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arXiv:2603.18326v1 Announce Type: new Abstract: While offline reinforcement learning provides reliable policies for real-world deployment, its inherent pessimism severely restricts an agent's ability to explore …
arXiv:2603.18328v1 Announce Type: new Abstract: Physics-Informed Neural Networks(PINNs) are a powerful and flexible learning framework that has gained significant attention in recent years. It has …
arXiv:2603.18348v1 Announce Type: new Abstract: Generative models, particularly Generative Adversarial Networks (GANs), often suffer from a lack of output diversity, frequently generating similar samples rather …
arXiv:2603.18387v1 Announce Type: new Abstract: This draft book offers a comprehensive and rigorous treatment of the mathematical principles underlying modern deep learning. The book spans …
arXiv:2603.18396v1 Announce Type: new Abstract: Bus holding control is challenging due to stochastic traffic and passenger demand. While deep reinforcement learning (DRL) shows promise, standard …
arXiv:2603.18397v1 Announce Type: new Abstract: Mass spectrometry (MS) stands as a cornerstone analytical technique for molecular identification, yet de novo structure elucidation from spectra remains …
arXiv:2603.18417v1 Announce Type: new Abstract: Sparse attention mechanisms promise to break the quadratic bottleneck of long-context transformers, yet production adoption remains limited by a critical …
arXiv:2603.18431v1 Announce Type: new Abstract: Quantum multi-armed bandits (MAB) and stochastic linear bandits (SLB) have recently attracted significant attention, as their quantum counterparts can achieve …
arXiv:2603.18432v1 Announce Type: new Abstract: Separating multiple effects in time series is fundamental yet challenging for time-series forecasting (TSF). However, existing TSF models cannot effectively …
arXiv:2603.18444v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has emerged as an effective post-training paradigm for improving the reasoning capabilities of large …
arXiv:2603.18448v1 Announce Type: new Abstract: Shot language understanding (SLU) is crucial for cinematic analysis but remains challenging due to its diverse cinematographic dimensions and subjective …
arXiv:2603.18464v1 Announce Type: new Abstract: Reinforcement learning (RL) for large-scale Vision-Language-Action (VLA) models faces significant challenges in computational efficiency and data acquisition. We propose AcceRL, …
