| Deep Voice: Real-time neural text-to-speech SO Arik, M Chrzanowski, A Coates, G Diamos, A Gibiansky, Y Kang, X Li, ... Internation Conference on Machine Learning, 2017 | 716 | 2017 |
| Deep Voice 2: Multi-Speaker Neural Text-to-Speech S Arik, G Diamos, A Gibiansky, J Miller, K Peng, W Ping, J Raiman, ... Advances in Neural Information Processing, 2017 | 560* | 2017 |
| Deep voice 3: Scaling text-to-speech with convolutional sequence learning W Ping, K Peng, A Gibiansky, SO Arik, A Kannan, S Narang, J Raiman, ... arXiv preprint arXiv:1710.07654, 2017 | 477 | 2017 |
| Test-time training with self-supervision for generalization under distribution shifts Y Sun, X Wang, Z Liu, J Miller, A Efros, M Hardt International conference on machine learning, 9229-9248, 2020 | 440* | 2020 |
| Traversing Knowledge Graphs in Vector Space K Guu, J Miller, P Liang Empirical Methods in Natural Language Processing, 2015 | 382 | 2015 |
| Beyond the imitation game: Quantifying and extrapolating the capabilities of language models A Srivastava, A Rastogi, A Rao, AAM Shoeb, A Abid, A Fisch, AR Brown, ... arXiv preprint arXiv:2206.04615, 2022 | 321 | 2022 |
| Deep voice 3: 2000-speaker neural text-to-speech W Ping, K Peng, A Gibiansky, SO Arik, A Kannan, S Narang, J Raiman, ... proc. ICLR, 214-217, 2018 | 310 | 2018 |
| Retiring adult: New datasets for fair machine learning F Ding, M Hardt, J Miller, L Schmidt Advances in neural information processing systems 34, 6478-6490, 2021 | 210 | 2021 |
| Stable recurrent models J Miller, M Hardt arXiv preprint arXiv:1805.10369, 2018 | 160* | 2018 |
| The social cost of strategic classification S Milli, J Miller, AD Dragan, M Hardt Proceedings of the Conference on Fairness, Accountability, and Transparency …, 2019 | 156 | 2019 |
| Accuracy on the line: on the strong correlation between out-of-distribution and in-distribution generalization JP Miller, R Taori, A Raghunathan, S Sagawa, PW Koh, V Shankar, ... International Conference on Machine Learning, 7721-7735, 2021 | 152 | 2021 |
| A meta-analysis of overfitting in machine learning R Roelofs, V Shankar, B Recht, S Fridovich-Keil, M Hardt, J Miller, ... Advances in Neural Information Processing Systems 32, 2019 | 142 | 2019 |
| The effect of natural distribution shift on question answering models J Miller, K Krauth, B Recht, L Schmidt International conference on machine learning, 6905-6916, 2020 | 121 | 2020 |
| Strategic Classification is Causal Modeling in Disguise J Miller, S Milli, M Hardt arXiv preprint arXiv:1910.10362, 2019 | 91 | 2019 |
| Openclip, July 2021 G Ilharco, M Wortsman, R Wightman, C Gordon, N Carlini, R Taori, ... If you use this software, please cite it as below 2 (4), 5, 0 | 69 | |
| OpenCLIP G Ilharco, M Wortsman, R Wightman, C Gordon, N Carlini, R Taori, ... Zenodo 4, 5, 2021 | 56 | 2021 |
| Outside the echo chamber: Optimizing the performative risk JP Miller, JC Perdomo, T Zrnic International Conference on Machine Learning, 7710-7720, 2021 | 54 | 2021 |
| Model similarity mitigates test set overuse H Mania, J Miller, L Schmidt, M Hardt, B Recht Advances in Neural Information Processing Systems 32, 2019 | 45 | 2019 |
| Globally Normalized Reader J Raiman, J Miller Empirical Methods in Natural Language Processing, 2017 | 31 | 2017 |
| Systems and methods for real-time neural text-to-speech M Chrzanowski, A Coates, G DIAMOS, A GIBIANSKY, J Miller, A Ng, ... US Patent 10,872,598, 2020 | 14 | 2020 |
