| Lookahead optimizer: k steps forward, 1 step back M Zhang, J Lucas, J Ba, GE Hinton Advances in neural information processing systems 32, 2019 | 657 | 2019 |
| Sorting out Lipschitz function approximation C Anil, J Lucas, R Grosse International Conference on Machine Learning, 291-301, 2019 | 293 | 2019 |
| 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 | 274* | 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 | 87 | 2019 |
| Aggregated momentum: Stability through passive damping J Lucas, S Sun, R Zemel, R Grosse arXiv preprint arXiv:1804.00325, 2018 | 70 | 2018 |
| 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 | 66 | 2018 |
| 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 | 28 | 2020 |
| 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 | 22* | 2021 |
| 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 | 13 | 2022 |
| 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 | 12 | 2022 |
| Theoretical bounds on estimation error for meta-learning J Lucas, M Ren, I Kameni, T Pitassi, R Zemel arXiv preprint arXiv:2010.07140, 2020 | 11 | 2020 |
| The calibration generalization gap AM Carrell, N Mallinar, J Lucas, P Nakkiran arXiv preprint arXiv:2210.01964, 2022 | 10 | 2022 |
| ATT3D: Amortized Text-to-3D Object Synthesis J Lorraine, K Xie, X Zeng, CH Lin, T Takikawa, N Sharp, TY Lin, MY Liu, ... arXiv preprint arXiv:2306.07349, 2023 | 8 | 2023 |
| Few-Shot Attribute Learning M Ren, E Triantafillou, KC Wang, J Lucas, J Snell, X Pitkow, AS Tolias, ... | 8* | 2021 |
| Causal Scene BERT: Improving object detection by searching for challenging groups of data C Resnick, O Litany, A Kar, K Kreis, J Lucas, K Cho, S Fidler arXiv preprint arXiv:2202.03651, 2022 | 5* | 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 | 1 | 2023 |
| Optimization and loss landscape geometry of deep learning J Lucas University of Toronto (Canada), 2022 | 1 | 2022 |
| ESTIMATING OPTIMAL TRAINING DATA SET SIZES FOR MACHINE LEARNING MODEL SYSTEMS AND APPLICATIONS RR Mahmood, M Law, JR Lucas, Z Yu, JM Alvarez Lopez, S Fidler US Patent App. 18/318,212, 2023 | | 2023 |
| Interactive AI Material Generation and Editing in NVIDIA Omniverse H Abu Alhaija, J Lucas, A Zook, M Babcock, D Tyner, R Rao, M Shugrina ACM SIGGRAPH 2023 Real-Time Live!, 1-2, 2023 | | 2023 |
| Spacetime Representation Learning MT Law, J Lucas The Eleventh International Conference on Learning Representations, 2023 | | 2023 |
Roger GrosseAssociate Professor, University of TorontoVerified email at cs.toronto.edu
