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arXiv:2603.22320v1 Announce Type: new Abstract: While climate models provide insights for climate decision-making, their use is constrained by significant computational and technical demands. Although machine …
arXiv:2603.22322v1 Announce Type: new Abstract: Machine learning systems deployed in medical devices require governance frameworks that ensure safety while enabling continuous improvement. Regulatory bodies including …
arXiv:2603.22323v1 Announce Type: new Abstract: Accurately predicting the state-of-health (SOH) and remaining useful life (RUL) of lithium-ion batteries is crucial for ensuring the safe and …
arXiv:2603.22324v1 Announce Type: new Abstract: We introduce Delta-Aware Quantization (DAQ), a data-free post-training quantization framework that preserves the knowledge acquired during post-training. Standard quantization objectives …
arXiv:2603.22325v1 Announce Type: new Abstract: Recurrent neural networks (RNNs) and self-attention are both widely used sequence-mixing layers that maintain an internal memory. However, this memory …
arXiv:2603.22326v1 Announce Type: new Abstract: Decision support systems are essential for maintaining grid stability in low-carbon power systems, such as wind power plants, by providing …
arXiv:2603.22328v1 Announce Type: new Abstract: Despite the strong predictive performance achieved by machine learning models across many application domains, assessing their trustworthiness through reliable estimates …
arXiv:2603.22329v1 Announce Type: new Abstract: Decoder-only language models are stateless: hidden representations are discarded after every forward pass and nothing persists across sessions. Jeong (2026a) …
arXiv:2603.22331v1 Announce Type: new Abstract: Every wildfire prediction model deployed today shares a dangerous property: none of these methods provides formal guarantees on how much …
arXiv:2603.22332v1 Announce Type: new Abstract: Data imputation is a cornerstone technique for handling missing values in real-world datasets, which are often plagued by missingness. Despite …
arXiv:2603.22333v1 Announce Type: new Abstract: State-space models (SSMs) offer efficient alternatives to attention with linear-time recurrence. Mamba2, a recent SSM-based language model, uses selective input …
arXiv:2603.22339v1 Announce Type: new Abstract: Chinchilla Approach 2 is among the most widely used methods for fitting neural scaling laws. Its parabolic approximation introduces systematic …
