David Dohan
David Dohan
Google Brain
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Cited by
Cited by
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
Rethinking attention with performers
K Choromanski, V Likhosherstov, D Dohan, X Song, A Gane, T Sarlos, ...
International Conference on Learning Representations, 2021
Palm: Scaling language modeling with pathways
A Chowdhery, S Narang, J Devlin, M Bosma, G Mishra, A Roberts, ...
arXiv preprint arXiv:2204.02311, 2022
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
Model-based reinforcement learning for biological sequence design
C Angermueller, D Dohan, D Belanger, R Deshpande, K Murphy, ...
International conference on learning representations, 2019
Learning hierarchical semantic segmentations of LIDAR data
D Dohan, B Matejek, T Funkhouser
2015 International Conference on 3D Vision, 273-281, 2015
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
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
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
Amortized bayesian optimization over discrete spaces
K Swersky, Y Rubanova, D Dohan, K Murphy
Conference on Uncertainty in Artificial Intelligence, 769-778, 2020
Is transfer learning necessary for protein landscape prediction?
A Shanehsazzadeh, D Belanger, D Dohan
NeurIPS workshop on Machine Learning in Structural Biology, 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
K-median algorithms: theory in practice
D Dohan, S Karp, B Matejek
Working paper, Princeton, Computer Science, 2015
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
Improving protein function annotation via unsupervised pre-training: Robustness, efficiency, and insights
D Dohan, A Gane, ML Bileschi, D Belanger, L Colwell
Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2021
Exploring neural architecture search for language tasks
MT Luong, D Dohan, AW Yu, QV Le, B Zoph, V Vasudevan
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
A comparison of generative models for sequence design
A Gane, D Belanger, D Dohan, C Angermueller, R Deshpande, S Vora, ...
Biological Sequences Design using Batched Bayesian Optimization
D Belanger, S Vora, Z Mariet, R Deshpande, D Dohan, C Angermueller, ...
NeurIPS workshop on Bayesian Deep Learning, 2019
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