| 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, ... Journal of Machine Learning Research, 2020 | 10207 | 2020 |
| Mixmatch: A holistic approach to semi-supervised learning D Berthelot, N Carlini, I Goodfellow, N Papernot, A Oliver, C Raffel Neural Information Processing Systems, 2019 | 2603 | 2019 |
| librosa: Audio and music signal analysis in python B McFee, C Raffel, D Liang, DPW Ellis, M McVicar, E Battenberg, O Nieto Python in Science Conference, 2015 | 2484 | 2015 |
| Fixmatch: Simplifying semi-supervised learning with consistency and confidence K Sohn, D Berthelot, CL Li, Z Zhang, N Carlini, ED Cubuk, A Kurakin, ... Neural Information Processing Systems, 2020 | 2305 | 2020 |
| 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, ... Annual Conference of the North American Chapter of the Association for …, 2020 | 1200 | 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 | 1196 | 2016 |
| Realistic evaluation of deep semi-supervised learning algorithms A Oliver, A Odena, C Raffel, ED Cubuk, I Goodfellow Neural Information Processing Systems, 2018 | 1035 | 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 International Conference on Learning Representations, 2019 | 869 | 2019 |
| Extracting training data from large language models N Carlini, F Tramer, E Wallace, M Jagielski, A Herbert-Voss, K Lee, ... USENIX Security Symposium, 2021 | 752 | 2021 |
| Multitask Prompted Training Enables Zero-Shot Task Generalization V Sanh, A Webson, C Raffel, SH Bach, L Sutawika, Z Alyafeai, A Chaffin, ... International Conference on Learning Representations, 2021 | 682 | 2021 |
| Thermometer Encoding: One Hot Way To Resist Adversarial Examples J Buckman, A Roy, C Raffel, I Goodfellow International Conference on Learning Representations, 2018 | 663 | 2018 |
| Emergent abilities of large language models J Wei, Y Tay, R Bommasani, C Raffel, B Zoph, S Borgeaud, D Yogatama, ... Transactions on Machine Learning Research, 2022 | 637 | 2022 |
| How Much Knowledge Can You Pack Into the Parameters of a Language Model? A Roberts, C Raffel, N Shazeer Conference on Empirical Methods in Natural Language Processing, 2020 | 540 | 2020 |
| Probabilistic machine learning: an introduction KP Murphy MIT press, 2022 | 514 | 2022 |
| mir_eval: A Transparent Implementation of Common MIR Metrics C Raffel, B McFee, EJ Humphrey, J Salamon, O Nieto, D Liang, DPW Ellis International Society for Music Information Retrieval Conference, 2014 | 511 | 2014 |
| Lasagne: first release S Dieleman, J Schlüter, C Raffel, E Olson, SK Sønderby, D Nouri, ... | 509* | 2015 |
| Bloom: A 176b-parameter open-access multilingual language model TL Scao, A Fan, C Akiki, E Pavlick, S Ilić, D Hesslow, R Castagné, ... arXiv preprint arXiv:2211.05100, 2022 | 472 | 2022 |
| 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, 2018 | 471 | 2018 |
| 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, 2019 | 388 | 2019 |
| Feed-forward networks with attention can solve some long-term memory problems C Raffel, DPW Ellis International Conference on Learning Representations, 2015 | 361* | 2015 |
Adam RobertsGoogle BrainBestätigte E-Mail-Adresse bei google.com
