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Andrew Dai
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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
56802023
Finetuned language models are zero-shot learners
J Wei, M Bosma, VY Zhao, K Guu, AW Yu, B Lester, N Du, AM Dai, QV Le
arXiv preprint arXiv:2109.01652, 2021
34842021
Scaling instruction-finetuned language models
HW Chung, L Hou, S Longpre, B Zoph, Y Tay, W Fedus, Y Li, X Wang, ...
Journal of Machine Learning Research 25 (70), 1-53, 2024
33272024
Natural questions: a benchmark for question answering research
T Kwiatkowski, J Palomaki, O Redfield, M Collins, A Parikh, C Alberti, ...
Transactions of the Association for Computational Linguistics 7, 453-466, 2019
30572019
Generating sentences from a continuous space
SR Bowman, L Vilnis, O Vinyals, AM Dai, R Jozefowicz, S Bengio
Proceedings of the 20th SIGNLL Conference on Computational Natural Language …, 2016
29552016
Gemini: a family of highly capable multimodal models
G Team, R Anil, S Borgeaud, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ...
arXiv preprint arXiv:2312.11805, 2023
25562023
Scalable and accurate deep learning with electronic health records
A Rajkomar, E Oren, K Chen, AM Dai, N Hajaj, M Hardt, PJ Liu, X Liu, ...
NPJ digital medicine 1 (1), 1-10, 2018
25502018
HyperNetworks
D Ha, A Dai, QV Le
Proceedings of the International Conference on Learning Representations, 2017
18732017
Semi-supervised sequence learning
AM Dai, QV Le
Advances in neural information processing systems 28, 2015
17072015
Palm 2 technical report
R Anil, AM Dai, O Firat, M Johnson, D Lepikhin, A Passos, S Shakeri, ...
arXiv preprint arXiv:2305.10403, 2023
15792023
Adversarial Training Methods for Semi-Supervised Text Classification
T Miyato, AM Dai, I Goodfellow
Proceedings of the International Conference on Learning Representations, 2017
13852017
Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
A Srivastava, A Rastogi, A Rao, AAM Shoeb, A Abid, A Fisch, AR Brown, ...
arXiv preprint arXiv:2206.04615, 2022
13182022
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
G Team, P Georgiev, VI Lei, R Burnell, L Bai, A Gulati, G Tanzer, ...
arXiv preprint arXiv:2403.05530, 2024
10312024
Music transformer
CZA Huang, A Vaswani, J Uszkoreit, N Shazeer, I Simon, C Hawthorne, ...
arXiv preprint arXiv:1809.04281, 2018
10222018
Maskgan: better text generation via filling in the_
W Fedus, I Goodfellow, AM Dai
arXiv preprint arXiv:1801.07736, 2018
6562018
Glam: Efficient scaling of language models with mixture-of-experts
N Du, Y Huang, AM Dai, S Tong, D Lepikhin, Y Xu, M Krikun, Y Zhou, ...
International Conference on Machine Learning, 5547-5569, 2022
6332022
Document embedding with paragraph vectors
AM Dai, C Olah, QV Le
NIPS 2014 Deep learning workshop, 2015
5842015
Gemini: A family of highly capable multimodal models
R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ...
arXiv preprint arXiv:2312.11805 1, 2023
3162023
Mixture-of-experts with expert choice routing
Y Zhou, T Lei, H Liu, N Du, Y Huang, V Zhao, AM Dai, QV Le, J Laudon
Advances in Neural Information Processing Systems 35, 7103-7114, 2022
2802022
Many paths to equilibrium: GANs do not need to decrease a divergence at every step
W Fedus, M Rosca, B Lakshminarayanan, AM Dai, S Mohamed, ...
arXiv preprint arXiv:1710.08446, 2017
2712017
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