arXiv:2603.24963v2 Announce Type: replace-cross
Abstract: Modern computational advertising platforms typically rely on recommendation systems to predict user responses, such as click-through rates, conversion rates, and other optimization events. To s…
arXiv:2603.27044v1 Announce Type: new
Abstract: Deep Reinforcement Learning (DRL) is widely recognized as sample-inefficient, a limitation attributable in part to the high dimensionality and substantial functional redundancy inherent to the policy par…
arXiv:2602.16898v4 Announce Type: replace-cross
Abstract: Task planning for robotic manipulation with large language models (LLMs) is an emerging area. Prior approaches rely on specialized models, fine tuning, or prompt tuning, and often operate in an…
arXiv:2603.26994v1 Announce Type: new
Abstract: T cells are a critical component of the adaptive immune system, playing a role in infectious disease, autoimmunity, and cancer. T cell function is mediated by the T cell receptor (TCR) protein, a highly …
arXiv:2511.22442v2 Announce Type: replace-cross
Abstract: Ranking methods or models based on their performance is of prime importance but is tricky because performance is fundamentally multidimensional. In the case of classification, precision and rec…
arXiv:2603.26975v1 Announce Type: new
Abstract: This study presents a probabilistic surrogate model for localized wildfire spread based on a conditional flow matching algorithm. The approach models fire progression as a stochastic process by learning …
arXiv:2509.25311v2 Announce Type: replace-cross
Abstract: We implement physics-informed-neural-networks (PINNs) to compute holographic entanglement entropy and entanglement wedge cross section. This technique allows us to compute these quantities for …
arXiv:2603.26954v1 Announce Type: new
Abstract: The trend towards larger training setups has brought a renewed interest in partially asynchronous two-phase optimizers which optimize locally and then synchronize across workers. Additionally, recent wor…
arXiv:2501.00277v2 Announce Type: replace-cross
Abstract: Modern AI algorithms require labeled data. In real world, majority of data are unlabeled. Labeling the data are costly. this is particularly true for some areas requiring special skills, such a…
arXiv:2603.26931v1 Announce Type: new
Abstract: Machine learning models often struggle to generalize across domains with varying data distributions, such as differing noise levels, leading to degraded performance. Traditional strategies like personali…