id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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2501.19082 | A Bias-Correction Decentralized Stochastic Gradient Algorithm with
Momentum Acceleration | cs.LG cs.DC math.OC stat.ML | Distributed stochastic optimization algorithms can simultaneously process
large-scale datasets, significantly accelerating model training. However, their
effectiveness is often hindered by the sparsity of distributed networks and
data heterogeneity. In this paper, we propose a momentum-accelerated
distributed stochas... |
2501.19083 | MotionPCM: Real-Time Motion Synthesis with Phased Consistency Model | cs.CV | Diffusion models have become a popular choice for human motion synthesis due
to their powerful generative capabilities. However, their high computational
complexity and large sampling steps pose challenges for real-time applications.
Fortunately, the Consistency Model (CM) provides a solution to greatly reduce
the nu... |
2501.19084 | Laser: Efficient Language-Guided Segmentation in Neural Radiance Fields | cs.CV | In this work, we propose a method that leverages CLIP feature distillation,
achieving efficient 3D segmentation through language guidance. Unlike previous
methods that rely on multi-scale CLIP features and are limited by processing
speed and storage requirements, our approach aims to streamline the workflow by
direct... |
2501.19086 | Fairness Analysis of CLIP-Based Foundation Models for X-Ray Image
Classification | cs.CV cs.AI | X-ray imaging is pivotal in medical diagnostics, offering non-invasive
insights into a range of health conditions. Recently, vision-language models,
such as the Contrastive Language-Image Pretraining (CLIP) model, have
demonstrated potential in improving diagnostic accuracy by leveraging
large-scale image-text datase... |
2501.19088 | JGHand: Joint-Driven Animatable Hand Avater via 3D Gaussian Splatting | cs.CV | Since hands are the primary interface in daily interactions, modeling
high-quality digital human hands and rendering realistic images is a critical
research problem. Furthermore, considering the requirements of interactive and
rendering applications, it is essential to achieve real-time rendering and
driveability of ... |
2501.19089 | Understanding Oversmoothing in GNNs as Consensus in Opinion Dynamics | cs.LG | In contrast to classes of neural networks where the learned representations
become increasingly expressive with network depth, the learned representations
in graph neural networks (GNNs), tend to become increasingly similar. This
phenomena, known as oversmoothing, is characterized by learned representations
that cann... |
2501.19090 | Pivoting Factorization: A Compact Meta Low-Rank Representation of
Sparsity for Efficient Inference in Large Language Models | cs.LG | The rapid growth of Large Language Models has driven demand for effective
model compression techniques to reduce memory and computation costs. Low-rank
pruning has gained attention for its tensor coherence and GPU compatibility
across all densities. However, low-rank pruning has struggled to match the
performance of ... |
2501.19091 | FL-APU: A Software Architecture to Ease Practical Implementation of
Cross-Silo Federated Learning | cs.DC cs.LG | Federated Learning (FL) is an upcoming technology that is increasingly
applied in real-world applications. Early applications focused on cross-device
scenarios, where many participants with limited resources train machine
learning (ML) models together, e.g., in the case of Google's GBoard.
Contrarily, cross-silo scen... |
2501.19093 | Improving Low-Resource Sequence Labeling with Knowledge Fusion and
Contextual Label Explanations | cs.CL | Sequence labeling remains a significant challenge in low-resource,
domain-specific scenarios, particularly for character-dense languages like
Chinese. Existing methods primarily focus on enhancing model comprehension and
improving data diversity to boost performance. However, these approaches still
struggle with inad... |
2501.19094 | Ambient Denoising Diffusion Generative Adversarial Networks for
Establishing Stochastic Object Models from Noisy Image Data | cs.CV eess.IV | It is widely accepted that medical imaging systems should be objectively
assessed via task-based image quality (IQ) measures that ideally account for
all sources of randomness in the measured image data, including the variation
in the ensemble of objects to be imaged. Stochastic object models (SOMs) that
can randomly... |
2501.19095 | PathE: Leveraging Entity-Agnostic Paths for Parameter-Efficient
Knowledge Graph Embeddings | cs.AI cs.LG | Knowledge Graphs (KGs) store human knowledge in the form of entities (nodes)
and relations, and are used extensively in various applications. KG embeddings
are an effective approach to addressing tasks like knowledge discovery, link
prediction, and reasoning. This is often done by allocating and learning
embedding ta... |
2501.19098 | $\infty$-Video: A Training-Free Approach to Long Video Understanding via
Continuous-Time Memory Consolidation | cs.CV cs.LG | Current video-language models struggle with long-video understanding due to
limited context lengths and reliance on sparse frame subsampling, often leading
to information loss. This paper introduces $\infty$-Video, which can process
arbitrarily long videos through a continuous-time long-term memory (LTM)
consolidatio... |
2501.19099 | Unraveling Zeroth-Order Optimization through the Lens of Low-Dimensional
Structured Perturbations | cs.LG | Zeroth-order (ZO) optimization has emerged as a promising alternative to
gradient-based backpropagation methods, particularly for black-box optimization
and large language model (LLM) fine-tuning. However, ZO methods suffer from
slow convergence due to high-variance stochastic gradient estimators. While
structured pe... |
2501.19102 | Reinforcement Learning on Reconfigurable Hardware: Overcoming Material
Variability in Laser Material Processing | cs.LG | Ensuring consistent processing quality is challenging in laser processes due
to varying material properties and surface conditions. Although some approaches
have shown promise in solving this problem via automation, they often rely on
predetermined targets or are limited to simulated environments. To address
these sh... |
2501.19104 | Neural Collapse Beyond the Unconstrained Features Model: Landscape,
Dynamics, and Generalization in the Mean-Field Regime | cs.LG | Neural Collapse is a phenomenon where the last-layer representations of a
well-trained neural network converge to a highly structured geometry. In this
paper, we focus on its first (and most basic) property, known as NC1: the
within-class variability vanishes. While prior theoretical studies establish
the occurrence ... |
2501.19105 | Relating Misfit to Gain in Weak-to-Strong Generalization Beyond the
Squared Loss | cs.LG math.PR | The paradigm of weak-to-strong generalization constitutes the training of a
strong AI model on data labeled by a weak AI model, with the goal that the
strong model nevertheless outperforms its weak supervisor on the target task of
interest. For the setting of real-valued regression with the squared loss,
recent work ... |
2501.19107 | Brain-inspired sparse training enables Transformers and LLMs to perform
as fully connected | cs.LG | This study aims to enlarge our current knowledge on application of
brain-inspired network science principles for training artificial neural
networks (ANNs) with sparse connectivity. Dynamic sparse training (DST) can
reduce the computational demands in ANNs, but faces difficulties to keep peak
performance at high spar... |
2501.19111 | A Benchmark for Incremental Micro-expression Recognition | cs.CV cs.AI | Micro-expression recognition plays a pivotal role in understanding hidden
emotions and has applications across various fields. Traditional recognition
methods assume access to all training data at once, but real-world scenarios
involve continuously evolving data streams. To respond to the requirement of
adapting to n... |
2501.19112 | Logical Modalities within the European AI Act: An Analysis | cs.AI cs.CY cs.LO | The paper presents a comprehensive analysis of the European AI Act in terms
of its logical modalities, with the aim of preparing its formal representation,
for example, within the logic-pluralistic Knowledge Engineering Framework and
Methodology (LogiKEy). LogiKEy develops computational tools for normative
reasoning ... |
2501.19113 | Genetic AI: Evolutionary Simulation for Data Analysis | cs.NE | We introduce Genetic AI, a novel method for data analysis by evolutionary
simulations. The method can be applied to data of any domain and allows for a
data-less training of AI models. Without employing predefined rules or training
data, Genetic AI first converts the input data into genes and organisms. In a
simulati... |
2501.19114 | Principal Components for Neural Network Initialization | cs.LG cs.AI | Principal Component Analysis (PCA) is a commonly used tool for dimension
reduction and denoising. Therefore, it is also widely used on the data prior to
training a neural network. However, this approach can complicate the
explanation of explainable AI (XAI) methods for the decision of the model. In
this work, we anal... |
2501.19116 | A Theoretical Justification for Asymmetric Actor-Critic Algorithms | cs.LG stat.ML | In reinforcement learning for partially observable environments, many
successful algorithms were developed within the asymmetric learning paradigm.
This paradigm leverages additional state information available at training time
for faster learning. Although the proposed learning objectives are usually
theoretically s... |
2501.19122 | FedRTS: Federated Robust Pruning via Combinatorial Thompson Sampling | cs.LG cs.AI | Federated Learning (FL) enables collaborative model training across
distributed clients without data sharing, but its high computational and
communication demands strain resource-constrained devices. While existing
methods use dynamic pruning to improve efficiency by periodically adjusting
sparse model topologies whi... |
2501.19125 | Upper Bounds on the Minimum Distance of Structured LDPC Codes | cs.IT math.IT | We investigate the minimum distance of structured binary Low-Density
Parity-Check (LDPC) codes whose parity-check matrices are of the form
$[\mathbf{C} \vert \mathbf{M}]$ where $\mathbf{C}$ is circulant and of column
weight $2$, and $\mathbf{M}$ has fixed column weight $r \geq 3$ and row weight
at least $1$. These co... |
2501.19128 | Shaping Sparse Rewards in Reinforcement Learning: A Semi-supervised
Approach | cs.LG cs.AI | In many real-world scenarios, reward signal for agents are exceedingly
sparse, making it challenging to learn an effective reward function for reward
shaping. To address this issue, our approach performs reward shaping not only
by utilizing non-zero-reward transitions but also by employing the
Semi-Supervised Learnin... |
2501.19129 | RGB-Event ISP: The Dataset and Benchmark | cs.CV eess.IV | Event-guided imaging has received significant attention due to its potential
to revolutionize instant imaging systems. However, the prior methods primarily
focus on enhancing RGB images in a post-processing manner, neglecting the
challenges of image signal processor (ISP) dealing with event sensor and the
benefits ev... |
2501.19133 | Decorrelated Soft Actor-Critic for Efficient Deep Reinforcement Learning | cs.LG cs.AI | The effectiveness of credit assignment in reinforcement learning (RL) when
dealing with high-dimensional data is influenced by the success of
representation learning via deep neural networks, and has implications for the
sample efficiency of deep RL algorithms. Input decorrelation has been
previously introduced as a ... |
2501.19134 | Mixed Feelings: Cross-Domain Sentiment Classification of Patient
Feedback | cs.CL | Sentiment analysis of patient feedback from the public health domain can aid
decision makers in evaluating the provided services. The current paper focuses
on free-text comments in patient surveys about general practitioners and
psychiatric healthcare, annotated with four sentence-level polarity classes --
positive, ... |
2501.19137 | A Metric for the Balance of Information in Graph Learning | cs.LG cs.AI | Graph learning on molecules makes use of information from both the molecular
structure and the features attached to that structure. Much work has been
conducted on biasing either towards structure or features, with the aim that
bias bolsters performance. Identifying which information source a dataset
favours, and the... |
2501.19140 | Transformation trees -- documentation of multimodal image registration | cs.CV | The paper presents proposals for the application of a tree structure to the
documentation of a set of transformations obtained as a result of various
registrations of multimodal images obtained in coordinate systems associated
with acquisition devices and being registered in one patient-specific
coordinate system. A ... |
2501.19143 | Imitation Game for Adversarial Disillusion with Multimodal Generative
Chain-of-Thought Role-Play | cs.AI cs.CR cs.CV | As the cornerstone of artificial intelligence, machine perception confronts a
fundamental threat posed by adversarial illusions. These adversarial attacks
manifest in two primary forms: deductive illusion, where specific stimuli are
crafted based on the victim model's general decision logic, and inductive
illusion, w... |
2501.19145 | Improving Multi-Label Contrastive Learning by Leveraging Label
Distribution | cs.LG cs.AI cs.CV | In multi-label learning, leveraging contrastive learning to learn better
representations faces a key challenge: selecting positive and negative samples
and effectively utilizing label information. Previous studies selected positive
and negative samples based on the overlap between labels and used them for
label-wise ... |
2501.19148 | Constant-Factor Distortion Mechanisms for $k$-Committee Election | cs.GT cs.DS cs.MA | In the $k$-committee election problem, we wish to aggregate the preferences
of $n$ agents over a set of alternatives and select a committee of $k$
alternatives that minimizes the cost incurred by the agents. While we typically
assume that agent preferences are captured by a cardinal utility function, in
many contexts... |
2501.19149 | On the inductive bias of infinite-depth ResNets and the bottleneck rank | cs.LG cs.AI stat.ML | We compute the minimum-norm weights of a deep linear ResNet, and find that
the inductive bias of this architecture lies between minimizing nuclear norm
and rank. This implies that, with appropriate hyperparameters, deep nonlinear
ResNets have an inductive bias towards minimizing bottleneck rank.
|
2501.19153 | Test-Time Training Scaling for Chemical Exploration in Drug Design | cs.LG | Chemical language models for molecular design have the potential to find
solutions to multi-parameter optimization problems in drug discovery via
reinforcement learning (RL). A key requirement to achieve this is the capacity
to "search" chemical space to identify all molecules of interest. Here, we
propose a challeng... |
2501.19155 | SWAT: Sliding Window Adversarial Training for Gradual Domain Adaptation | cs.CV cs.AI | Domain shifts are critical issues that harm the performance of machine
learning. Unsupervised Domain Adaptation (UDA) mitigates this issue but suffers
when the domain shifts are steep and drastic. Gradual Domain Adaptation (GDA)
alleviates this problem in a mild way by gradually adapting from the source to
the target... |
2501.19158 | A theoretical framework for overfitting in energy-based modeling | cs.LG cond-mat.dis-nn cond-mat.stat-mech | We investigate the impact of limited data on training pairwise energy-based
models for inverse problems aimed at identifying interaction networks.
Utilizing the Gaussian model as testbed, we dissect training trajectories
across the eigenbasis of the coupling matrix, exploiting the independent
evolution of eigenmodes ... |
2501.19159 | GDO: Gradual Domain Osmosis | cs.CV | In this paper, we propose a new method called Gradual Domain Osmosis, which
aims to solve the problem of smooth knowledge migration from source domain to
target domain in Gradual Domain Adaptation (GDA). Traditional Gradual Domain
Adaptation methods mitigate domain bias by introducing intermediate domains and
self-tr... |
2501.19160 | RMDM: Radio Map Diffusion Model with Physics Informed | cs.CV | With the rapid development of wireless communication technology, the
efficient utilization of spectrum resources, optimization of communication
quality, and intelligent communication have become critical. Radio map
reconstruction is essential for enabling advanced applications, yet challenges
such as complex signal p... |
2501.19161 | Locality-aware Surrogates for Gradient-based Black-box Optimization | cs.LG | In physics and engineering, many processes are modeled using
non-differentiable black-box simulators, making the optimization of such
functions particularly challenging. To address such cases, inspired by the
Gradient Theorem, we propose locality-aware surrogate models for active
model-based black-box optimization. W... |
2501.19164 | Poison as Cure: Visual Noise for Mitigating Object Hallucinations in
LVMs | cs.CV | Large vision-language models (LVMs) extend large language models (LLMs) with
visual perception capabilities, enabling them to process and interpret visual
information. A major challenge compromising their reliability is object
hallucination that LVMs may generate plausible but factually inaccurate
information. We pro... |
2501.19168 | Implications of zero-growth economics analysed with an agent-based model | econ.GN cs.MA q-fin.EC | The ever-approaching limits of the Earth's biosphere and the potentially
catastrophic consequences caused by climate change have begun to call into
question the endless growth of the economy. There is increasing interest in the
prospects of zero economic growth from the degrowth and post-growth literature.
In particu... |
2501.19172 | PSyDUCK: Training-Free Steganography for Latent Diffusion | cs.LG cs.CR | Recent advances in AI-generated steganography highlight its potential for
safeguarding the privacy of vulnerable democratic actors, including aid
workers, journalists, and whistleblowers operating in oppressive regimes. In
this work, we address current limitations and establish the foundations for
large-throughput ge... |
2501.19176 | Augmented Intelligence for Multimodal Virtual Biopsy in Breast Cancer
Using Generative Artificial Intelligence | eess.IV cs.AI cs.CV | Full-Field Digital Mammography (FFDM) is the primary imaging modality for
routine breast cancer screening; however, its effectiveness is limited in
patients with dense breast tissue or fibrocystic conditions. Contrast-Enhanced
Spectral Mammography (CESM), a second-level imaging technique, offers enhanced
accuracy in ... |
2501.19178 | No Foundations without Foundations -- Why semi-mechanistic models are
essential for regulatory biology | cs.LG | Despite substantial efforts, deep learning has not yet delivered a
transformative impact on elucidating regulatory biology, particularly in the
realm of predicting gene expression profiles. Here, we argue that genuine
"foundation models" of regulatory biology will remain out of reach unless
guided by frameworks that ... |
2501.19179 | Learning Non-Local Molecular Interactions via Equivariant Local
Representations and Charge Equilibration | physics.chem-ph cs.LG physics.comp-ph | Graph Neural Network (GNN) potentials relying on chemical locality offer
near-quantum mechanical accuracy at significantly reduced computational costs.
By propagating local information to distance particles, Message-passing neural
networks (MPNNs) extend the locality concept to model interactions beyond their
local n... |
2501.19180 | Enhancing Model Defense Against Jailbreaks with Proactive Safety
Reasoning | cs.CR cs.AI | Large language models (LLMs) are vital for a wide range of applications yet
remain susceptible to jailbreak threats, which could lead to the generation of
inappropriate responses. Conventional defenses, such as refusal and adversarial
training, often fail to cover corner cases or rare domains, leaving LLMs still
vuln... |
2501.19182 | A Communication Framework for Compositional Generation | cs.LG | Compositionality and compositional generalization--the ability to understand
novel combinations of known concepts--are central characteristics of human
language and are hypothesized to be essential for human cognition. In machine
learning, the emergence of this property has been studied in a communication
game settin... |
2501.19183 | Position: Curvature Matrices Should Be Democratized via Linear Operators | cs.LG | Structured large matrices are prevalent in machine learning. A particularly
important class is curvature matrices like the Hessian, which are central to
understanding the loss landscape of neural nets (NNs), and enable second-order
optimization, uncertainty quantification, model pruning, data attribution, and
more. H... |
2501.19184 | A Survey on Class-Agnostic Counting: Advancements from Reference-Based
to Open-World Text-Guided Approaches | cs.CV | Visual object counting has recently shifted towards class-agnostic counting
(CAC), which addresses the challenge of counting objects across arbitrary
categories -- a crucial capability for flexible and generalizable counting
systems. Unlike humans, who effortlessly identify and count objects from
diverse categories w... |
2501.19191 | Secured Communication Schemes for UAVs in 5G: CRYSTALS-Kyber and IDS | cs.CR cs.AI | This paper introduces a secure communication architecture for Unmanned Aerial
Vehicles (UAVs) and ground stations in 5G networks, addressing critical
challenges in network security. The proposed solution integrates the Advanced
Encryption Standard (AES) with Elliptic Curve Cryptography (ECC) and
CRYSTALS-Kyber for ke... |
2501.19194 | APEX: Automated Parameter Exploration for Low-Power Wireless Protocols | cs.NI cs.SY eess.SY | Careful parametrization of networking protocols is crucial to maximize the
performance of low-power wireless systems and ensure that stringent application
requirements can be met. This is a non-trivial task involving thorough
characterization on testbeds and requiring expert knowledge. Unfortunately, the
community st... |
2501.19195 | Rethinking Early Stopping: Refine, Then Calibrate | cs.LG cs.AI | Machine learning classifiers often produce probabilistic predictions that are
critical for accurate and interpretable decision-making in various domains. The
quality of these predictions is generally evaluated with proper losses like
cross-entropy, which decompose into two components: calibration error assesses
gener... |
2501.19196 | RaySplats: Ray Tracing based Gaussian Splatting | cs.CV | 3D Gaussian Splatting (3DGS) is a process that enables the direct creation of
3D objects from 2D images. This representation offers numerous advantages,
including rapid training and rendering. However, a significant limitation of
3DGS is the challenge of incorporating light and shadow reflections, primarily
due to th... |
2501.19200 | A Variational Perspective on Generative Protein Fitness Optimization | cs.LG | The goal of protein fitness optimization is to discover new protein variants
with enhanced fitness for a given use. The vast search space and the sparsely
populated fitness landscape, along with the discrete nature of protein
sequences, pose significant challenges when trying to determine the gradient
towards configu... |
2501.19201 | Efficient Reasoning with Hidden Thinking | cs.CL cs.AI cs.LG | Chain-of-Thought (CoT) reasoning has become a powerful framework for
improving complex problem-solving capabilities in Multimodal Large Language
Models (MLLMs). However, the verbose nature of textual reasoning introduces
significant inefficiencies. In this work, we propose $\textbf{Heima}$ (as
hidden llama), an effic... |
2501.19202 | Improving the Robustness of Representation Misdirection for Large
Language Model Unlearning | cs.CL | Representation Misdirection (RM) and variants are established large language
model (LLM) unlearning methods with state-of-the-art performance. In this
paper, we show that RM methods inherently reduce models' robustness, causing
them to misbehave even when a single non-adversarial forget-token is in the
retain-query. ... |
2501.19203 | Single cell resolution 3D imaging and segmentation within intact live
tissues | q-bio.QM cs.AI cs.CV q-bio.CB q-bio.TO | Epithelial cells form diverse structures from squamous spherical organoids to
densely packed pseudostratified tissues. Quantification of cellular properties
in these contexts requires high-resolution deep imaging and computational
techniques to achieve truthful three-dimensional (3D) structural features.
Here, we des... |
2501.19205 | RIGNO: A Graph-based framework for robust and accurate operator learning
for PDEs on arbitrary domains | cs.LG | Learning the solution operators of PDEs on arbitrary domains is challenging
due to the diversity of possible domain shapes, in addition to the often
intricate underlying physics. We propose an end-to-end graph neural network
(GNN) based neural operator to learn PDE solution operators from data on point
clouds in arbi... |
2501.19206 | An Empirical Game-Theoretic Analysis of Autonomous Cyber-Defence Agents | cs.AI cs.CR cs.GT | The recent rise in increasingly sophisticated cyber-attacks raises the need
for robust and resilient autonomous cyber-defence (ACD) agents. Given the
variety of cyber-attack tactics, techniques and procedures (TTPs) employed,
learning approaches that can return generalisable policies are desirable.
Meanwhile, the ass... |
2501.19207 | Learning Sheaf Laplacian Optimizing Restriction Maps | eess.SP cs.LG | The aim of this paper is to propose a novel framework to infer the sheaf
Laplacian, including the topology of a graph and the restriction maps, from a
set of data observed over the nodes of a graph. The proposed method is based on
sheaf theory, which represents an important generalization of graph signal
processing. ... |
2501.19208 | Learning While Repositioning in On-Demand Vehicle Sharing Networks | stat.ML cs.LG math.OC | We consider a network inventory problem motivated by one-way, on-demand
vehicle sharing services. Due to uncertainties in both demand and returns, as
well as a fixed number of rental units across an $n$-location network, the
service provider must periodically reposition vehicles to match supply with
demand spatially ... |
2501.19214 | A single-loop SPIDER-type stochastic subgradient method for
expectation-constrained nonconvex nonsmooth optimization | math.OC cs.CC cs.LG cs.NA math.NA | Many real-world problems, such as those with fairness constraints, involve
complex expectation constraints and large datasets, necessitating the design of
efficient stochastic methods to solve them. Most existing research focuses on
cases with no {constraint} or easy-to-project constraints or deterministic
constraint... |
2501.19215 | Strassen Attention: Unlocking Compositional Abilities in Transformers
Based on a New Lower Bound Method | cs.LG cs.AI | We propose a novel method to evaluate the theoretical limits of Transformers,
allowing us to prove the first lower bounds against one-layer softmax
Transformers with infinite precision. We establish those bounds for three tasks
that require advanced reasoning. The first task, Match3 (Sanford et al., 2023),
requires l... |
2501.19216 | E2Former: A Linear-time Efficient and Equivariant Transformer for
Scalable Molecular Modeling | cs.LG cond-mat.mtrl-sci | Equivariant Graph Neural Networks (EGNNs) have demonstrated significant
success in modeling microscale systems, including those in chemistry, biology
and materials science. However, EGNNs face substantial computational challenges
due to the high cost of constructing edge features via spherical tensor
products, making... |
2501.19218 | A parallelizable variant of HCA* | eess.SY cs.SY | This paper presents a parallelizable variant of the well-known Hierarchical
Cooperative A* algorithm (HCA*) for the multi-agent path finding (MAPF)
problem. In this variant, all agents initially find their shortest paths
disregarding the presence of others. This is done using A*. Then an
intersection graph (IG) is co... |
2501.19220 | Analysis and predictability of centrality measures in competition
networks | cs.SI | The Common Out-Neighbor (or CON) score quantifies shared influence through
outgoing links in competitive contexts. A dynamic analysis of competition
networks reveals the CON score as a powerful predictor of node rankings.
Defined in first-order and second-order forms, the CON score captures both
direct and indirect c... |
2501.19223 | Through the Looking Glass: LLM-Based Analysis of AR/VR Android
Applications Privacy Policies | cs.CR cs.LG | \begin{abstract} This paper comprehensively analyzes privacy policies in
AR/VR applications, leveraging BERT, a state-of-the-art text classification
model, to evaluate the clarity and thoroughness of these policies. By comparing
the privacy policies of AR/VR applications with those of free and premium
websites, this ... |
2501.19224 | Fast exact recovery of noisy matrix from few entries: the infinity norm
approach | math.ST cs.LG math.CO math.PR stat.AP stat.TH | The matrix recovery (completion) problem, a central problem in data science
and theoretical computer science, is to recover a matrix $A$ from a relatively
small sample of entries.
While such a task is impossible in general, it has been shown that one can
recover $A$ exactly in polynomial time, with high probability... |
2501.19227 | Integrating Semi-Supervised and Active Learning for Semantic
Segmentation | cs.CV cs.AI | In this paper, we propose a novel active learning approach integrated with an
improved semi-supervised learning framework to reduce the cost of manual
annotation and enhance model performance. Our proposed approach effectively
leverages both the labelled data selected through active learning and the
unlabelled data e... |
2501.19232 | A Zero-Shot Generalization Framework for LLM-Driven Cross-Domain
Sequential Recommendation | cs.IR cs.AI | Zero-shot cross-domain sequential recommendation (ZCDSR) enables predictions
in unseen domains without the need for additional training or fine-tuning,
making it particularly valuable in data-sparse environments where traditional
models struggle. Recent advancements in large language models (LLMs) have
greatly improv... |
2501.19234 | Hourly Short Term Load Forecasting for Residential Buildings and Energy
Communities | cs.LG | Electricity load consumption may be extremely complex in terms of profile
patterns, as it depends on a wide range of human factors, and it is often
correlated with several exogenous factors, such as the availability of
renewable energy and the weather conditions. The first goal of this paper is to
investigate the per... |
2501.19237 | DINAMO: Dynamic and INterpretable Anomaly MOnitoring for Large-Scale
Particle Physics Experiments | hep-ex cs.LG | Ensuring reliable data collection in large-scale particle physics experiments
demands Data Quality Monitoring (DQM) procedures to detect possible detector
malfunctions and preserve data integrity. Traditionally, this
resource-intensive task has been handled by human shifters that struggle with
frequent changes in ope... |
2501.19239 | Multi-agent Multi-armed Bandit with Fully Heavy-tailed Dynamics | cs.LG stat.ML | We study decentralized multi-agent multi-armed bandits in fully heavy-tailed
settings, where clients communicate over sparse random graphs with heavy-tailed
degree distributions and observe heavy-tailed (homogeneous or heterogeneous)
reward distributions with potentially infinite variance. The objective is to
maximiz... |
2501.19241 | Emancipatory Information Retrieval | cs.IR cs.HC | Our world today is facing a confluence of several mutually reinforcing crises
each of which intersects with concerns of social justice and emancipation. This
paper is a provocation for the role of computer-mediated information access in
our emancipatory struggles. We define emancipatory information retrieval as the
s... |
2501.19243 | Accelerating Diffusion Transformer via Error-Optimized Cache | cs.CV | Diffusion Transformer (DiT) is a crucial method for content generation.
However, it needs a lot of time to sample. Many studies have attempted to use
caching to reduce the time consumption of sampling. Existing caching methods
accelerate generation by reusing DiT features from the previous time step and
skipping calc... |
2501.19245 | SHARPIE: A Modular Framework for Reinforcement Learning and Human-AI
Interaction Experiments | cs.AI cs.HC | Reinforcement learning (RL) offers a general approach for modeling and
training AI agents, including human-AI interaction scenarios. In this paper, we
propose SHARPIE (Shared Human-AI Reinforcement Learning Platform for
Interactive Experiments) to address the need for a generic framework to support
experiments with R... |
2501.19247 | Clustering in hyperbolic balls | cs.LG | The idea of representations of the data in negatively curved manifolds
recently attracted a lot of attention and gave a rise to the new research
direction named {\it hyperbolic machine learning} (ML). In order to unveil the
full potential of this new paradigm, efficient techniques for data analysis and
statistical mo... |
2501.19252 | Inference-Time Text-to-Video Alignment with Diffusion Latent Beam Search | cs.CV | The remarkable progress in text-to-video diffusion models enables
photorealistic generations, although the contents of the generated video often
include unnatural movement or deformation, reverse playback, and motionless
scenes. Recently, an alignment problem has attracted huge attention, where we
steer the output of... |
2501.19254 | Linear $Q$-Learning Does Not Diverge: Convergence Rates to a Bounded Set | cs.LG cs.AI stat.ML | $Q$-learning is one of the most fundamental reinforcement learning
algorithms. Previously, it is widely believed that $Q$-learning with linear
function approximation (i.e., linear $Q$-learning) suffers from possible
divergence. This paper instead establishes the first $L^2$ convergence rate of
linear $Q$-learning to ... |
2501.19255 | ContextFormer: Redefining Efficiency in Semantic Segmentation | cs.CV | Semantic segmentation assigns labels to pixels in images, a critical yet
challenging task in computer vision. Convolutional methods, although capturing
local dependencies well, struggle with long-range relationships. Vision
Transformers (ViTs) excel in global context capture but are hindered by high
computational dem... |
2501.19256 | Objective Metrics for Human-Subjects Evaluation in Explainable
Reinforcement Learning | cs.AI cs.HC cs.RO | Explanation is a fundamentally human process. Understanding the goal and
audience of the explanation is vital, yet existing work on explainable
reinforcement learning (XRL) routinely does not consult humans in their
evaluations. Even when they do, they routinely resort to subjective metrics,
such as confidence or und... |
2501.19258 | VisualSpeech: Enhance Prosody with Visual Context in TTS | cs.CL | Text-to-Speech (TTS) synthesis faces the inherent challenge of producing
multiple speech outputs with varying prosody from a single text input. While
previous research has addressed this by predicting prosodic information from
both text and speech, additional contextual information, such as visual
features, remains u... |
2501.19259 | Neuro-LIFT: A Neuromorphic, LLM-based Interactive Framework for
Autonomous Drone FlighT at the Edge | cs.RO cs.CV cs.LG cs.NE cs.SY eess.SY | The integration of human-intuitive interactions into autonomous systems has
been limited. Traditional Natural Language Processing (NLP) systems struggle
with context and intent understanding, severely restricting human-robot
interaction. Recent advancements in Large Language Models (LLMs) have
transformed this dynami... |
2501.19264 | mFollowIR: a Multilingual Benchmark for Instruction Following in
Retrieval | cs.IR cs.CL cs.LG | Retrieval systems generally focus on web-style queries that are short and
underspecified. However, advances in language models have facilitated the
nascent rise of retrieval models that can understand more complex queries with
diverse intents. However, these efforts have focused exclusively on English;
therefore, we ... |
2501.19265 | Medical Semantic Segmentation with Diffusion Pretrain | cs.CV cs.LG | Recent advances in deep learning have shown that learning robust feature
representations is critical for the success of many computer vision tasks,
including medical image segmentation. In particular, both transformer and
convolutional-based architectures have benefit from leveraging pretext tasks
for pretraining. Ho... |
2501.19266 | Jackpot! Alignment as a Maximal Lottery | cs.AI cs.LG econ.TH | Reinforcement Learning from Human Feedback (RLHF), the standard for aligning
Large Language Models (LLMs) with human values, is known to fail to satisfy
properties that are intuitively desirable, such as respecting the preferences
of the majority \cite{ge2024axioms}. To overcome these issues, we propose the
use of a ... |
2501.19267 | Transformer-Based Financial Fraud Detection with Cloud-Optimized
Real-Time Streaming | cs.CE | As the financial industry becomes more interconnected and reliant on digital
systems, fraud detection systems must evolve to meet growing threats.
Cloud-enabled Transformer models present a transformative opportunity to
address these challenges. By leveraging the scalability, flexibility, and
advanced AI capabilities... |
2501.19270 | Imagine with the Teacher: Complete Shape in a Multi-View Distillation
Way | cs.CV | Point cloud completion aims to recover the completed 3D shape of an object
from its partial observation caused by occlusion, sensor's limitation, noise,
etc. When some key semantic information is lost in the incomplete point cloud,
the neural network needs to infer the missing part based on the input
information. Int... |
2501.19271 | Concept-Based Explainable Artificial Intelligence: Metrics and
Benchmarks | cs.AI cs.LG | Concept-based explanation methods, such as concept bottleneck models (CBMs),
aim to improve the interpretability of machine learning models by linking their
decisions to human-understandable concepts, under the critical assumption that
such concepts can be accurately attributed to the network's feature space.
However... |
2501.19273 | Minimax discrete distribution estimation with self-consumption | cs.IT math.IT math.ST stat.TH | Learning distributions from i.i.d. samples is a well-understood problem.
However, advances in generative machine learning prompt an interesting new,
non-i.i.d. setting: after receiving a certain number of samples, an estimated
distribution is fixed, and samples from this estimate are drawn and introduced
into the sam... |
2501.19274 | GO: The Great Outdoors Multimodal Dataset | cs.RO | The Great Outdoors (GO) dataset is a multi-modal annotated data resource
aimed at advancing ground robotics research in unstructured environments. This
dataset provides the most comprehensive set of data modalities and annotations
compared to existing off-road datasets. In total, the GO dataset includes six
unique se... |
2501.19277 | On Pareto Optimality for the Multinomial Logistic Bandit | stat.ML cs.LG | We provide a new online learning algorithm for tackling the Multinomial Logit
Bandit (MNL-Bandit) problem. Despite the challenges posed by the combinatorial
nature of the MNL model, we develop a novel Upper Confidence Bound (UCB)-based
method that achieves Pareto optimality by balancing regret minimization and
estima... |
2501.19278 | Pheromone-based Learning of Optimal Reasoning Paths | cs.CL | Large Language Models (LLMs) have demonstrated remarkable reasoning
capabilities through chain-of-thought prompting, yet discovering effective
reasoning methods for complex problems remains challenging due to the vast
space of possible intermediate steps. We introduce Ant Colony
Optimization-guided Tree of Thought (A... |
2501.19279 | S-VOTE: Similarity-based Voting for Client Selection in Decentralized
Federated Learning | cs.LG cs.DC | Decentralized Federated Learning (DFL) enables collaborative,
privacy-preserving model training without relying on a central server. This
decentralized approach reduces bottlenecks and eliminates single points of
failure, enhancing scalability and resilience. However, DFL also introduces
challenges such as suboptimal... |
2501.19281 | Statistical Physics of Deep Neural Networks: Generalization Capability,
Beyond the Infinite Width, and Feature Learning | cond-mat.dis-nn cs.LG | Deep Neural Networks (DNNs) excel at many tasks, often rivaling or surpassing
human performance. Yet their internal processes remain elusive, frequently
described as "black boxes." While performance can be refined experimentally,
achieving a fundamental grasp of their inner workings is still a challenge.
Statistica... |
2501.19283 | Application of Generative Adversarial Network (GAN) for Synthetic
Training Data Creation to improve performance of ANN Classifier for
extracting Built-Up pixels from Landsat Satellite Imagery | cs.CV cs.LG | Training a neural network for pixel based classification task using low
resolution Landsat images is difficult as the size of the training data is
usually small due to less number of available pixels that represent a single
class without any mixing with other classes. Due to this scarcity of training
data, neural net... |
2501.19285 | OneBatchPAM: A Fast and Frugal K-Medoids Algorithm | cs.LG | This paper proposes a novel k-medoids approximation algorithm to handle
large-scale datasets with reasonable computational time and memory complexity.
We develop a local-search algorithm that iteratively improves the medoid
selection based on the estimation of the k-medoids objective. A single batch of
size m << n pr... |
2501.19287 | Differentially Private In-context Learning via Sampling Few-shot Mixed
with Zero-shot Outputs | cs.LG | In-context learning (ICL) has shown promising improvement in downstream task
adaptation of LLMs by augmenting prompts with relevant input-output examples
(demonstrations). However, the ICL demonstrations can contain privacy-sensitive
information, which can be leaked and/or regurgitated by the LLM output.
Differential... |
2501.19297 | Analysis of LLMs vs Human Experts in Requirements Engineering | cs.SE cs.AI | The majority of research around Large Language Models (LLM) application to
software development has been on the subject of code generation. There is
little literature on LLMs' impact on requirements engineering (RE), which deals
with the process of developing and verifying the system requirements. Within
RE, there is... |
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