| Training deep networks with synthetic data: Bridging the reality gap by domain randomization J Tremblay, A Prakash, D Acuna, M Brophy, V Jampani, C Anil, T To, ... Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 987 | 2018 |
| Training deep networks with synthetic data: Bridging the reality gap by domain randomization J Tremblay, A Prakash, D Acuna, M Brophy, V Jampani, C Anil, T To, ... Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 987 | 2018 |
| Solving quantitative reasoning problems with language models A Lewkowycz, A Andreassen, D Dohan, E Dyer, H Michalewski, ... Advances in Neural Information Processing Systems 35, 3843-3857, 2022 | 448 | 2022 |
| Sorting out lipschitz function approximation C Anil, J Lucas, R Grosse International Conference on Machine Learning, 291-301, 2019 | 340 | 2019 |
| Exploring length generalization in large language models C Anil, Y Wu, A Andreassen, A Lewkowycz, V Misra, V Ramasesh, ... Advances in Neural Information Processing Systems 35, 38546-38556, 2022 | 133 | 2022 |
| Timbretron: A wavenet (cyclegan (cqt (audio))) pipeline for musical timbre transfer S Huang, Q Li, C Anil, X Bao, S Oore, RB Grosse arXiv preprint arXiv:1811.09620, 2018 | 125 | 2018 |
| 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 | 104 | 2019 |
| Towards monosemanticity: Decomposing language models with dictionary learning T Bricken, A Templeton, J Batson, B Chen, A Jermyn, T Conerly, N Turner, ... Transformer Circuits Thread, 2, 2023 | 89 | 2023 |
| Studying large language model generalization with influence functions R Grosse, J Bae, C Anil, N Elhage, A Tamkin, A Tajdini, B Steiner, D Li, ... arXiv preprint arXiv:2308.03296, 2023 | 59 | 2023 |
| Solving quantitative reasoning problems with language models, 2022 A Lewkowycz, A Andreassen, D Dohan, E Dyer, H Michalewski, ... URL https://arxiv. org/abs/2206.14858, 2022 | 31 | 2022 |
| Generation of synthetic images for training a neural network model J Tremblay, A Prakash, MA Brophy, V Jampani, C Anil, ST Birchfield, ... US Patent 10,867,214, 2020 | 23 | 2020 |
| Sleeper agents: Training deceptive llms that persist through safety training E Hubinger, C Denison, J Mu, M Lambert, M Tong, M MacDiarmid, ... arXiv preprint arXiv:2401.05566, 2024 | 20 | 2024 |
| Path Independent Equilibrium Models Can Better Exploit Test-Time Computation C Anil, A Pokle, K Liang, J Treutlein, Y Wu, S Bai, JZ Kolter, RB Grosse Advances in Neural Information Processing Systems 35, 7796-7809, 2022 | 12 | 2022 |
| Learning to Give Checkable Answers with Prover-Verifier Games C Anil, G Zhang, Y Wu, R Grosse arXiv preprint arXiv:2108.12099, 2021 | 10 | 2021 |
| Refining labeling of time-associated data C Anil US Patent App. 16/153,430, 2019 | 8 | 2019 |
| Learning to Elect C Anil, X Bao Advances in Neural Information Processing Systems 34, 8006-8017, 2021 | 5 | 2021 |
| Out-of-Distribution Generalization with Deep Equilibrium Models K Liang, C Anil, Y Wu, R Grosse ICML 2021 Workshop on Uncertainty and Robustness in Deep Learning, 2021 | 5 | 2021 |
| Neural network model trained using generated synthetic images J Tremblay, A Prakash, MA Brophy, V Jampani, C Anil, ST Birchfield, ... US Patent 11,715,251, 2023 | | 2023 |
| Sparse capsule networks for informative representation learning in digital pathology M McNeil, C Anil, A Martel Medical Imaging 2022: Digital and Computational Pathology 12039, 305-311, 2022 | | 2022 |
| Generation of synthetic images for training a neural network model J Tremblay, A Prakash, MA Brophy, V Jampani, C Anil, ST Birchfield, ... US Patent 11,182,649, 2021 | | 2021 |
Roger GrosseAssociate Professor, University of Toronto在 cs.toronto.edu 的电子邮件经过验证
