1 citations · 1 across the 11 of their papers we have counts for
12 papers
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…
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…
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…
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…
Optimal Brain Apoptosis
Mingyuan Sun, Zheng Fang, Jiaxu Wang +5
The increasing complexity and parameter count of Convolutional Neural Networks (CNNs) and Transformers pose challenges in terms of computational efficiency and resource demands. Pr…
Adaptive Calibration: A Unified Conversion Framework of Spiking Neural Network
Ziqing Wang, Yuetong Fang, Jiahang Cao +2
Spiking Neural Networks (SNNs) are seen as an energy-efficient alternative to traditional Artificial Neural Networks (ANNs), but the performance gap remains a challenge. While this…
Sharpening Your Density Fields: Spiking Neuron Aided Fast Geometry Learning
Yi Gu, Zhaorui Wang, Dongjun Ye +1
Neural Radiance Fields (NeRF) have achieved remarkable progress in neural rendering. Extracting geometry from NeRF typically relies on the Marching Cubes algorithm, which uses a ha…
Reuse and Blend: Energy-Efficient Optical Neural Network Enabled by Weight Sharing
Bo Xu, Yuetong Fang, Shaoliang Yu +1
Optical neural networks (ONN) based on micro-ring resonators (MRR) have emerged as a promising alternative to significantly accelerating the massive matrix-vector multiplication (M…
Optical Spiking Neurons Enable High-Speed and Energy-Efficient Optical Neural Networks
Bo Xu, Zefeng Huang, Yuetong Fang +5
Optical neural networks (ONNs) perform extensive computations using photons instead of electrons, resulting in passively energy-efficient and low-latency computing. Among various O…
Adaptive Calibration: A Unified Conversion Framework of Spiking Neural Networks
Ziqing Wang, Yuetong Fang, Jiahang Cao +1
Spiking Neural Networks (SNNs) have emerged as a promising energy-efficient alternative to traditional Artificial Neural Networks (ANNs). Despite this, bridging the performance gap…