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arXiv:2603.08859v1 Announce Type: new Abstract: Hybrid sequence models--combining Transformer and state-space model layers--seek to gain the expressive versatility of attention as well as the computational …
arXiv:2603.08900v1 Announce Type: new Abstract: Considering the high volume, wide variety, and rapid speed of data generation, investigating feature selection methods for big data presents …
arXiv:2603.08907v1 Announce Type: new Abstract: We present a comprehensive ablation of nine finite-sample bound families for selective prediction with risk control, combining concentration inequalities (Hoeffding, …
arXiv:2603.08913v1 Announce Type: new Abstract: Genomic language models (GLMs) have emerged as powerful tools for learning representations of DNA sequences, enabling advances in variant prediction, …
arXiv:2603.08914v1 Announce Type: new Abstract: Over-parameterized neural networks incur prohibitive memory and computational costs for resource-constrained deployment. The Strong Lottery Ticket (SLT) hypothesis suggests that …
arXiv:2603.08960v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) models deliver high quality at low training FLOPs, but this efficiency often vanishes at inference. We identify a …
arXiv:2603.08965v1 Announce Type: new Abstract: AI memory systems increasingly organize knowledge into graph structures -- knowledge graphs, entity relations, community hierarchies -- yet lack a …
arXiv:2603.08972v1 Announce Type: new Abstract: Internet of Things (IoT) Analytics often involves applying machine learning (ML) models on data streams. In such scenarios, traditional ML …
arXiv:2603.08987v1 Announce Type: new Abstract: Recent advances in medical large language models have explored Test-Time Reinforcement Learning (TTRL) to enhance reasoning. However, standard TTRL often …
arXiv:2603.09014v1 Announce Type: new Abstract: Flow models have rapidly become the go-to method for training and deploying large-scale generators, owing their success to inference-time flexibility …
arXiv:2603.09016v1 Announce Type: new Abstract: Flatness measures based on the spectrum or the trace of the Hessian of the loss are widely used as proxies …
arXiv:2603.09024v1 Announce Type: new Abstract: Sudden concept drift makes previously trained predictors unreliable, yet deciding when to retrain and what post-drift data size is sufficient …