This paper investigates the online conversion problem, which involves
sequentially trading a divisible resource (e.g., energy) under dynamically
changing prices to maximize profit. A key challenge in online conversion is
managing decisions under horizon uncertainty, where the duration of trading is
either known, reve... |
2502.03821 | PsyPlay: Personality-Infused Role-Playing Conversational Agents | cs.CL | The current research on Role-Playing Conversational Agents (RPCAs) with Large
Language Models (LLMs) primarily focuses on imitating specific speaking styles
and utilizing character backgrounds, neglecting the depiction of deeper
personality traits.~In this study, we introduce personality-infused
role-playing for LLM ... |
2502.03822 | Dynamic Rank Adjustment in Diffusion Policies for Efficient and Flexible
Training | cs.RO | Diffusion policies trained via offline behavioral cloning have recently
gained traction in robotic motion generation. While effective, these policies
typically require a large number of trainable parameters. This model size
affords powerful representations but also incurs high computational cost during
training. Idea... |
2502.03824 | Syntriever: How to Train Your Retriever with Synthetic Data from LLMs | cs.CL cs.AI | LLMs have boosted progress in many AI applications. Recently, there were
attempts to distill the vast knowledge of LLMs into information retrieval
systems. Those distillation methods mostly use output probabilities of LLMs
which are unavailable in the latest black-box LLMs. We propose Syntriever, a
training framework... |
2502.03825 | Synthetic Poisoning Attacks: The Impact of Poisoned MRI Image on U-Net
Brain Tumor Segmentation | eess.IV cs.CR cs.CV | Deep learning-based medical image segmentation models, such as U-Net, rely on
high-quality annotated datasets to achieve accurate predictions. However, the
increasing use of generative models for synthetic data augmentation introduces
potential risks, particularly in the absence of rigorous quality control. In
this p... |
2502.03826 | FairT2I: Mitigating Social Bias in Text-to-Image Generation via Large
Language Model-Assisted Detection and Attribute Rebalancing | cs.CV | The proliferation of Text-to-Image (T2I) models has revolutionized content
creation, providing powerful tools for diverse applications ranging from
artistic expression to educational material development and marketing. Despite
these technological advancements, significant ethical concerns arise from these
models' rel... |
2502.03827 | A comprehensive survey of contemporary Arabic sentiment analysis:
Methods, Challenges, and Future Directions | cs.CL cs.AI | Sentiment Analysis, a popular subtask of Natural Language Processing, employs
computational methods to extract sentiment, opinions, and other subjective
aspects from linguistic data. Given its crucial role in understanding human
sentiment, research in sentiment analysis has witnessed significant growth in
the recent ... |
2502.03829 | FE-UNet: Frequency Domain Enhanced U-Net with Segment Anything
Capability for Versatile Image Segmentation | cs.CV | Image segmentation is a critical task in visual understanding. Convolutional
Neural Networks (CNNs) are predisposed to capture high-frequency features in
images, while Transformers exhibit a contrasting focus on low-frequency
features. In this paper, we experimentally quantify the contrast sensitivity
function of CNN... |
2502.03835 | Single-Domain Generalized Object Detection by Balancing Domain Diversity
and Invariance | cs.CV | Single-domain generalization for object detection (S-DGOD) aims to transfer
knowledge from a single source domain to unseen target domains. In recent
years, many models have focused primarily on achieving feature invariance to
enhance robustness. However, due to the inherent diversity across domains, an
excessive emp... |
2502.03836 | Adapting Human Mesh Recovery with Vision-Language Feedback | cs.CV | Human mesh recovery can be approached using either regression-based or
optimization-based methods. Regression models achieve high pose accuracy but
struggle with model-to-image alignment due to the lack of explicit 2D-3D
correspondences. In contrast, optimization-based methods align 3D models to 2D
observations but a... |
2502.03839 | On the Number of Control Nodes in Boolean Networks with Degree
Constraints | eess.SY cs.SY | This paper studies the minimum control node set problem for Boolean networks
(BNs) with degree constraints. The main contribution is to derive the
nontrivial lower and upper bounds on the size of the minimum control node set
through combinatorial analysis of four types of BNs (i.e., $k$-$k$-XOR-BNs,
simple $k$-$k$-AN... |
2502.03843 | Improving Natural Language Understanding for LLMs via Large-Scale
Instruction Synthesis | cs.CL cs.AI | High-quality, large-scale instructions are crucial for aligning large
language models (LLMs), however, there is a severe shortage of instruction in
the field of natural language understanding (NLU). Previous works on
constructing NLU instructions mainly focus on information extraction (IE),
neglecting tasks such as m... |
2502.03845 | PAGNet: Pluggable Adaptive Generative Networks for Information
Completion in Multi-Agent Communication | cs.MA | For partially observable cooperative tasks, multi-agent systems must develop
effective communication and understand the interplay among agents in order to
achieve cooperative goals. However, existing multi-agent reinforcement learning
(MARL) with communication methods lack evaluation metrics for information
weights a... |
2502.03850 | Electromagnetic Channel Modeling and Capacity Analysis for HMIMO
Communications | cs.IT eess.SP math.IT | Advancements in emerging technologies, e.g., reconfigurable intelligent
surfaces and holographic MIMO (HMIMO), facilitate unprecedented manipulation of
electromagnetic (EM) waves, significantly enhancing the performance of wireless
communication systems. To accurately characterize the achievable performance
limits of... |
2502.03852 | Pursuing Better Decision Boundaries for Long-Tailed Object Detection via
Category Information Amount | cs.CV cs.AI | In object detection, the instance count is typically used to define whether a
dataset exhibits a long-tail distribution, implicitly assuming that models will
underperform on categories with fewer instances. This assumption has led to
extensive research on category bias in datasets with imbalanced instance
counts. How... |
2502.03854 | Mirror Descent Actor Critic via Bounded Advantage Learning | cs.LG | Regularization is a core component of recent Reinforcement Learning (RL)
algorithms. Mirror Descent Value Iteration (MDVI) uses both Kullback-Leibler
divergence and entropy as regularizers in its value and policy updates. Despite
its empirical success in discrete action domains and strong theoretical
guarantees, the ... |
2502.03855 | Semi-rPPG: Semi-Supervised Remote Physiological Measurement with
Curriculum Pseudo-Labeling | cs.CV | Remote Photoplethysmography (rPPG) is a promising technique to monitor
physiological signals such as heart rate from facial videos. However, the
labeled facial videos in this research are challenging to collect. Current rPPG
research is mainly based on several small public datasets collected in simple
environments, w... |
2502.03856 | Taking A Closer Look at Interacting Objects: Interaction-Aware Open
Vocabulary Scene Graph Generation | cs.CV | Today's open vocabulary scene graph generation (OVSGG) extends traditional
SGG by recognizing novel objects and relationships beyond predefined
categories, leveraging the knowledge from pre-trained large-scale models. Most
existing methods adopt a two-stage pipeline: weakly supervised pre-training
with image captions... |
2502.03859 | Stabilizing scheduling logic for networked control systems under limited
capacity and lossy communication networks | eess.SY cs.SY | In this paper we address the problem of designing scheduling logic for
stabilizing Networked Control Systems (NCSs) with plants and controllers
remotely-located over a limited capacity communication network subject to data
losses. Our specific contributions include characterization of stability under
worst case data ... |
2502.03860 | BOLT: Bootstrap Long Chain-of-Thought in Language Models without
Distillation | cs.CL | Large language models (LLMs), such as o1 from OpenAI, have demonstrated
remarkable reasoning capabilities. o1 generates a long chain-of-thought
(LongCoT) before answering a question. LongCoT allows LLMs to analyze problems,
devise plans, reflect, and backtrack effectively. These actions empower LLM to
solve complex p... |
2502.03866 | Weyl symmetry of the gradient-flow in information geometry | gr-qc cs.IT math-ph math.IT math.MP | We have revisited the gradient-flow in information geometry from the
perspective of Weyl symmetry. The gradient-flow equations are derived from the
proposed action which is invariant under the Weyl's gauge transformations. In
Weyl integrable geometry, we have related Amari's $\alpha$-connections in IG to
the Weyl inv... |
2502.03876 | Position: Untrained Machine Learning for Anomaly Detection | cs.LG | Anomaly detection based on 3D point cloud data is an important research
problem and receives more and more attention recently. Untrained anomaly
detection based on only one sample is an emerging research problem motivated by
real manufacturing industries such as personalized manufacturing that only one
sample can be ... |
2502.03877 | Advanced Object Detection and Pose Estimation with Hybrid Task Cascade
and High-Resolution Networks | cs.CV | In the field of computer vision, 6D object detection and pose estimation are
critical for applications such as robotics, augmented reality, and autonomous
driving. Traditional methods often struggle with achieving high accuracy in
both object detection and precise pose estimation simultaneously. This study
proposes a... |
2502.03884 | Rank Also Matters: Hierarchical Configuration for Mixture of Adapter
Experts in LLM Fine-Tuning | cs.LG cs.AI | Large language models (LLMs) have demonstrated remarkable success across
various tasks, accompanied by a continuous increase in their parameter size.
Parameter-efficient fine-tuning (PEFT) methods, such as Low-Rank Adaptation
(LoRA), address the challenges of fine-tuning LLMs by significantly reducing
the number of t... |
2502.03885 | InfinitePOD: Building Datacenter-Scale High-Bandwidth Domain for LLM
with Optical Circuit Switching Transceivers | cs.NI cs.DC cs.LG | Scaling Large Language Model (LLM) training relies on multi-dimensional
parallelism, where High-Bandwidth Domains (HBDs) are critical for
communication-intensive parallelism like Tensor Parallelism (TP) and Expert
Parallelism (EP). However, existing HBD architectures face fundamental
limitations in scalability, cost,... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.