activity
20172020
most citedEigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis

30 citations · 43 across the 2 of their papers we have counts for

collaborators

5 papers

cs.LG2020

Picking Winning Tickets Before Training by Preserving Gradient Flow

Chaoqi Wang, Guodong Zhang, Roger Grosse

Overparameterization has been shown to benefit both the optimization and generalization of neural networks, but large networks are resource hungry at both training and test time. N…

cs.LG201930 cited

EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis

Chaoqi Wang, Roger Grosse, Sanja Fidler +1

Reducing the test time resource requirements of a neural network while preserving test accuracy is crucial for running inference on resource-constrained devices. To achieve this go…

cs.LG2018

Three Mechanisms of Weight Decay Regularization

Guodong Zhang, Chaoqi Wang, Bowen Xu +1

Weight decay is one of the standard tricks in the neural network toolbox, but the reasons for its regularization effect are poorly understood, and recent results have cast doubt on…

cs.LG2018

Differentiable Compositional Kernel Learning for Gaussian Processes

Shengyang Sun, Guodong Zhang, Chaoqi Wang +3

The generalization properties of Gaussian processes depend heavily on the choice of kernel, and this choice remains a dark art. We present the Neural Kernel Network (NKN), a flexib…

cs.CV201713 cited

A Revisit on Deep Hashings for Large-scale Content Based Image Retrieval

Deng Cai, Xiuye Gu, Chaoqi Wang

There is a growing trend in studying deep hashing methods for content-based image retrieval (CBIR), where hash functions and binary codes are learnt using deep convolutional neural…