Guodong Zhang
Guodong Zhang
Ph.D. student, University of Toronto
在 cs.toronto.edu 的电子邮件经过验证 - 首页
标题引用次数年份
Deformable convolutional networks
J Dai, H Qi, Y Xiong, Y Li, G Zhang, H Hu, Y Wei
International Conference on Computer Vision, 2017
6332017
Noisy Natural Gradient as Variational Inference
G Zhang, S Sun, D Duvenaud, R Grosse
International Conference on Machine Learning, 2018
322018
Three Mechanisms of Weight Decay Regularization
G Zhang, C Wang, B Xu, R Grosse
International Conference on Learning Representations, 2019
172019
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
172018
Functional Variational Bayesian Neural Networks
S Sun, G Zhang, J Shi, R Grosse
International Conference on Learning Representations, 2019
132019
Benchmarking Model-Based Reinforcement Learning
T Wang, X Bao, I Clavera, J Hoang, Y Wen, E Langlois, S Zhang, G Zhang, ...
6*2019
Interplay between optimization and generalization of stochastic gradient descent with covariance noise
Y Wen, K Luk, M Gazeau, G Zhang, H Chan, J Ba
arXiv preprint arXiv:1902.08234, 2019
42019
Eigenvalue Corrected Noisy Natural Gradient
J Bae, G Zhang, R Grosse
Neural Information Processing Systems (Bayesian Deep Learning Workshop), 2018
42018
Fast Convergence of Natural Gradient Descent for Overparameterized Neural Networks
G Zhang, J Martens, R Grosse
Advances in Neural Information Processing Systems, 2019
32019
Nonnegative matrix cofactorization for weakly supervised image parsing
G Zhang, X Gong
IEEE Signal Processing Letters 23 (11), 1682-1686, 2016
32016
EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis
C Wang, R Grosse, S Fidler, G Zhang
International Conference on Machine Learning, 2019
22019
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
12019
On Solving Minimax Optimization Locally: A Follow-the-Ridge Approach
Y Wang, G Zhang, J Ba
arXiv preprint arXiv:1910.07512, 2019
2019
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