David Dohan
David Dohan
Google Brain
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Zitiert von
Zitiert von
Unsupervised pixel-level domain adaptation with generative adversarial networks
K Bousmalis, N Silberman, D Dohan, D Erhan, D Krishnan
Proceedings of the IEEE conference on computer vision and pattern …, 2017
Qanet: Combining local convolution with global self-attention for reading comprehension
AW Yu, D Dohan, MT Luong, R Zhao, K Chen, M Norouzi, QV Le
International Conference on Learning Representations, 2018
Palm: Scaling language modeling with pathways
A Chowdhery, S Narang, J Devlin, M Bosma, G Mishra, A Roberts, ...
arXiv preprint arXiv:2204.02311, 2022
Rethinking attention with performers
K Choromanski, V Likhosherstov, D Dohan, X Song, A Gane, T Sarlos, ...
International Conference on Learning Representations, 2021
Program synthesis with large language models
J Austin, A Odena, M Nye, M Bosma, H Michalewski, D Dohan, E Jiang, ...
arXiv preprint arXiv:2108.07732, 2021
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
Show your work: Scratchpads for intermediate computation with language models
M Nye, AJ Andreassen, G Gur-Ari, H Michalewski, J Austin, D Bieber, ...
arXiv preprint arXiv:2112.00114, 2021
Solving quantitative reasoning problems with language models
A Lewkowycz, A Andreassen, D Dohan, E Dyer, H Michalewski, ...
arXiv preprint arXiv:2206.14858, 2022
Model-based reinforcement learning for biological sequence design
C Angermueller, D Dohan, D Belanger, R Deshpande, K Murphy, ...
Masked language modeling for proteins via linearly scalable long-context transformers
K Choromanski, V Likhosherstov, D Dohan, X Song, A Gane, T Sarlos, ...
arXiv preprint arXiv:2006.03555, 2020
Learning hierarchical semantic segmentations of LIDAR data
D Dohan, B Matejek, T Funkhouser
2015 International Conference on 3D Vision, 273-281, 2015
Population-based black-box optimization for biological sequence design
C Angermueller, D Belanger, A Gane, Z Mariet, D Dohan, K Murphy, ...
International Conference on Machine Learning, 324-334, 2020
Language model cascades
D Dohan, W Xu, A Lewkowycz, J Austin, D Bieber, RG Lopes, Y Wu, ...
arXiv preprint arXiv:2207.10342, 2022
Is transfer learning necessary for protein landscape prediction?
A Shanehsazzadeh, D Belanger, D Dohan
NeurIPS workshop on Machine Learning in Structural Biology, 2020
Amortized bayesian optimization over discrete spaces
K Swersky, Y Rubanova, D Dohan, K Murphy
Conference on Uncertainty in Artificial Intelligence, 769-778, 2020
Transforming source domain images into target domain images
K Bousmalis, N Silberman, DM Dohan, D Erhan, D Krishnan
US Patent 10,991,074, 2021
Latent programmer: Discrete latent codes for program synthesis
J Hong, D Dohan, R Singh, C Sutton, M Zaheer
International Conference on Machine Learning, 4308-4318, 2021
K-median algorithms: theory in practice
D Dohan, S Karp, B Matejek
Working paper, Princeton, Computer Science, 2015
Computationally efficient neural network architecture search
DM Dohan, DR So, C Liang, QV Le
US Patent 10,997,503, 2021
Towards learning universal hyperparameter optimizers with transformers
Y Chen, X Song, C Lee, Z Wang, R Zhang, D Dohan, K Kawakami, ...
Advances in Neural Information Processing Systems 35, 32053-32068, 2022
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