id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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2502.04746 | On $(\mathcal{L},\mathcal{P})$-Twisted Generalized Reed-Solomon Codes | cs.IT math.IT | Twisted generalized Reed-Solomon (TGRS) codes are an extension of the
generalized Reed-Solomon (GRS) codes by adding specific twists, which attract
much attention recently. This paper presents an in-depth and comprehensive
investigation of the TGRS codes for the most general form by using a universal
method. At first... |
2502.04747 | Every Software as an Agent: Blueprint and Case Study | cs.SE cs.AI | The rise of (multimodal) large language models (LLMs) has shed light on
software agent -- where software can understand and follow user instructions in
natural language. However, existing approaches such as API-based and GUI-based
agents are far from satisfactory at accuracy and efficiency aspects. Instead,
we advoca... |
2502.04748 | Self-Supervised Learning for Pre-training Capsule Networks: Overcoming
Medical Imaging Dataset Challenges | cs.CV cs.LG | Deep learning techniques are increasingly being adopted in diagnostic medical
imaging. However, the limited availability of high-quality, large-scale medical
datasets presents a significant challenge, often necessitating the use of
transfer learning approaches. This study investigates self-supervised learning
methods... |
2502.04749 | Bounding User Contributions in the Worst-Case for User-Level
Differentially Private Mean Estimation | cs.IT math.IT | In this article, we revisit the well-studied problem of mean estimation under
user-level $\varepsilon$-differential privacy (DP). While user-level
$\varepsilon$-DP mechanisms for mean estimation, which typically bound (or
clip) user contributions to reduce sensitivity, are well-known, an analysis of
their estimation ... |
2502.04750 | Tighter sparse variational Gaussian processes | stat.ML cs.LG | Sparse variational Gaussian process (GP) approximations based on inducing
points have become the de facto standard for scaling GPs to large datasets,
owing to their theoretical elegance, computational efficiency, and ease of
implementation. This paper introduces a provably tighter variational
approximation by relaxin... |
2502.04751 | Holistically Guided Monte Carlo Tree Search for Intricate Information
Seeking | cs.IR cs.CL | In the era of vast digital information, the sheer volume and heterogeneity of
available information present significant challenges for intricate information
seeking. Users frequently face multistep web search tasks that involve
navigating vast and varied data sources. This complexity demands every step
remains compre... |
2502.04756 | Concept Navigation and Classification via Open Source Large Language
Model Processing | cs.CL cs.AI cs.LG | This paper presents a novel methodological framework for detecting and
classifying latent constructs, including frames, narratives, and topics, from
textual data using Open-Source Large Language Models (LLMs). The proposed
hybrid approach combines automated summarization with human-in-the-loop
validation to enhance t... |
2502.04757 | ELITE: Enhanced Language-Image Toxicity Evaluation for Safety | cs.CV cs.CL | Current Vision Language Models (VLMs) remain vulnerable to malicious prompts
that induce harmful outputs. Existing safety benchmarks for VLMs primarily rely
on automated evaluation methods, but these methods struggle to detect implicit
harmful content or produce inaccurate evaluations. Therefore, we found that
existi... |
2502.04758 | Differential Privacy of Quantum and Quantum-Inspired-Classical
Recommendation Algorithms | quant-ph cs.CR cs.ET cs.LG | We analyze the DP (differential privacy) properties of the quantum
recommendation algorithm and the quantum-inspired-classical recommendation
algorithm. We discover that the quantum recommendation algorithm is a privacy
curating mechanism on its own, requiring no external noise, which is different
from traditional di... |
2502.04759 | Enhancing Phishing Email Identification with Large Language Models | cs.CR cs.AI | Phishing has long been a common tactic used by cybercriminals and continues
to pose a significant threat in today's digital world. When phishing attacks
become more advanced and sophisticated, there is an increasing need for
effective methods to detect and prevent them. To address the challenging
problem of detecting... |
2502.04760 | Graph Federated Learning Based Proactive Content Caching in Edge
Computing | cs.LG cs.AI | With the rapid growth of mobile data traffic and the increasing prevalence of
video streaming, proactive content caching in edge computing has become crucial
for reducing latency and alleviating network congestion. However, traditional
caching strategies such as FIFO, LRU, and LFU fail to effectively predict
future c... |
2502.04762 | Autoregressive Generation of Static and Growing Trees | cs.CV | We propose a transformer architecture and training strategy for tree
generation. The architecture processes data at multiple resolutions and has an
hourglass shape, with middle layers processing fewer tokens than outer layers.
Similar to convolutional networks, we introduce longer range skip connections
to completent... |
2502.04763 | Shapley Value Approximation Based on k-Additive Games | cs.GT cs.LG | The Shapley value is the prevalent solution for fair division problems in
which a payout is to be divided among multiple agents. By adopting a
game-theoretic view, the idea of fair division and the Shapley value can also
be used in machine learning to quantify the individual contribution of features
or data points to... |
2502.04770 | Efficient Evaluation of Quantization-Effects in Neural Codecs | eess.AS cs.LG | Neural codecs, comprising an encoder, quantizer, and decoder, enable signal
transmission at exceptionally low bitrates. Training these systems requires
techniques like the straight-through estimator, soft-to-hard annealing, or
statistical quantizer emulation to allow a non-zero gradient across the
quantizer. Evaluati... |
2502.04771 | DMPA: Model Poisoning Attacks on Decentralized Federated Learning for
Model Differences | cs.LG cs.AI | Federated learning (FL) has garnered significant attention as a prominent
privacy-preserving Machine Learning (ML) paradigm. Decentralized FL (DFL)
eschews traditional FL's centralized server architecture, enhancing the
system's robustness and scalability. However, these advantages of DFL also
create new vulnerabilit... |
2502.04773 | An Extended Benchmarking of Multi-Agent Reinforcement Learning
Algorithms in Complex Fully Cooperative Tasks | cs.LG | Multi-Agent Reinforcement Learning (MARL) has recently emerged as a
significant area of research. However, MARL evaluation often lacks systematic
diversity, hindering a comprehensive understanding of algorithms' capabilities.
In particular, cooperative MARL algorithms are predominantly evaluated on
benchmarks such as... |
2502.04774 | SeDi-Instruct: Enhancing Alignment of Language Models through
Self-Directed Instruction Generation | cs.CL | The rapid evolution of Large Language Models (LLMs) has enabled the industry
to develop various AI-based services. Instruction tuning is considered
essential in adapting foundation models for target domains to provide
high-quality services to customers. A key challenge in instruction tuning is
obtaining high-quality ... |
2502.04777 | Community detection for directed networks revisited using bimodularity | cs.SI | Community structure is a key feature omnipresent in real-world network data.
Plethora of methods have been proposed to reveal subsets of densely
interconnected nodes using criteria such as the modularity index. These
approaches have been successful for undirected graphs, but directed edge
information has not yet been... |
2502.04778 | Behavior-Regularized Diffusion Policy Optimization for Offline
Reinforcement Learning | cs.LG cs.AI | The primary focus of offline reinforcement learning (RL) is to manage the
risk of hazardous exploitation of out-of-distribution actions. An effective
approach to achieve this goal is through behavior regularization, which
augments conventional RL objectives by incorporating constraints that enforce
the policy to rema... |
2502.04780 | SiriuS: Self-improving Multi-agent Systems via Bootstrapped Reasoning | cs.AI | Multi-agent AI systems powered by large language models (LLMs) are
increasingly applied to solve complex tasks. However, these systems often rely
on fragile, manually designed prompts and heuristics, making optimization
difficult. A key challenge in optimizing multi-agent systems is acquiring
suitable training data f... |
2502.04786 | Enhancing SQL Injection Detection and Prevention Using Generative Models | cs.CR cs.AI | SQL Injection (SQLi) continues to pose a significant threat to the security
of web applications, enabling attackers to manipulate databases and access
sensitive information without authorisation. Although advancements have been
made in detection techniques, traditional signature-based methods still
struggle to identi... |
2502.04789 | Probing Internal Representations of Multi-Word Verbs in Large Language
Models | cs.CL | This study investigates the internal representations of verb-particle
combinations, called multi-word verbs, within transformer-based large language
models (LLMs), specifically examining how these models capture lexical and
syntactic properties at different neural network layers. Using the BERT
architecture, we analy... |
2502.04790 | S$^2$-MAD: Breaking the Token Barrier to Enhance Multi-Agent Debate
Efficiency | cs.CL cs.AI | Large language models (LLMs) have demonstrated remarkable capabilities across
various natural language processing (NLP) scenarios, but they still face
challenges when handling complex arithmetic and logical reasoning tasks. While
Chain-Of-Thought (CoT) reasoning, self-consistency (SC) and self-correction
strategies h... |
2502.04793 | $t$-Testing the Waters: Empirically Validating Assumptions for Reliable
A/B-Testing | stat.ME cs.LG | A/B-tests are a cornerstone of experimental design on the web, with
wide-ranging applications and use-cases. The statistical $t$-test comparing
differences in means is the most commonly used method for assessing treatment
effects, often justified through the Central Limit Theorem (CLT). The CLT
ascertains that, as th... |
2502.04794 | MedMimic: Physician-Inspired Multimodal Fusion for Early Diagnosis of
Fever of Unknown Origin | eess.IV cs.AI cs.CV | Fever of unknown origin FUO remains a diagnostic challenge. MedMimic is
introduced as a multimodal framework inspired by real-world diagnostic
processes. It uses pretrained models such as DINOv2, Vision Transformer, and
ResNet-18 to convert high-dimensional 18F-FDG PET/CT imaging into
low-dimensional, semantically me... |
2502.04795 | Developmentally-plausible Working Memory Shapes a Critical Period for
Language Acquisition | cs.CL | Large language models possess general linguistic abilities but acquire
language less efficiently than humans. This study proposes a method for
integrating the developmental characteristics of working memory during the
critical period, a stage when human language acquisition is particularly
efficient, into the trainin... |
2502.04797 | Self-Rationalization in the Wild: A Large Scale Out-of-Distribution
Evaluation on NLI-related tasks | cs.CL | Free-text explanations are expressive and easy to understand, but many
datasets lack annotated explanation data, making it challenging to train models
for explainable predictions. To address this, we investigate how to use
existing explanation datasets for self-rationalization and evaluate models'
out-of-distribution... |
2502.04799 | A Regularized Newton Method for Nonconvex Optimization with Global and
Local Complexity Guarantees | math.OC cs.LG | We consider the problem of finding an $\epsilon$-stationary point of a
nonconvex function with a Lipschitz continuous Hessian and propose a quadratic
regularized Newton method incorporating a new class of regularizers constructed
from the current and previous gradients. The method leverages a recently
developed linea... |
2502.04804 | DetVPCC: RoI-based Point Cloud Sequence Compression for 3D Object
Detection | cs.CV | While MPEG-standardized video-based point cloud compression (VPCC) achieves
high compression efficiency for human perception, it struggles with a poor
trade-off between bitrate savings and detection accuracy when supporting 3D
object detectors. This limitation stems from VPCC's inability to prioritize
regions of diff... |
2502.04807 | Robust Conformal Outlier Detection under Contaminated Reference Data | stat.ML cs.LG stat.ME | Conformal prediction is a flexible framework for calibrating machine learning
predictions, providing distribution-free statistical guarantees. In outlier
detection, this calibration relies on a reference set of labeled inlier data to
control the type-I error rate. However, obtaining a perfectly labeled inlier
referen... |
2502.04809 | Humans Co-exist, So Must Embodied Artificial Agents | cs.LG | Modern embodied artificial agents excel in static, predefined tasks but fall
short in dynamic and long-term interactions with humans. On the other hand,
humans can adapt and evolve continuously, exploiting the situated knowledge
embedded in their environment and other agents, thus contributing to meaningful
interacti... |
2502.04813 | Describing Nonstationary Data Streams in Frequency Domain | cs.LG | Concept drift is among the primary challenges faced by the data stream
processing methods. The drift detection strategies, designed to counteract the
negative consequences of such changes, often rely on analyzing the problem
metafeatures. This work presents the Frequency Filtering Metadescriptor -- a
tool for charact... |
2502.04818 | Harnessing omnipresent oscillator networks as computational resource | cs.LG math.DS nlin.AO nlin.CD | Nature is pervaded with oscillatory behavior. In networks of coupled
oscillators patterns can arise when the system synchronizes to an external
input. Hence, these networks provide processing and memory of input. We present
a universal framework for harnessing oscillator networks as computational
resource. This reser... |
2502.04827 | Uplink Rate-Splitting Multiple Access for Mobile Edge Computing with
Short-Packet Communications | cs.IT eess.SP math.IT | In this paper, a Rate-Splitting Multiple Access (RSMA) scheme is proposed to
assist a Mobile Edge Computing (MEC) system where local computation tasks from
two users are offloaded to the MEC server, facilitated by uplink RSMA for
processing. The efficiency of the MEC service is hence primarily influenced by
the RSMA-... |
2502.04829 | Optimistic Gradient Learning with Hessian Corrections for
High-Dimensional Black-Box Optimization | cs.LG cs.AI | Black-box algorithms are designed to optimize functions without relying on
their underlying analytical structure or gradient information, making them
essential when gradients are inaccessible or difficult to compute. Traditional
methods for solving black-box optimization (BBO) problems predominantly rely on
non-param... |
2502.04832 | Memory Capacity of Nonlinear Recurrent Networks: Is it Informative? | cs.LG stat.ML | The total memory capacity (MC) of linear recurrent neural networks (RNNs) has
been proven to be equal to the rank of the corresponding Kalman controllability
matrix, and it is almost surely maximal for connectivity and input weight
matrices drawn from regular distributions. This fact questions the usefulness
of this ... |
2502.04834 | Lightweight Operations for Visual Speech Recognition | cs.CV cs.AI cs.CL cs.LG | Visual speech recognition (VSR), which decodes spoken words from video data,
offers significant benefits, particularly when audio is unavailable. However,
the high dimensionality of video data leads to prohibitive computational costs
that demand powerful hardware, limiting VSR deployment on resource-constrained
devic... |
2502.04837 | Online Robot Motion Planning Methodology Guided by Group Social
Proxemics Feature | cs.RO cs.SY eess.SY | Nowadays robot is supposed to demonstrate human-like perception, reasoning
and behavior pattern in social or service application. However, most of the
existing motion planning methods are incompatible with above requirement. A
potential reason is that the existing navigation algorithms usually intend to
treat people ... |
2502.04840 | Coherent Local Explanations for Mathematical Optimization | math.OC cs.LG | The surge of explainable artificial intelligence methods seeks to enhance
transparency and explainability in machine learning models. At the same time,
there is a growing demand for explaining decisions taken through complex
algorithms used in mathematical optimization. However, current explanation
methods do not tak... |
2502.04843 | PoI: Pixel of Interest for Novel View Synthesis Assisted Scene
Coordinate Regression | cs.CV | The task of estimating camera poses can be enhanced through novel view
synthesis techniques such as NeRF and Gaussian Splatting to increase the
diversity and extension of training data. However, these techniques often
produce rendered images with issues like blurring and ghosting, which
compromise their reliability. ... |
2502.04846 | UAV-Based Cell-Free Massive MIMO: Joint Placement and Power Optimization
under Fronthaul Capacity Limitations | eess.SP cs.IT math.IT | We consider a cell-free massive multiple-input multiple-output (mMIMO)
network, where unmanned aerial vehicles (UAVs) equipped with multiple antennas
serve as distributed UAV-access points (UAV-APs). These UAV-APs provide
seamless coverage by jointly serving user equipments (UEs) with out predefined
cell boundaries. ... |
2502.04847 | HumanDiT: Pose-Guided Diffusion Transformer for Long-form Human Motion
Video Generation | cs.CV | Human motion video generation has advanced significantly, while existing
methods still struggle with accurately rendering detailed body parts like hands
and faces, especially in long sequences and intricate motions. Current
approaches also rely on fixed resolution and struggle to maintain visual
consistency. To addre... |
2502.04849 | Advancing Wasserstein Convergence Analysis of Score-Based Models:
Insights from Discretization and Second-Order Acceleration | stat.ML cs.LG math.PR | Score-based diffusion models have emerged as powerful tools in generative
modeling, yet their theoretical foundations remain underexplored. In this work,
we focus on the Wasserstein convergence analysis of score-based diffusion
models. Specifically, we investigate the impact of various discretization
schemes, includi... |
2502.04850 | Aequa: Fair Model Rewards in Collaborative Learning via Slimmable
Networks | cs.LG cs.DC | Collaborative learning enables multiple participants to learn a single global
model by exchanging focused updates instead of sharing data. One of the core
challenges in collaborative learning is ensuring that participants are rewarded
fairly for their contributions, which entails two key sub-problems:
contribution as... |
2502.04852 | Relative Age Estimation Using Face Images | cs.CV | This work introduces a novel deep-learning approach for estimating age from a
single facial image by refining an initial age estimate. The refinement
leverages a reference face database of individuals with similar ages and
appearances. We employ a network that estimates age differences between an
input image and refe... |
2502.04863 | Enhancing Disinformation Detection with Explainable AI and Named Entity
Replacement | cs.CL | The automatic detection of disinformation presents a significant challenge in
the field of natural language processing. This task addresses a multifaceted
societal and communication issue, which needs approaches that extend beyond the
identification of general linguistic patterns through data-driven algorithms.
In th... |
2502.04864 | $TAR^2$: Temporal-Agent Reward Redistribution for Optimal Policy
Preservation in Multi-Agent Reinforcement Learning | cs.MA cs.AI cs.LG cs.RO | In cooperative multi-agent reinforcement learning (MARL), learning effective
policies is challenging when global rewards are sparse and delayed. This
difficulty arises from the need to assign credit across both agents and time
steps, a problem that existing methods often fail to address in episodic,
long-horizon task... |
2502.04870 | IPSeg: Image Posterior Mitigates Semantic Drift in Class-Incremental
Segmentation | cs.CV | Class incremental learning aims to enable models to learn from sequential,
non-stationary data streams across different tasks without catastrophic
forgetting. In class incremental semantic segmentation (CISS), the semantic
content of image pixels evolves over incremental phases, known as semantic
drift. In this work,... |
2502.04873 | Training-free Task-oriented Grasp Generation | cs.RO | This paper presents a training-free pipeline for task-oriented grasp
generation that combines pre-trained grasp generation models with
vision-language models (VLMs). Unlike traditional approaches that focus solely
on stable grasps, our method incorporates task-specific requirements by
leveraging the semantic reasonin... |
2502.04874 | The Role of Integrity Monitoring in Connected and Automated Vehicles:
Current State-of-Practice and Future Directions | cs.RO cs.SY eess.SY | Connected and Automated Vehicle (CAV) research has gained traction in the
last decade due to significant advancements in perception, navigation,
communication, and control functions. Accurate and reliable position
information is needed to meet the requirements of CAV applications, especially
when safety is concerned.... |
2502.04878 | Sparse Autoencoders Do Not Find Canonical Units of Analysis | cs.LG cs.AI | A common goal of mechanistic interpretability is to decompose the activations
of neural networks into features: interpretable properties of the input
computed by the model. Sparse autoencoders (SAEs) are a popular method for
finding these features in LLMs, and it has been postulated that they can be
used to find a \t... |
2502.04879 | Statistical Collusion by Collectives on Learning Platforms | stat.ML cs.LG | As platforms increasingly rely on learning algorithms, collectives may form
and seek ways to influence these platforms to align with their own interests.
This can be achieved by coordinated submission of altered data. To evaluate the
potential impact of such behavior, it is essential to understand the
computations th... |
2502.04882 | pytopicgram: A library for data extraction and topic modeling from
Telegram channels | cs.CL | Telegram is a popular platform for public communication, generating large
amounts of messages through its channels. pytopicgram is a Python library that
helps researchers collect, organize, and analyze these Telegram messages. The
library offers key features such as easy message retrieval, detailed channel
informatio... |
2502.04883 | Evaluating Standard and Dialectal Frisian ASR: Multilingual Fine-tuning
and Language Identification for Improved Low-resource Performance | cs.CL cs.LG cs.SD eess.AS | Automatic Speech Recognition (ASR) performance for low-resource languages is
still far behind that of higher-resource languages such as English, due to a
lack of sufficient labeled data. State-of-the-art methods deploy
self-supervised transfer learning where a model pre-trained on large amounts of
data is fine-tuned ... |
2502.04889 | Any-stepsize Gradient Descent for Separable Data under Fenchel--Young
Losses | stat.ML cs.LG | The gradient descent (GD) has been one of the most common optimizer in
machine learning. In particular, the loss landscape of a neural network is
typically sharpened during the initial phase of training, making the training
dynamics hover on the edge of stability. This is beyond our standard
understanding of GD conve... |
2502.04890 | Exploit Gradient Skewness to Circumvent Byzantine Defenses for Federated
Learning | cs.LG | Federated Learning (FL) is notorious for its vulnerability to Byzantine
attacks. Most current Byzantine defenses share a common inductive bias: among
all the gradients, the densely distributed ones are more likely to be honest.
However, such a bias is a poison to Byzantine robustness due to a newly
discovered phenome... |
2502.04891 | GNNs Getting ComFy: Community and Feature Similarity Guided Rewiring | cs.LG cs.SI stat.ML | Maximizing the spectral gap through graph rewiring has been proposed to
enhance the performance of message-passing graph neural networks (GNNs) by
addressing over-squashing. However, as we show, minimizing the spectral gap can
also improve generalization. To explain this, we analyze how rewiring can
benefit GNNs with... |
2502.04892 | A Foundational Brain Dynamics Model via Stochastic Optimal Control | cs.LG q-bio.NC stat.ML | We introduce a foundational model for brain dynamics that utilizes stochastic
optimal control (SOC) and amortized inference. Our method features a
continuous-discrete state space model (SSM) that can robustly handle the
intricate and noisy nature of fMRI signals. To address computational
limitations, we implement an ... |
2502.04895 | Deep Learning Models for Physical Layer Communications | cs.LG eess.SP | The increased availability of data and computing resources has enabled
researchers to successfully adopt machine learning (ML) techniques and make
significant contributions in several engineering areas. ML and in particular
deep learning (DL) algorithms have shown to perform better in tasks where a
physical bottom-up... |
2502.04896 | Goku: Flow Based Video Generative Foundation Models | cs.CV | This paper introduces Goku, a state-of-the-art family of joint
image-and-video generation models leveraging rectified flow Transformers to
achieve industry-leading performance. We detail the foundational elements
enabling high-quality visual generation, including the data curation pipeline,
model architecture design,... |
2502.04898 | ARTInp: CBCT-to-CT Image Inpainting and Image Translation in
Radiotherapy | eess.IV cs.AI cs.CV | A key step in Adaptive Radiation Therapy (ART) workflows is the evaluation of
the patient's anatomy at treatment time to ensure the accuracy of the delivery.
To this end, Cone Beam Computerized Tomography (CBCT) is widely used being
cost-effective and easy to integrate into the treatment process. Nonetheless,
CBCT im... |
2502.04899 | Unified Approaches in Self-Supervised Event Stream Modeling: Progress
and Prospects | cs.LG cs.AI | The proliferation of digital interactions across diverse domains, such as
healthcare, e-commerce, gaming, and finance, has resulted in the generation of
vast volumes of event stream (ES) data. ES data comprises continuous sequences
of timestamped events that encapsulate detailed contextual information relevant
to eac... |
2502.04901 | On the Difficulty of Constructing a Robust and Publicly-Detectable
Watermark | cs.CR cs.LG | This work investigates the theoretical boundaries of creating
publicly-detectable schemes to enable the provenance of watermarked imagery.
Metadata-based approaches like C2PA provide unforgeability and
public-detectability. ML techniques offer robust retrieval and watermarking.
However, no existing scheme combines ro... |
2502.04903 | Wavelet-Assisted Multi-Frequency Attention Network for Pansharpening | eess.IV cs.AI cs.CV | Pansharpening aims to combine a high-resolution panchromatic (PAN) image with
a low-resolution multispectral (LRMS) image to produce a high-resolution
multispectral (HRMS) image. Although pansharpening in the frequency domain
offers clear advantages, most existing methods either continue to operate
solely in the spat... |
2502.04907 | Scalable and consistent embedding of probability measures into Hilbert
spaces via measure quantization | stat.ML cs.LG | This paper is focused on statistical learning from data that come as
probability measures. In this setting, popular approaches consist in embedding
such data into a Hilbert space with either Linearized Optimal Transport or
Kernel Mean Embedding. However, the cost of computing such embeddings prohibits
their direct us... |
2502.04908 | Effective Sampling for Robot Motion Planning Through the Lens of
Lattices | cs.RO cs.CG cs.DM | Sampling-based methods for motion planning, which capture the structure of
the robot's free space via (typically random) sampling, have gained popularity
due to their scalability, simplicity, and for offering global guarantees, such
as probabilistic completeness and asymptotic optimality. Unfortunately, the
practical... |
2502.04910 | On the Power of Heuristics in Temporal Graphs | cs.LG | Dynamic graph datasets often exhibit strong temporal patterns, such as
recency, which prioritizes recent interactions, and popularity, which favors
frequently occurring nodes. We demonstrate that simple heuristics leveraging
only these patterns can perform on par or outperform state-of-the-art neural
network models u... |
2502.04912 | Joint Beamforming Design for Integrated Sensing and Communication
Systems with Hybrid-Colluding Eavesdroppers | eess.SY cs.SY | In this paper, we consider the physical layer security (PLS) problem for
integrated sensing and communication (ISAC) systems in the presence of
hybrid-colluding eavesdroppers, where an active eavesdropper (AE) and a passive
eavesdropper (PE) collude to intercept the confidential information. To ensure
the accuracy of... |
2502.04917 | Complex Physics-Informed Neural Network | cs.LG cs.AI | We propose compleX-PINN, a novel physics-informed neural network (PINN)
architecture that incorporates a learnable activation function inspired by
Cauchy integral theorem. By learning the parameters of the activation function,
compleX-PINN achieves high accuracy with just a single hidden layer. Empirical
results show... |
2502.04918 | Explainable and externally validated machine learning for
neuropsychiatric diagnosis via electrocardiograms | eess.SP cs.LG | Electrocardiogram (ECG) analysis has emerged as a promising tool for
identifying physiological changes associated with neuropsychiatric conditions.
The relationship between cardiovascular health and neuropsychiatric disorders
suggests that ECG abnormalities could serve as valuable biomarkers for more
efficient detect... |
2502.04923 | Cached Multi-Lora Composition for Multi-Concept Image Generation | cs.CV cs.AI | Low-Rank Adaptation (LoRA) has emerged as a widely adopted technique in
text-to-image models, enabling precise rendering of multiple distinct elements,
such as characters and styles, in multi-concept image generation. However,
current approaches face significant challenges when composing these LoRAs for
multi-concept... |
2502.04925 | Convergent NMPC-based Reinforcement Learning Using Deep Expected Sarsa
and Nonlinear Temporal Difference Learning | eess.SY cs.RO cs.SY | In this paper, we present a learning-based nonlinear model predictive
controller (NMPC) using an original reinforcement learning (RL) method to learn
the optimal weights of the NMPC scheme. The controller is used as the current
action-value function of a deep Expected Sarsa where the subsequent
action-value function,... |
2502.04928 | Generative-enhanced optimization for knapsack problems: an
industry-relevant study | cs.LG quant-ph | Optimization is a crucial task in various industries such as logistics,
aviation, manufacturing, chemical, pharmaceutical, and insurance, where finding
the best solution to a problem can result in significant cost savings and
increased efficiency. Tensor networks (TNs) have gained prominence in recent
years in modeli... |
2502.04935 | Conformal Prediction for Electricity Price Forecasting in the Day-Ahead
and Real-Time Balancing Market | cs.LG cs.AI | The integration of renewable energy into electricity markets poses
significant challenges to price stability and increases the complexity of
market operations. Accurate and reliable electricity price forecasting is
crucial for effective market participation, where price dynamics can be
significantly more challenging ... |
2502.04937 | Data-driven Modality Fusion: An AI-enabled Framework for Large-Scale
Sensor Network Management | cs.NI cs.AI cs.LG | The development and operation of smart cities relyheavily on large-scale
Internet-of-Things (IoT) networks and sensor infrastructures that continuously
monitor various aspects of urban environments. These networks generate vast
amounts of data, posing challenges related to bandwidth usage, energy
consumption, and sys... |
2502.04942 | WikiReddit: Tracing Information and Attention Flows Between Online
Platforms | cs.CY cs.DB cs.HC cs.SI | The World Wide Web is a complex interconnected digital ecosystem, where
information and attention flow between platforms and communities throughout the
globe. These interactions co-construct how we understand the world, reflecting
and shaping public discourse. Unfortunately, researchers often struggle to
understand h... |
2502.04946 | SurGen: 1020 H&E-stained Whole Slide Images With Survival and Genetic
Markers | cs.CV | $\textbf{Background}$: Cancer remains one of the leading causes of morbidity
and mortality worldwide. Comprehensive datasets that combine histopathological
images with genetic and survival data across various tumour sites are essential
for advancing computational pathology and personalised medicine.
$\textbf{Results}... |
2502.04949 | Does Unsupervised Domain Adaptation Improve the Robustness of Amortized
Bayesian Inference? A Systematic Evaluation | stat.ML cs.LG stat.ME | Neural networks are fragile when confronted with data that significantly
deviates from their training distribution. This is true in particular for
simulation-based inference methods, such as neural amortized Bayesian inference
(ABI), where models trained on simulated data are deployed on noisy real-world
observations... |
2502.04951 | The Rising Threat to Emerging AI-Powered Search Engines | cs.CR cs.AI cs.LG | Recent advancements in Large Language Models (LLMs) have significantly
enhanced the capabilities of AI-Powered Search Engines (AIPSEs), offering
precise and efficient responses by integrating external databases with
pre-existing knowledge. However, we observe that these AIPSEs raise risks such
as quoting malicious co... |
2502.04955 | Claim Extraction for Fact-Checking: Data, Models, and Automated Metrics | cs.CL | In this paper, we explore the problem of Claim Extraction using one-to-many
text generation methods, comparing LLMs, small summarization models finetuned
for the task, and a previous NER-centric baseline QACG. As the current
publications on Claim Extraction, Fact Extraction, Claim Generation and
Check-worthy Claim De... |
2502.04958 | SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model | cs.CL | Fine-tuning is a key approach for adapting language models to specific
downstream tasks, but updating all model parameters becomes impractical as
model sizes increase. Parameter-Efficient Fine-Tuning (PEFT) methods, such as
Low-Rank Adaptation (LoRA), address this challenge by introducing additional
adaptation parame... |
2502.04959 | No Task Left Behind: Isotropic Model Merging with Common and
Task-Specific Subspaces | cs.LG | Model merging integrates the weights of multiple task-specific models into a
single multi-task model. Despite recent interest in the problem, a significant
performance gap between the combined and single-task models remains. In this
paper, we investigate the key characteristics of task matrices -- weight update
matri... |
2502.04960 | Commonality and Individuality! Integrating Humor Commonality with
Speaker Individuality for Humor Recognition | cs.CL | Humor recognition aims to identify whether a specific speaker's text is
humorous. Current methods for humor recognition mainly suffer from two
limitations: (1) they solely focus on one aspect of humor commonalities,
ignoring the multifaceted nature of humor; and (2) they typically overlook the
critical role of speake... |
2502.04963 | Fast Adaptive Anti-Jamming Channel Access via Deep Q Learning and
Coarse-Grained Spectrum Prediction | cs.LG cs.AI | This paper investigates the anti-jamming channel access problem in complex
and unknown jamming environments, where the jammer could dynamically adjust its
strategies to target different channels. Traditional channel hopping
anti-jamming approaches using fixed patterns are ineffective against such
dynamic jamming atta... |
2502.04964 | CoCoA: A Generalized Approach to Uncertainty Quantification by
Integrating Confidence and Consistency of LLM Outputs | cs.CL | Uncertainty quantification (UQ) methods for Large Language Models (LLMs)
encompasses a variety of approaches, with two major types being particularly
prominent: information-based, which focus on model confidence expressed as
token probabilities, and consistency-based, which assess the semantic
relationship between mu... |
2502.04967 | Towards Smarter Sensing: 2D Clutter Mitigation in RL-Driven Cognitive
MIMO Radar | eess.SP cs.LG | Motivated by the growing interest in integrated sensing and communication for
6th generation (6G) networks, this paper presents a cognitive Multiple-Input
Multiple-Output (MIMO) radar system enhanced by reinforcement learning (RL) for
robust multitarget detection in dynamic environments. The system employs a
planar a... |
2502.04970 | Gradient-based Explanations for Deep Learning Survival Models | stat.ML cs.LG | Deep learning survival models often outperform classical methods in
time-to-event predictions, particularly in personalized medicine, but their
"black box" nature hinders broader adoption. We propose a framework for
gradient-based explanation methods tailored to survival neural networks,
extending their use beyond re... |
2502.04973 | DE-PADA: Personalized Augmentation and Domain Adaptation for ECG
Biometrics Across Physiological States | cs.LG | Electrocardiogram (ECG)-based biometrics offer a promising method for user
identification, combining intrinsic liveness detection with morphological
uniqueness. However, elevated heart rates introduce significant physiological
variability, posing challenges to pattern recognition systems and leading to a
notable perf... |
2502.04975 | Training-free Neural Architecture Search through Variance of Knowledge
of Deep Network Weights | cs.CV | Deep learning has revolutionized computer vision, but it achieved its
tremendous success using deep network architectures which are mostly
hand-crafted and therefore likely suboptimal. Neural Architecture Search (NAS)
aims to bridge this gap by following a well-defined optimization paradigm which
systematically looks... |
2502.04979 | Enhancing Pre-Trained Decision Transformers with Prompt-Tuning Bandits | cs.LG | Harnessing large offline datasets is vital for training foundation models
that can generalize across diverse tasks. Offline Reinforcement Learning (RL)
offers a powerful framework for these scenarios, enabling the derivation of
optimal policies even from suboptimal data. The Prompting Decision Transformer
(PDT) is an... |
2502.04981 | OccGS: Zero-shot 3D Occupancy Reconstruction with Semantic and
Geometric-Aware Gaussian Splatting | cs.CV | Obtaining semantic 3D occupancy from raw sensor data without manual
annotations remains an essential yet challenging task. While prior works have
approached this as a perception prediction problem, we formulate it as
scene-aware 3D occupancy reconstruction with geometry and semantics. In this
work, we propose OccGS, ... |
2502.04988 | CMamba: Learned Image Compression with State Space Models | eess.IV cs.CV | Learned Image Compression (LIC) has explored various architectures, such as
Convolutional Neural Networks (CNNs) and transformers, in modeling image
content distributions in order to achieve compression effectiveness. However,
achieving high rate-distortion performance while maintaining low computational
complexity (... |
2502.04991 | C2GM: Cascading Conditional Generation of Multi-scale Maps from Remote
Sensing Images Constrained by Geographic Features | eess.IV cs.CV | Multi-scale maps are essential representations of surveying and cartographic
results, serving as fundamental components of geographic services. Current
image generation networks can quickly produce map tiles from remote-sensing
images. However, generative models designed for natural images often focus on
texture feat... |
2502.04995 | A Variant of the Bravyi-Terhal Bound for Arbitrary Boundary Conditions | quant-ph cs.IT math.IT | We present a modified version of the Bravyi-Terhal bound that applies to
quantum codes defined by local parity-check constraints on a $D$-dimensional
lattice quotient. Specifically, we consider a quotient $\mathbb{Z}^D/\Lambda$
of $\mathbb{Z}^D$ of cardinality $n$, where $\Lambda$ is some $D$-dimensional
sublattice o... |
2502.04997 | Aligning Black-box Language Models with Human Judgments | cs.CL cs.AI cs.LG | Large language models (LLMs) are increasingly used as automated judges to
evaluate recommendation systems, search engines, and other subjective tasks,
where relying on human evaluators can be costly, time-consuming, and
unscalable. LLMs offer an efficient solution for continuous, automated
evaluation. However, since ... |
2502.04998 | On Sequential Fault-Intolerant Process Planning | cs.AI | We propose and study a planning problem we call Sequential Fault-Intolerant
Process Planning (SFIPP). SFIPP captures a reward structure common in many
sequential multi-stage decision problems where the planning is deemed
successful only if all stages succeed. Such reward structures are different
from classic additive... |
2502.05000 | Robust Graph Learning Against Adversarial Evasion Attacks via Prior-Free
Diffusion-Based Structure Purification | cs.LG cs.AI | Adversarial evasion attacks pose significant threats to graph learning, with
lines of studies that have improved the robustness of Graph Neural Networks
(GNNs). However, existing works rely on priors about clean graphs or attacking
strategies, which are often heuristic and inconsistent. To achieve robust graph
learni... |
2502.05001 | A New Paradigm in Tuning Learned Indexes: A Reinforcement Learning
Enhanced Approach | cs.DB cs.AI cs.SY eess.SY | Learned Index Structures (LIS) have significantly advanced data management by
leveraging machine learning models to optimize data indexing. However,
designing these structures often involves critical trade-offs, making it
challenging for both designers and end-users to find an optimal balance
tailored to specific wor... |
2502.05003 | QuEST: Stable Training of LLMs with 1-Bit Weights and Activations | cs.LG | One approach to reducing the massive costs of large language models (LLMs) is
the use of quantized or sparse representations for training or deployment.
While post-training compression methods are very popular, the question of
obtaining even more accurate compressed models by directly training over such
representatio... |
2502.05007 | Analyzing Advanced AI Systems Against Definitions of Life and
Consciousness | cs.AI | Could artificial intelligence ever become truly conscious in a functional
sense; this paper explores that open-ended question through the lens of Life, a
concept unifying classical biological criteria (Oxford, NASA, Koshland) with
empirical hallmarks such as adaptive self maintenance, emergent complexity, and
rudimen... |
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