Quality follows upgrading
All Articles
arXiv:2603.04730v1 Announce Type: new Abstract: Many modern biological assays, including RNA sequencing, yield integer-valued counts that reflect the number of molecules detected. These measurements are …
arXiv:2603.04731v1 Announce Type: new Abstract: Unlearnable Examples (UEs) serve as a data protection strategy that generates imperceptible perturbations to mislead models into learning spurious correlations …
arXiv:2603.04736v1 Announce Type: new Abstract: Learning a transport model that maps a source distribution to a target distribution is a canonical problem in machine learning, …
arXiv:2603.04755v1 Announce Type: new Abstract: Obstructive sleep apnea (OSA) is a sleep disorder that affects nearly one billion people globally and significantly elevates cardiovascular risk. …
arXiv:2603.04767v1 Announce Type: new Abstract: Conditional time series generation plays a critical role in addressing data scarcity and enabling causal analysis in real-world applications. Despite …
arXiv:2603.04768v1 Announce Type: new Abstract: Equalizer parameter optimization is critical for signal integrity in high-speed memory systems operating at multi-gigabit data rates. However, existing methods …
arXiv:2603.04780v1 Announce Type: new Abstract: Causal discovery with latent variables is a fundamental task. Yet most existing methods rely on strong structural assumptions, such as …
arXiv:2603.04790v1 Announce Type: new Abstract: Reinforcement learning (RL) has been extensively employed in a wide range of decision-making problems, such as games and robotics. Recently, …
arXiv:2603.04827v1 Announce Type: new Abstract: Algorithmic speedup of training common neural architectures is made difficult by the lack of structure guaranteed by the function compositions …
arXiv:2603.04831v1 Announce Type: new Abstract: Popular explanation methods often produce unreliable feature importance scores due to missingness bias, a systematic distortion that arises when models …
arXiv:2603.04851v1 Announce Type: new Abstract: Why is safety alignment in LLMs shallow? We prove that gradient-based alignment inherently concentrates on positions where harm is decided …
arXiv:2603.04881v1 Announce Type: new Abstract: Differentially private learning is essential for training models on sensitive data, but empirical studies consistently show that it can degrade …