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Rewon Child
Rewon Child
Founding Team / Technical Staff at Inflection
Bestätigte E-Mail-Adresse bei inflection.ai - Startseite
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Zitiert von
Zitiert von
Jahr
Language models are few-shot learners
T Brown, B Mann, N Ryder, M Subbiah, JD Kaplan, P Dhariwal, ...
Advances in neural information processing systems 33, 1877-1901, 2020
46831*2020
Language models are unsupervised multitask learners
A Radford, J Wu, R Child, D Luan, D Amodei, I Sutskever
OpenAI blog 1 (8), 9, 2019
25698*2019
Palm: Scaling language modeling with pathways
A Chowdhery, S Narang, J Devlin, M Bosma, G Mishra, A Roberts, ...
Journal of Machine Learning Research 24 (240), 1-113, 2023
53942023
Scaling laws for neural language models
J Kaplan, S McCandlish, T Henighan, TB Brown, B Chess, R Child, ...
arXiv preprint arXiv:2001.08361, 2020
25792020
Generating long sequences with sparse transformers
R Child, S Gray, A Radford, I Sutskever
arXiv preprint arXiv:1904.10509, 2019
20642019
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
18462020
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
6712022
Very deep vaes generalize autoregressive models and can outperform them on images
R Child
International Conference on Learning Representations (ICLR) 2021, Spotlight, 2020
3522020
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
302*2017
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
291*2017
& Amodei, D.(2020)
TB Brown, B Mann, N Ryder, M Subbiah, J Kaplan, P Dhariwal, ...
Language models are few-shot learners, 2005
1962005
Language models are few-shot learners
B Mann, N Ryder, M Subbiah, J Kaplan, P Dhariwal, A Neelakantan, ...
arXiv preprint arXiv:2005.14165 1, 2020
1732020
Scaling laws for neural language models. arXiv 2020
J Kaplan, S McCandlish, T Henighan, TB Brown, B Chess, R Child, ...
arXiv preprint arXiv:2001.08361, 2001
1312001
Language models are few-shot learners (arXiv: 2005.14165). arXiv
TB Brown, B Mann, N Ryder, M Subbiah, J Kaplan, P Dhariwal, ...
1252005
Palm: Scaling language modeling with pathways. arXiv 2022
A Chowdhery, S Narang, J Devlin, M Bosma, G Mishra, A Roberts, ...
arXiv preprint arXiv:2204.02311 10, 2022
1192022
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
1052021
Active learning for speech recognition: the power of gradients
J Huang, R Child, V Rao, H Liu, S Satheesh, A Coates
arXiv preprint arXiv:1612.03226, 2016
812016
Language models are unsupervised multitask learners. OpenAI blog (2019)
A Radford, J Wu, R Child, D Luan, D Amodei, I Sutskever
URL: https://cdn. openai. com/better-language-models …, 2022
792022
Language models are few-shot learners
A Radford, J Wu, R Child, D Luan, D Amodei, I Sutskever
Adv. Neural Inf. Process. Syst 33, 146, 2020
762020
Scaling laws for neural language models (2020)
J Kaplan, S McCandlish, T Henighan, TB Brown, B Chess, R Child, ...
arXiv preprint arXiv:2001.08361, 2001
712001
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