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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 …
arXiv:2602.18849v1 Announce Type: new Abstract: Despite powering modern AI, transformers remain mysteriously brittle to train. We develop a stability theory that explains why pre-LayerNorm works, …
arXiv:2602.18851v1 Announce Type: new Abstract: Attention scores in transformers are bilinear forms $S_{ij} = x_i^\top M x_j / \sqrt{d_h}$ whose maximum magnitude governs overflow risk …
arXiv:2602.18856v1 Announce Type: new Abstract: Reinforcement learning (RL) has enabled major advances in fields such as robotics and natural language processing. A key challenge in …
arXiv:2602.18857v1 Announce Type: new Abstract: Optimally trading-off exploration and exploitation is the holy grail of reinforcement learning as it promises maximal data-efficiency for solving any …
arXiv:2602.18858v1 Announce Type: new Abstract: Hyperbolic spaces provide a natural geometry for representing hierarchical and tree-structured data due to their exponential volume growth. To leverage …
