| Lookahead optimizer: k steps forward, 1 step back M Zhang, J Lucas, J Ba, GE Hinton Advances in neural information processing systems 32, 2019 | 796 | 2019 |
| Sorting out Lipschitz function approximation C Anil, J Lucas, R Grosse International Conference on Machine Learning, 291-301, 2019 | 360 | 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 | 331* | 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 | 109 | 2019 |
| Aggregated momentum: Stability through passive damping J Lucas, S Sun, R Zemel, R Grosse arXiv preprint arXiv:1804.00325, 2018 | 83 | 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 | 73 | 2018 |
| Att3d: Amortized text-to-3d object synthesis J Lorraine, K Xie, X Zeng, CH Lin, T Takikawa, N Sharp, TY Lin, MY Liu, ... Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | 56 | 2023 |
| 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 | 39 | 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 | 33* | 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 | 25 | 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 | 22 | 2022 |
| The calibration generalization gap AM Carrell, N Mallinar, J Lucas, P Nakkiran arXiv preprint arXiv:2210.01964, 2022 | 17 | 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 | 14 | 2020 |
| Graph metanetworks for processing diverse neural architectures D Lim, H Maron, MT Law, J Lorraine, J Lucas arXiv preprint arXiv:2312.04501, 2023 | 13 | 2023 |
| Few-Shot Attribute Learning M Ren, E Triantafillou, KC Wang, J Lucas, J Snell, X Pitkow, AS Tolias, ... | 13* | 2020 |
| Latte3d: Large-scale amortized text-to-enhanced3d synthesis K Xie, J Lorraine, T Cao, J Gao, J Lucas, A Torralba, S Fidler, X Zeng arXiv preprint arXiv:2403.15385, 2024 | 10 | 2024 |
| 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 | 8* | 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 | 4 | 2023 |
| Improving hyperparameter optimization with checkpointed model weights N Mehta, J Lorraine, S Masson, R Arunachalam, ZP Bhat, J Lucas, ... arXiv preprint arXiv:2406.18630, 2024 | 1 | 2024 |
| 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 | 1 | 2023 |
Roger GrosseAssociate Professor, University of TorontoVerified email at cs.toronto.edu
