9 citations · 18 across the 4 of their papers we have counts for
5 papers
Bypass Exponential Time Preprocessing: Fast Neural Network Training via Weight-Data Correlation Preprocessing
Josh Alman, Jiehao Liang, Zhao Song +2
Over the last decade, deep neural networks have transformed our society, and they are already widely applied in various machine learning applications. State-of-art deep neural netw…
Scatterbrain: Unifying Sparse and Low-rank Attention Approximation
Beidi Chen, Tri Dao, Eric Winsor +3
Recent advances in efficient Transformers have exploited either the sparsity or low-rank properties of attention matrices to reduce the computational and memory bottlenecks of mode…
Does Preprocessing Help Training Over-parameterized Neural Networks?
Zhao Song, Shuo Yang, Ruizhe Zhang
Deep neural networks have achieved impressive performance in many areas. Designing a fast and provable method for training neural networks is a fundamental question in machine lear…
Fast Sketching of Polynomial Kernels of Polynomial Degree
Zhao Song, David P. Woodruff, Zheng Yu +1
Kernel methods are fundamental in machine learning, and faster algorithms for kernel approximation provide direct speedups for many core tasks in machine learning. The polynomial k…
Metric Transforms and Low Rank Matrices via Representation Theory of the Real Hyperrectangle
Josh Alman, Timothy Chu, Gary Miller +3
In this paper, we develop a new technique which we call representation theory of the real hyperrectangle, which describes how to compute the eigenvectors and eigenvalues of certain…