6 papers
Exact Causal Attention with 10% Fewer Operations
Dmitry Rybin, Yushun Zhang, Ding Tian +2
We present Exact Causal Attention (ECA), a Strassen-style algorithm that computes exact Causal Attention using 10\% fewer operations. ECA improves a special class of matrix multipl…
Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective
Senmiao Wang, Yupeng Chen, Yushun Zhang +2
Graph Neural Networks (GNNs) often suffer from performance degradation as the network depth increases. This paper addresses this issue by introducing initialization methods that en…
Can Be Faster
Dmitry Rybin, Yushun Zhang, Zhi-Quan Luo
We present RXTX, a new algorithm for computing the product of matrix by its transpose for . RXTX uses fewer multiplications and fe…
Towards Quantifying the Hessian Structure of Neural Networks
Zhaorui Dong, Yushun Zhang, Jianfeng Yao +1
Empirical studies reported that the Hessian matrix of neural networks (NNs) exhibits a near-block-diagonal structure, yet its theoretical foundation remains unclear. In this work,…
Finite Horizon Optimization: Framework and Applications
Yushun Zhang, Dmitry Rybin, Zhi-Quan Luo
In modern engineering scenarios, there is often a strict upper bound on the number of algorithm iterations that can be performed within a given time limit. This raises the question…
When Expressivity Meets Trainability: Fewer than Neurons Can Work
Jiawei Zhang, Yushun Zhang, Mingyi Hong +2
Modern neural networks are often quite wide, causing large memory and computation costs. It is thus of great interest to train a narrower network. However, training narrow neural n…