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cs.NE2025
Temporal-adaptive Weight Quantization for Spiking Neural Networks
Han Zhang, Qingyan Meng, Jiaqi Wang +3
Weight quantization in spiking neural networks (SNNs) could further reduce energy consumption. However, quantizing weights without sacrificing accuracy remains challenging. In this…
stat.ML2025
Optimal Dynamic Regret by Transformers for Non-Stationary Reinforcement Learning
Baiyuan Chen, Shinji Ito, Masaaki Imaizumi
Transformers have demonstrated exceptional performance across a wide range of domains. While their ability to perform reinforcement learning in-context has been established both th…