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| Generating long sequences with sparse transformers R Child, S Gray, A Radford, I Sutskever arXiv preprint arXiv:1904.10509, 2019 | 1724 | 2019 |
| Generative pretraining from pixels M Chen, A Radford, R Child, J Wu, H Jun, D Luan, I Sutskever International conference on machine learning, 1691-1703, 2020 | 1578 | 2020 |
| Using deepspeed and megatron to train megatron-turing nlg 530b, a large-scale generative language model S Smith, M Patwary, B Norick, P LeGresley, S Rajbhandari, J Casper, ... arXiv preprint arXiv:2201.11990, 2022 | 556 | 2022 |
| Very deep vaes generalize autoregressive models and can outperform them on images R Child International Conference on Learning Representations (ICLR) 2021, Spotlight, 2020 | 303 | 2020 |
| Exploring neural transducers for end-to-end speech recognition E Battenberg, J Chen, R Child, A Coates, YGY Li, H Liu, S Satheesh, ... 2017 IEEE automatic speech recognition and understanding workshop (ASRU …, 2017 | 281* | 2017 |
| Convolutional recurrent neural networks for small-footprint keyword spotting SO Arik, M Kliegl, R Child, J Hestness, A Gibiansky, C Fougner, ... arXiv preprint arXiv:1703.05390, 2017 | 279* | 2017 |
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| DALL· E: Creating images from text A Ramesh, M Pavlov, G Goh, S Gray, M Chen, R Child, V Misra, P Mishkin, ... OpenAI blog 2, 2021 | 89 | 2021 |
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| Distribution augmentation for generative modeling H Jun, R Child, M Chen, J Schulman, A Ramesh, A Radford, I Sutskever International Conference on Machine Learning, 5006-5019, 2020 | 57 | 2020 |
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