Academic
arXiv:2602.18728v1 Announce Type: new Abstract: Unsupervised multi-view clustering (MVC) aims to partition data into meaningful groups by leveraging complementary information from multiple views without labels, …
arXiv:2602.18733v1 Announce Type: new Abstract: Training data leakage from Large Language Models (LLMs) raises serious concerns related to privacy, security, and copyright compliance. A central …
arXiv:2602.18739v1 Announce Type: new Abstract: Generative world models (WMs) are increasingly used to synthesize controllable, sensor-conditioned driving videos, yet their reliance on physical priors exposes …
arXiv:2602.18740v1 Announce Type: new Abstract: This study presents a hierarchical, network-level traffic flow control framework for mixed traffic consisting of Human-driven Vehicles (HVs), Connected and …
arXiv:2602.18744v1 Announce Type: new Abstract: Radio maps are essential for efficient radio resource management in future 6G and low-altitude networks. While deep learning (DL) techniques …
arXiv:2602.18769v1 Announce Type: new Abstract: Understanding disease-gene associations is essential for unravelling disease mechanisms and advancing diagnostics and therapeutics. Traditional approaches based on manual curation …
arXiv:2602.18786v1 Announce Type: new Abstract: Ad ranking systems must simultaneously optimize multiple objectives including click-through rate (CTR), conversion rate (CVR), revenue, and user experience metrics. …
arXiv:2602.18793v1 Announce Type: new Abstract: Graph anomaly detection (GAD) is critical for identifying abnormal nodes in graph-structured data from diverse domains, including cybersecurity and social …
arXiv:2602.18795v1 Announce Type: new Abstract: Latent Dirichlet Allocation (LDA) is a foundational model for discovering latent thematic structure in discrete data, but its Dirichlet prior …
arXiv:2602.18801v1 Announce Type: new Abstract: Neural operators provide fast PDE surrogates and often generalize across parameters and resolutions. However, in the short train long test …
arXiv:2602.18825v1 Announce Type: new Abstract: Bayesian neural networks (BNNs) are a useful tool for uncertainty quantification, but require substantially more computational resources than conventional neural …
arXiv:2602.18837v1 Announce Type: new Abstract: Despite their theoretical advantages, spectral methods based on the graph Fourier transform (GFT) are seldom used in graph neural networks …
