Seguir
Arvind Neelakantan
Arvind Neelakantan
Meta
Dirección de correo verificada de meta.com
Título
Citado por
Citado por
Año
Language models are few-shot learners
TB Brown
arXiv preprint arXiv:2005.14165, 2020
399912020
Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray
TB Brown, B Mann, N Ryder, M Subbiah, J Kaplan, P Dhariwal, ...
Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford …, 2020
95062020
Gpt-4 technical report
J Achiam, S Adler, S Agarwal, L Ahmad, I Akkaya, FL Aleman, D Almeida, ...
arXiv preprint arXiv:2303.08774, 2023
79062023
GPT-4 technical report
R OpenAI
ArXiv 2303, 08774, 2023
16032023
Adding gradient noise improves learning for very deep networks
A Neelakantan, L Vilnis, QV Le, I Sutskever, L Kaiser, K Kurach, J Martens
International Conference on Learning Representations Workshop (ICLR Workshop …, 2015
6492015
Efficient non-parametric estimation of multiple embeddings per word in vector space
A Neelakantan, J Shankar, A Passos, A McCallum
Conference on Empirical Methods in Natural Language Processing, 2014, 2015
6282015
Text and code embeddings by contrastive pre-training
A Neelakantan, T Xu, R Puri, A Radford, JM Han, J Tworek, Q Yuan, ...
arXiv preprint arXiv:2201.10005, 2022
4392022
Compositional vector space models for knowledge base completion
A Neelakantan, B Roth, A McCallum
arXiv preprint arXiv:1504.06662, 2015
3602015
Chains of reasoning over entities, relations, and text using recurrent neural networks
R Das, A Neelakantan, D Belanger, A McCallum
European Chapter of the Association for Computational Linguistics (EACL), 2017., 2016
3412016
Neural programmer: Inducing latent programs with gradient descent
A Neelakantan, QV Le, I Sutskever
International Conference on Learning Representations (ICLR), 2016, 2015
2972015
Language Models are Few-Shot Learners. 2020. doi: 10.48550
TB Brown, B Mann, N Ryder, M Subbiah, J Kaplan, P Dhariwal, ...
arxiv, 5-7, 2005
2612005
Taskmaster-1: Toward a realistic and diverse dialog dataset
B Byrne, K Krishnamoorthi, C Sankar, A Neelakantan, D Duckworth, ...
arXiv preprint arXiv:1909.05358, 2019
2352019
& Amodei, D.(2020)
TB Brown, B Mann, N Ryder, M Subbiah, J Kaplan, P Dhariwal, ...
Language models are few-shot learners, 2005
2002005
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
1942020
Learning a natural language interface with neural programmer
A Neelakantan, QV Le, M Abadi, A McCallum, D Amodei
International Conference on Learning Representations (ICLR), 2017., 2016
1412016
Language models are few-shot learners (arXiv: 2005.14165). arXiv
TB Brown, B Mann, N Ryder, M Subbiah, J Kaplan, P Dhariwal, ...
1322005
Theory and experiments on vector quantized autoencoders
A Roy, A Vaswani, A Neelakantan, N Parmar
arXiv preprint arXiv:1805.11063, 2018
1002018
Inferring Missing Entity Type Instances for Knowledge Base Completion: New Dataset and Methods
A Neelakantan, MW Chang
The North American Chapter of the Association for Computational Linguistics …, 2015
972015
Trading off diversity and quality in natural language generation
H Zhang, D Duckworth, D Ippolito, A Neelakantan
arXiv preprint arXiv:2004.10450, 2020
932020
Predicting the impact of scientific concepts using full‐text features
K McKeown, H Daume III, S Chaturvedi, J Paparrizos, K Thadani, P Barrio, ...
Journal of the Association for Information Science and Technology 67 (11 …, 2016
852016
El sistema no puede realizar la operación en estos momentos. Inténtalo de nuevo más tarde.
Artículos 1–20