| Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach A Fallah, A Mokhtari, A Ozdaglar Advances in Neural Information Processing Systems 33, 3557-3568, 2020 | 699 | 2020 |
| The computational limits of deep learning NC Thompson, K Greenewald, K Lee, GF Manso arXiv preprint arXiv:2007.05558, 2020 | 575 | 2020 |
| Personalized federated learning: A meta-learning approach A Fallah, A Mokhtari, A Ozdaglar arXiv preprint arXiv:2002.07948, 2020 | 551 | 2020 |
| On the convergence theory of gradient-based model-agnostic meta-learning algorithms A Fallah, A Mokhtari, A Ozdaglar International Conference on Artificial Intelligence and Statistics, 1082-1092, 2020 | 226 | 2020 |
| Relative uncertainty learning for facial expression recognition Y Zhang, C Wang, W Deng Advances in Neural Information Processing Systems 34, 17616-17627, 2021 | 194* | 2021 |
| Neural circuit policies enabling auditable autonomy M Lechner, R Hasani, A Amini, TA Henzinger, D Rus, R Grosu Nature Machine Intelligence 2 (10), 642-652, 2020 | 189 | 2020 |
| Is conditional generative modeling all you need for decision-making? A Ajay, Y Du, A Gupta, J Tenenbaum, T Jaakkola, P Agrawal arXiv preprint arXiv:2211.15657, 2022 | 180 | 2022 |
| Bao: Making learned query optimization practical R Marcus, P Negi, H Mao, N Tatbul, M Alizadeh, T Kraska Proceedings of the 2021 International Conference on Management of Data, 1275 …, 2021 | 161 | 2021 |
| Breeds: Benchmarks for subpopulation shift S Santurkar, D Tsipras, A Madry arXiv preprint arXiv:2008.04859, 2020 | 155 | 2020 |
| From imagenet to image classification: Contextualizing progress on benchmarks D Tsipras, S Santurkar, L Engstrom, A Ilyas, A Madry International Conference on Machine Learning, 9625-9635, 2020 | 147 | 2020 |
| Integration of neural network-based symbolic regression in deep learning for scientific discovery S Kim, PY Lu, S Mukherjee, M Gilbert, L Jing, V Čeperić, M Soljačić IEEE transactions on neural networks and learning systems 32 (9), 4166-4177, 2020 | 143 | 2020 |
| Tsunami: A learned multi-dimensional index for correlated data and skewed workloads J Ding, V Nathan, M Alizadeh, T Kraska arXiv preprint arXiv:2006.13282, 2020 | 127 | 2020 |
| Hydra: A real-time spatial perception system for 3D scene graph construction and optimization N Hughes, Y Chang, L Carlone arXiv preprint arXiv:2201.13360, 2022 | 118 | 2022 |
| Rapid locomotion via reinforcement learning GB Margolis, G Yang, K Paigwar, T Chen, P Agrawal The International Journal of Robotics Research 43 (4), 572-587, 2024 | 115 | 2024 |
| Equivariant contrastive learning R Dangovski, L Jing, C Loh, S Han, A Srivastava, B Cheung, P Agrawal, ... arXiv preprint arXiv:2111.00899, 2021 | 105 | 2021 |
| Beyond expertise and roles: A framework to characterize the stakeholders of interpretable machine learning and their needs H Suresh, SR Gomez, KK Nam, A Satyanarayan Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems …, 2021 | 104 | 2021 |
| Use of neural networks for stable, accurate and physically consistent parameterization of subgrid atmospheric processes with good performance at reduced precision J Yuval, PA O'Gorman, CN Hill Geophysical Research Letters 48 (6), e2020GL091363, 2021 | 100 | 2021 |
| Do GANs always have Nash equilibria? F Farnia, A Ozdaglar International Conference on Machine Learning, 3029-3039, 2020 | 97 | 2020 |
| Generative models as a data source for multiview representation learning A Jahanian, X Puig, Y Tian, P Isola arXiv preprint arXiv:2106.05258, 2021 | 95 | 2021 |
| Predictive and generative machine learning models for photonic crystals T Christensen, C Loh, S Picek, D Jakobović, L Jing, S Fisher, V Ceperic, ... Nanophotonics 9 (13), 4183-4192, 2020 | 85 | 2020 |
