Guodong Zhang
Guodong Zhang
Ph.D. student, University of Toronto
Verified email at cs.toronto.edu - Homepage
Title
Cited by
Cited by
Year
Deformable convolutional networks
J Dai, H Qi, Y Xiong, Y Li, G Zhang, H Hu, Y Wei
International Conference on Computer Vision, 2017
22092017
Benchmarking Model-Based Reinforcement Learning
T Wang, X Bao, I Clavera, J Hoang, Y Wen, E Langlois, S Zhang, G Zhang, ...
160*2019
Noisy Natural Gradient as Variational Inference
G Zhang, S Sun, D Duvenaud, R Grosse
International Conference on Machine Learning, 2018
1352018
Functional Variational Bayesian Neural Networks
S Sun, G Zhang, J Shi, R Grosse
International Conference on Learning Representations, 2019
1262019
Picking Winning Tickets Before Training by Preserving Gradient Flow
C Wang, G Zhang, R Grosse
International Conference on Learning Representations, 2020
1132020
Three Mechanisms of Weight Decay Regularization
G Zhang, C Wang, B Xu, R Grosse
International Conference on Learning Representations, 2019
1022019
Differentiable Compositional Kernel Learning for Gaussian Processes
S Sun, G Zhang, C Wang, W Zeng, J Li, R Grosse
International Conference on Machine Learning, 2018
532018
On Solving Minimax Optimization Locally: A Follow-the-Ridge Approach
Y Wang, G Zhang, J Ba
International Conference on Learning Representations, 2020
482020
Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model
G Zhang, L Li, Z Nado, J Martens, S Sachdeva, GE Dahl, CJ Shallue, ...
Advances in Neural Information Processing Systems, 2019
442019
Fast Convergence of Natural Gradient Descent for Overparameterized Neural Networks
G Zhang, J Martens, R Grosse
Advances in Neural Information Processing Systems, 2019
442019
EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis
C Wang, R Grosse, S Fidler, G Zhang
International Conference on Machine Learning, 2019
352019
Eigenvalue Corrected Noisy Natural Gradient
J Bae, G Zhang, R Grosse
Neural Information Processing Systems (Bayesian Deep Learning Workshop), 2018
172018
An Empirical Study of Stochastic Gradient Descent with Structured Covariance Noise
Y Wen, K Luk, M Gazeau, G Zhang, H Chan, J Ba
International Conference on Artificial Intelligence and Statistics, 3621-3631, 2020
14*2020
A Unified Analysis of First-Order Methods for Smooth Games via Integral Quadratic Constraints
G Zhang, X Bao, L Lessard, R Grosse
Journal of Machine Learning Research, 2021
52021
On the suboptimality of negative momentum for minimax optimization
G Zhang, Y Wang
International Conference on Artificial Intelligence and Statistics, 2020
42020
Nonnegative matrix cofactorization for weakly supervised image parsing
G Zhang, X Gong
IEEE Signal Processing Letters 23 (11), 1682-1686, 2016
32016
Don't Fix What ain't Broke: Near-optimal Local Convergence of Alternating Gradient Descent-Ascent for Minimax Optimization
G Zhang, Y Wang, L Lessard, R Grosse
arXiv preprint arXiv:2102.09468, 2021
12021
Learning to Give Checkable Answers with Prover-Verifier Games
C Anil, G Zhang, Y Wu, R Grosse
arXiv preprint arXiv:2108.12099, 2021
2021
Differentiable Annealed Importance Sampling and the Perils of Gradient Noise
G Zhang, K Hsu, J Li, C Finn, R Grosse
arXiv preprint arXiv:2107.10211, 2021
2021
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Articles 1–19