10 papers
Adaptive Scaling of Policy Constraints for Offline Reinforcement Learning
Tan Jing, Xiaorui Li, Chao Yao +4
Offline reinforcement learning (RL) enables learning effective policies from fixed datasets without any environment interaction. Existing methods typically employ policy constraint…
Spiking Neural Networks Need High Frequency Information
Yuetong Fang, Deming Zhou, Ziqing Wang +5
Spiking Neural Networks promise brain-inspired and energy-efficient computation by transmitting information through binary (0/1) spikes. Yet, their performance still lags behind th…
TDSNNs: Competitive Topographic Deep Spiking Neural Networks for Visual Cortex Modeling
Deming Zhou, Yuetong Fang, Zhaorui Wang +1
The primate visual cortex exhibits topographic organization, where functionally similar neurons are spatially clustered, a structure widely believed to enhance neural processing ef…
TsetlinKWS: A 65nm 16.58uW, 0.63mm2 State-Driven Convolutional Tsetlin Machine-Based Accelerator For Keyword Spotting
Baizhou Lin, Yuetong Fang, Renjing Xu +2
The Tsetlin Machine (TM) has recently attracted attention as a low-power alternative to neural networks due to its simple and interpretable inference mechanisms. However, its perfo…
FARM: Frame-Accelerated Augmentation and Residual Mixture-of-Experts for Physics-Based High-Dynamic Humanoid Control
Tan Jing, Shiting Chen, Yangfan Li +2
Unified physics-based humanoid controllers are pivotal for robotics and character animation, yet models that excel on gentle, everyday motions still stumble on explosive actions, h…
Hierarchical Multi-Label Contrastive Learning for Protein-Protein Interaction Prediction Across Organisms
Shiyi Liu, Buwen Liang, Yuetong Fang +2
Recent advances in AI for science have highlighted the power of contrastive learning in bridging heterogeneous biological data modalities. Building on this paradigm, we propose HIP…