Arvind Neelakantan
Arvind Neelakantan
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Language models are few-shot learners
TB Brown, B Mann, N Ryder, M Subbiah, J Kaplan, P Dhariwal, ...
arXiv preprint arXiv:2005.14165, 2020
GPT-4 technical report
IA J Achiam, S Adler, S Agarwal, L Ahmad
ArXiv 2303, 2023
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
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
Compositional vector space models for knowledge base inference
A Neelakantan, B Roth, A McCallum
2015 aaai spring symposium series, 2015
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
Neural programmer: Inducing latent programs with gradient descent
A Neelakantan, QV Le, I Sutskever
International Conference on Learning Representations (ICLR), 2016, 2015
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
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
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
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
Theory and experiments on vector quantized autoencoders
A Roy, A Vaswani, A Neelakantan, N Parmar
arXiv preprint arXiv:1805.11063, 2018
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
Trading off diversity and quality in natural language generation
H Zhang, D Duckworth, D Ippolito, A Neelakantan
arXiv preprint arXiv:2004.10450, 2020
Learning Dictionaries for Named Entity Recognition using Minimal Supervision
A Neelakantan, M Collins
European Chapter of the Association for Computational Linguistics., 2014
Generalizing to unseen entities and entity pairs with row-less universal schema
P Verga, A Neelakantan, A McCallum
European Chapter of the Association for Computational Linguistics (EACL), 2017., 2016
RelNet: End-to-end Modeling of Entities & Relations
T Bansal, A Neelakantan, A McCallum
arXiv preprint arXiv:1706.07179, 2017
Unsupervised neural machine translation with generative language models only
JM Han, I Babuschkin, H Edwards, A Neelakantan, T Xu, S Polu, A Ray, ...
arXiv preprint arXiv:2110.05448, 2021
Parallel scheduled sampling
D Duckworth, A Neelakantan, B Goodrich, L Kaiser, S Bengio
arXiv preprint arXiv:1906.04331, 2019
Active error detection and resolution for speech-to-speech translation
R Prasad, R Kumar, S Ananthakrishnan, W Chen, S Hewavitharana, ...
Proceedings of the 9th International Workshop on Spoken Language Translation …, 2012
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