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arXiv:2601.21279v1 Announce Type: new Abstract: Spiking Neural Networks (SNNs) promise energy-efficient computing through event-driven sparsity, yet all existing approaches sacrifice accuracy by approximating continuous values...
arXiv:2601.21280v1 Announce Type: new Abstract: Accurate antenna affiliation identification is crucial for optimizing and maintaining communication networks. Current practice, however, relies on the cumbersome and...
arXiv:2601.21281v1 Announce Type: new Abstract: Neural combinatorial optimization (NCO) solvers, implemented with graph neural networks (GNNs), have introduced new approaches for solving routing problems. Trained...
arXiv:2601.21282v1 Announce Type: new Abstract: Recent advances in generative foundational models, often termed "world models," have propelled interest in applying them to critical tasks like...
arXiv:2601.21283v1 Announce Type: new Abstract: LLM unlearning is a technique to remove the impacts of undesirable knowledge from the model without retraining from scratch, which...
arXiv:2601.21284v1 Announce Type: new Abstract: Diffusion models have emerged as powerful generative tools for modeling complex data distributions, yet their purely data-driven nature limits applicability...
arXiv:2601.21285v1 Announce Type: new Abstract: Accurately capturing feature interactions is essential in recommender systems, and recent trends show that scaling up model capacity could be...
arXiv:2601.21286v1 Announce Type: new Abstract: In primary-backup replication, consensus latency is bounded by the time for backup nodes to replay (re-execute) transactions proposed by the...
arXiv:2601.21287v1 Announce Type: new Abstract: Privacy-preserving deep learning addresses privacy concerns in Machine Learning as a Service (MLaaS) by using Homomorphic Encryption (HE) for linear...
arXiv:2601.21288v1 Announce Type: new Abstract: Autonomous driving is an important and safety-critical task, and recent advances in LLMs/VLMs have opened new possibilities for reasoning and...
arXiv:2601.21289v1 Announce Type: new Abstract: Identifying the extent to which every temporal segment influences a model's predictions is essential for explaining model decisions and increasing...
arXiv:2601.21291v1 Announce Type: new Abstract: Depth completion aims to predict a dense depth map from a color image with sparse depth measurements. Although deep learning...
arXiv:2601.21293v1 Announce Type: new Abstract: Reliability-centered prognostics for rotating machinery requires early warning signals that remain accurate under nonstationary operating conditions, domain shifts across speed/load/sensors,...
arXiv:2601.21294v1 Announce Type: new Abstract: Partial Least Squares (PLS) learns shared structure from paired data via the top singular vectors of the empirical cross-covariance (PLS-SVD),...
arXiv:2601.21296v1 Announce Type: new Abstract: Dataset Distillation (DD) seeks to create a compact dataset from a large, real-world dataset. While recent methods often rely on...
arXiv:2601.21297v1 Announce Type: new Abstract: We introduce Deep QP Safety Filter, a fully data-driven safety layer for black-box dynamical systems. Our method learns a Quadratic-Program...
arXiv:2601.21298v1 Announce Type: new Abstract: Code commits in a version control system (e.g., Git) should be atomic, i.e., focused on a single goal, such as...
arXiv:2601.21299v1 Announce Type: new Abstract: Complex networks are powerful representations of complex systems across scales and domains, and the field is experiencing unprecedented growth in...
arXiv:2601.21300v1 Announce Type: new Abstract: In this paper, we address the problem of circumnavigation of a stationary target by a heterogeneous group comprising of $\textbf{n}$...
arXiv:2601.21301v1 Announce Type: new Abstract: We present the convergence rates of synchronous and asynchronous Q-learning for average-reward Markov decision processes, where the absence of contraction...
arXiv:2601.21305v1 Announce Type: new Abstract: The rapid advance of Generative AI into software development prompts this empirical investigation of perceptual effects on practice. We study...
arXiv:2601.21306v1 Announce Type: new Abstract: This paper investigates search in model-based reinforcement learning (RL). Conventional wisdom holds that long-term predictions and compounding errors are the...
arXiv:2601.21307v1 Announce Type: new Abstract: The rapid growth of the global population, alongside exponential technological advancement, has intensified the demand for food production. Meeting this...
arXiv:2601.21309v1 Announce Type: new Abstract: The increasing scale of graph datasets has significantly improved the performance of graph representation learning methods, but it has also...