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arXiv:2602.20527v1 Announce Type: new Abstract: Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) have advanced rapidly in recent years and have been successfully applied to …
arXiv:2602.20530v1 Announce Type: new Abstract: Emotion recognition from multi-modal physiological and behavioral signals plays a pivotal role in affective computing, yet most existing models remain …
arXiv:2602.20549v1 Announce Type: new Abstract: The choice of prior is central to solving ill-posed imaging inverse problems, making it essential to select one consistent with …
arXiv:2602.20557v1 Announce Type: new Abstract: Symbolic Regression (SR) tries to reveal the hidden equations behind observed data. However, most methods search within a discrete equation …
arXiv:2602.20567v1 Announce Type: new Abstract: Push-Sum-based decentralized learning enables optimization over directed communication networks, where information exchange may be asymmetric. While convergence properties of such …
arXiv:2602.20573v1 Announce Type: new Abstract: Molecules are commonly represented as SMILES strings, which can be readily converted to fixed-size molecular fingerprints. These fingerprints serve as …
arXiv:2602.20578v1 Announce Type: new Abstract: We study online maximization of non-monotone Diminishing-Return(DR)-submodular functions over down-closed convex sets, a regime where existing projection-free online methods suffer …
arXiv:2602.19049v1 Announce Type: new Abstract: Large language models increasingly rely on long chains of thought to improve accuracy, yet such gains come with substantial inference-time …
arXiv:2602.19058v1 Announce Type: new Abstract: Large vision-language models (LVLMs) have rapidly advanced across various domains, yet they still lag behind strong text-only large language models …
arXiv:2602.19079v1 Announce Type: new Abstract: Topic modeling extracts latent themes from large text collections, but leading approaches like BERTopic face critical limitations: stochastic instability, loss …
arXiv:2602.19101v1 Announce Type: new Abstract: Value alignment of Large Language Models (LLMs) requires us to empirically measure these models' actual, acquired representation of value. Among …