Tackling climate change with machine learning D Rolnick, PL Donti, LH Kaack, K Kochanski, A Lacoste, K Sankaran, ... ACM Computing Surveys (CSUR) 55 (2), 1-96, 2022 | 1068* | 2022 |
Experience replay for continual learning D Rolnick, A Ahuja, J Schwarz, TP Lillicrap, G Wayne Advances in Neural Information Processing Systems, 2019 | 947 | 2019 |
Why does deep and cheap learning work so well? HW Lin, M Tegmark, D Rolnick Journal of Statistical Physics 168, 1223-1247, 2017 | 765 | 2017 |
Deep learning is robust to massive label noise D Rolnick, A Veit, S Belongie, N Shavit arXiv preprint arXiv:1705.10694, 2017 | 654 | 2017 |
How to start training: The effect of initialization and architecture B Hanin, D Rolnick Advances in Neural Information Processing Systems, 2018 | 275 | 2018 |
Complexity of linear regions in deep networks B Hanin, D Rolnick International Conference on Machine Learning, 2596-2604, 2019 | 230 | 2019 |
Deep ReLU networks have surprisingly few activation patterns B Hanin, D Rolnick Advances in Neural Information Processing Systems, 361-370, 2019 | 229 | 2019 |
The power of deeper networks for expressing natural functions D Rolnick, M Tegmark International Conference on Learning Representations, 2018 | 224 | 2018 |
Aligning artificial intelligence with climate change mitigation LH Kaack, PL Donti, E Strubell, G Kamiya, F Creutzig, D Rolnick Nature Climate Change 12 (6), 518-527, 2022 | 199 | 2022 |
DC3: A learning method for optimization with hard constraints PL Donti, D Rolnick, JZ Kolter International Conference on Learning Representations, 2021 | 156 | 2021 |
Measuring and regularizing networks in function space AS Benjamin, D Rolnick, K Kording International Conference on Learning Representations, 2019 | 143 | 2019 |
Reverse-engineering deep ReLU networks D Rolnick, K Kording International Conference on Machine Learning, 8178-8187, 2020 | 102 | 2020 |
Digitalization and the Anthropocene F Creutzig, D Acemoglu, X Bai, PN Edwards, MJ Hintz, LH Kaack, S Kilkis, ... Annual review of environment and resources 47 (1), 479-509, 2022 | 48 | 2022 |
A multi-pass approach to large-scale connectomics Y Meirovitch, A Matveev, H Saribekyan, D Budden, D Rolnick, G Odor, ... arXiv preprint arXiv:1612.02120, 2016 | 43 | 2016 |
Cross-classification clustering: An efficient multi-object tracking technique for 3-d instance segmentation in connectomics Y Meirovitch, L Mi, H Saribekyan, A Matveev, D Rolnick, N Shavit Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 38 | 2019 |
Lightweight, pre-trained transformers for remote sensing timeseries G Tseng, R Cartuyvels, I Zvonkov, M Purohit, D Rolnick, H Kerner arXiv preprint arXiv:2304.14065, 2023 | 30 | 2023 |
Bugs in the data: How ImageNet misrepresents biodiversity AS Luccioni, D Rolnick AAAI Conference on Artificial Intelligence, 2022 | 28 | 2022 |
Faenet: Frame averaging equivariant gnn for materials modeling AA Duval, V Schmidt, A Hernández-Garcıa, S Miret, FD Malliaros, ... International Conference on Machine Learning, 9013-9033, 2023 | 27 | 2023 |
Morphological error detection in 3D segmentations D Rolnick, Y Meirovitch, T Parag, H Pfister, V Jain, JW Lichtman, ... arXiv preprint arXiv:1705.10882, 2017 | 25 | 2017 |
Techniques for symbol grounding with satnet S Topan, D Rolnick, X Si Advances in Neural Information Processing Systems 34, 20733-20744, 2021 | 23 | 2021 |