9 citations · 49 across the 15 of their papers we have counts for
5 papers · 1 filter
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…
Sketching for First Order Method: Efficient Algorithm for Low-Bandwidth Channel and Vulnerability
Zhao Song, Yitan Wang, Zheng Yu +1
Sketching is one of the most fundamental tools in large-scale machine learning. It enables runtime and memory saving via randomly compressing the original large problem into lower…
A Sublinear Adversarial Training Algorithm
Yeqi Gao, Lianke Qin, Zhao Song +1
Adversarial training is a widely used strategy for making neural networks resistant to adversarial perturbations. For a neural network of width , input training data in …
A Faster Quantum Algorithm for Semidefinite Programming via Robust IPM Framework
Baihe Huang, Shunhua Jiang, Zhao Song +2
This paper studies a fundamental problem in convex optimization, which is to solve semidefinite programming (SDP) with high accuracy. This paper follows from the existing robust SD…
Bounding the Width of Neural Networks via Coupled Initialization -- A Worst Case Analysis
Alexander Munteanu, Simon Omlor, Zhao Song +1
A common method in training neural networks is to initialize all the weights to be independent Gaussian vectors. We observe that by instead initializing the weights into independen…