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cs.LG2026
Riemannian Attention Mechanisms for Transformers: A Theoretical Framework and Architecture Design
Sen Song
All Transformer-based large language models compute attention via the Euclidean inner product, an architectural choice that Dong et al. (2021) proved causes representational rank t…
cs.LG2026
Rethinking EEG-Based Disease Diagnosis: Decoupling Instance Representation Learning from Subject-Level Supervision
Zhiyuan Ma, Zeyuan Li, Zhiyi Lu +7
EEG-based disease diagnosis requires one prediction per subject, yet common pipelines segment recordings into short instances, inherit the subject label for every instance, and tra…
cs.LG2026
Signal-Adaptive Trust Regions for Gradient-Free Optimization of Recurrent Spiking Neural Networks
Jinhao Li, Yuhao Sun, Zhiyuan Ma +5
Recurrent spiking neural networks (RSNNs) are a promising substrate for energy-efficient control policies, but training them for high-dimensional, long-horizon reinforcement learni…