6 papers
Winner-Take-All Spiking Transformer for Language Modeling
Chenlin Zhou, Sihang Guo, Jiaqi Wang +6
Spiking Transformers, which combine the scalability of Transformers with the sparse, energy-efficient property of Spiking Neural Networks (SNNs), have achieved impressive results i…
BrainFuse: a unified infrastructure integrating realistic biological modeling and core AI methodology
Baiyu Chen, Yujie Wu, Siyuan Xu +9
Neuroscience and artificial intelligence represent distinct yet complementary pathways to general intelligence. However, amid the ongoing boom in AI research and applications, the…
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…
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…
Is Smoothness the Key to Robustness? A Comparison of Attention and Convolution Models Using a Novel Metric
Baiyuan Chen
Robustness is a critical aspect of machine learning models. Existing robustness evaluation approaches often lack theoretical generality or rely heavily on empirical assessments, li…
Exploiting Topological Priors for Boosting Point Cloud Generation
Baiyuan Chen
This paper presents an innovative enhancement to the Sphere as Prior Generative Adversarial Network (SP-GAN) model, a state-of-the-art GAN designed for point cloud generation. A no…