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James Lucas
James Lucas
Research Scientist, NVIDIA
Verified email at cs.toronto.edu - Homepage
Title
Cited by
Cited by
Year
Lookahead optimizer: k steps forward, 1 step back
M Zhang, J Lucas, J Ba, GE Hinton
Advances in neural information processing systems 32, 2019
5682019
Sorting out Lipschitz function approximation
C Anil, J Lucas, R Grosse
International Conference on Machine Learning, 291-301, 2019
2442019
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
229*2019
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, 2019
652019
Aggregated momentum: Stability through passive damping
J Lucas, S Sun, R Zemel, R Grosse
arXiv preprint arXiv:1804.00325, 2018
632018
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
592018
Regularized linear autoencoders recover the principal components, eventually
X Bao, J Lucas, S Sachdeva, RB Grosse
Advances in Neural Information Processing Systems 33, 6971-6981, 2020
202020
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
18*2021
Theoretical bounds on estimation error for meta-learning
J Lucas, M Ren, I Kameni, T Pitassi, R Zemel
arXiv preprint arXiv:2010.07140, 2020
102020
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, 1, 2020
72020
Optimizing data collection for machine learning
R Mahmood, J Lucas, JM Alvarez, S Fidler, M Law
Advances in Neural Information Processing Systems 35, 29915-29928, 2022
62022
How Much More Data Do I Need? Estimating Requirements for Downstream Tasks
R Mahmood, J Lucas, D Acuna, D Li, J Philion, JM Alvarez, Z Yu, S Fidler, ...
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022
62022
The Calibration Generalization Gap
A Carrell, N Mallinar, J Lucas, P Nakkiran
arXiv preprint arXiv:2210.01964, 2022
32022
Causal Scene BERT: Improving object detection by searching for challenging groups
C Resnick, O Litany, A Kar, K Kreis, J Lucas, K Cho, S Fidler
2*2022
Bridging the Sim2Real gap with CARE: Supervised Detection Adaptation with Conditional Alignment and Reweighting
V Prabhu, D Acuna, A Liao, R Mahmood, MT Law, J Hoffman, S Fidler, ...
arXiv preprint arXiv:2302.04832, 2023
12023
Optimization and loss landscape geometry of deep learning
J Lucas
University of Toronto (Canada), 2022
12022
Few-Shot Attribute Learning
M Ren, E Triantafillou, KC Wang, J Lucas, J Snell, X Pitkow, AS Tolias, ...
12020
Probing Few-Shot Generalization with Attributes
M Ren, E Triantafillou, KC Wang, J Lucas, J Snell, X Pitkow, AS Tolias, ...
arXiv preprint arXiv:2012.05895, 2020
2020
Spacetime Representation Learning
MT Law, J Lucas
The Eleventh International Conference on Learning Representations, 0
Exploring representation learning for flexible few-shot tasks
M Ren, E Triantafillou, KC Wang, J Lucas, J Snell, X Pitkow, AS Tolias, ...
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