James Lucas
James Lucas
PhD Candidate, University of Toronto; Vector Institute
Verified email at cs.toronto.edu - Homepage
Title
Cited by
Cited by
Year
Lookahead optimizer: k steps forward, 1 step back
MR Zhang, J Lucas, G Hinton, J Ba
arXiv preprint arXiv:1907.08610, 2019
2712019
Sorting out Lipschitz function approximation
C Anil, J Lucas, R Grosse
International Conference on Machine Learning, 291-301, 2019
1272019
Don't blame the ELBO! A linear VAE perspective on posterior collapse
J Lucas, G Tucker, R Grosse, M Norouzi
arXiv preprint arXiv:1911.02469, 2019
89*2019
Aggregated momentum: Stability through passive damping
J Lucas, S Sun, R Zemel, R Grosse
arXiv preprint arXiv:1804.00325, 2018
342018
Adversarial distillation of bayesian neural network posteriors
KC Wang, P Vicol, J Lucas, L Gu, R Grosse, R Zemel
International Conference on Machine Learning, 5190-5199, 2018
332018
Preventing gradient attenuation in lipschitz constrained convolutional networks
Q Li, S Haque, C Anil, J Lucas, RB Grosse, JH Jacobsen
Advances in neural information processing systems 32, 15390-15402, 2019
302019
Regularized linear autoencoders recover the principal components, eventually
X Bao, J Lucas, S Sachdeva, R Grosse
arXiv preprint arXiv:2007.06731, 2020
62020
Lookahead Optimizer: K steps forward, 1 step back. arXiv 2019
MR Zhang, J Lucas, G Hinton, J Ba
arXiv preprint arXiv:1907.08610, 0
6
Theoretical bounds on estimation error for meta-learning
J Lucas, M Ren, I Kameni, T Pitassi, R Zemel
arXiv preprint arXiv:2010.07140, 2020
42020
Analyzing Monotonic Linear Interpolation in Neural Network Loss Landscapes
J Lucas, J Bae, MR Zhang, S Fort, R Zemel, R Grosse
arXiv preprint arXiv:2104.11044, 2021
3*2021
Flexible Few-Shot Learning with Contextual Similarity
M Ren, E Triantafillou, KC Wang, J Lucas, J Snell, X Pitkow, AS Tolias, ...
arXiv preprint arXiv:2012.05895, 2020
32020
Exploring representation learning for flexible few-shot tasks
M Ren, E Triantafillou, KC Wang, J Lucas, J Snell, X Pitkow, AS Tolias, ...
2020
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Articles 1–12