| Exploring the limits of transfer learning with a unified text-to-text transformer. C Raffel, N Shazeer, A Roberts, K Lee, S Narang, M Matena, Y Zhou, W Li, ... J. Mach. Learn. Res. 21 (140), 1-67, 2020 | 4133 | 2020 |
| librosa: Audio and music signal analysis in python B McFee, C Raffel, D Liang, DPW Ellis, M McVicar, E Battenberg, O Nieto Proceedings of the 14th python in science conference 8, 2015 | 1701 | 2015 |
| Mixmatch: A holistic approach to semi-supervised learning D Berthelot, N Carlini, I Goodfellow, N Papernot, A Oliver, C Raffel Advances in Neural Information Processing Systems, 5050-5060, 2019 | 1470 | 2019 |
| Fixmatch: Simplifying semi-supervised learning with consistency and confidence K Sohn*, D Berthelot*, CL Li, Z Zhang, N Carlini, ED Cubuk, A Kurakin, ... arXiv preprint arXiv:2001.07685, 2020 | 1045 | 2020 |
| Theano: A Python framework for fast computation of mathematical expressions R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, N Ballas, ... arXiv preprint arXiv:1605.02688, 2016 | 984 | 2016 |
| Realistic evaluation of deep semi-supervised learning algorithms A Oliver*, A Odena*, C Raffel*, ED Cubuk, ... Advances in Neural Information Processing Systems, 3235-3246, 2018 | 796 | 2018 |
| Thermometer Encoding: One Hot Way To Resist Adversarial Examples J Buckman*, A Roy*, C Raffel, I Goodfellow (* denotes equal contribution) | 493 | 2018 |
| Remixmatch: Semi-supervised learning with distribution alignment and augmentation anchoring D Berthelot, N Carlini, ED Cubuk, A Kurakin, K Sohn, H Zhang, C Raffel arXiv preprint arXiv:1911.09785, 2019 | 480 | 2019 |
| mT5: A massively multilingual pre-trained text-to-text transformer L Xue, N Constant, A Roberts, M Kale, R Al-Rfou, A Siddhant, A Barua, ... arXiv preprint arXiv:2010.11934, 2020 | 454 | 2020 |
| mir_eval: A Transparent Implementation of Common MIR Metrics C Raffel, B McFee, EJ Humphrey, J Salamon, O Nieto, D Liang, DPW Ellis Proc. of the 15th International Society for Music Information Retrieval …, 2014 | 413 | 2014 |
| A hierarchical latent vector model for learning long-term structure in music A Roberts, J Engel, C Raffel, C Hawthorne, D Eck International conference on machine learning, 4364-4373, 2018 | 344 | 2018 |
| Feed-forward networks with attention can solve some long-term memory problems C Raffel, DPW Ellis arXiv preprint arXiv:1512.08756, 2015 | 305 | 2015 |
| Extracting training data from large language models N Carlini, F Tramer, E Wallace, M Jagielski, A Herbert-Voss, K Lee, ... 30th USENIX Security Symposium (USENIX Security 21), 2633-2650, 2021 | 299 | 2021 |
| Lasagne: first release S Dieleman, J Schlüter, C Raffel, E Olson, SK Sĝnderby, D Nouri, ... Zenodo: Geneva, Switzerland 3, 2015 | 296 | 2015 |
| How Much Knowledge Can You Pack Into the Parameters of a Language Model? A Roberts*, C Raffel*, N Shazeer (* denotes equal contribution) arXiv preprint arXiv:2002.08910, 2020 | 277 | 2020 |
| Imperceptible, robust, and targeted adversarial examples for automatic speech recognition Y Qin, N Carlini, G Cottrell, I Goodfellow, C Raffel International conference on machine learning, 5231-5240, 2019 | 267 | 2019 |
| Online and linear-time attention by enforcing monotonic alignments C Raffel, MT Luong, PJ Liu, RJ Weiss, D Eck International Conference on Machine Learning, 2837-2846, 2017 | 229 | 2017 |
| Onsets and frames: Dual-objective piano transcription C Hawthorne, E Elsen, J Song, A Roberts, I Simon, C Raffel, J Engel, ... arXiv preprint arXiv:1710.11153, 2017 | 216 | 2017 |
| Monotonic chunkwise attention CC Chiu*, C Raffel* (* denotes equal contribution) arXiv preprint arXiv:1712.05382, 2017 | 203 | 2017 |
| Learning-based methods for comparing sequences, with applications to audio-to-midi alignment and matching C Raffel Columbia University, 2016 | 203 | 2016 |
