| Evaluating prerequisite qualities for learning end-to-end dialog systems J Dodge, A Gane, X Zhang, A Bordes, S Chopra, AH Miller, A Szlam, ... International Conference on Learning Representations, 2016 | 158 | 2016 |
| Rethinking attention with performers K Choromanski, V Likhosherstov, D Dohan, X Song, A Gane, T Sarlos, ... International Conference on Learning Representations, 2020 | 38 | 2020 |
| The variational homoencoder: Learning to learn high capacity generative models from few examples LB Hewitt, MI Nye, A Gane, T Jaakkola, JB Tenenbaum Uncertainty in Artificial Intelligence, 2018 | 33 | 2018 |
| Learning with maximum a-posteriori perturbation models A Gane, T Hazan, T Jaakkola Artificial Intelligence and Statistics, 247-256, 2014 | 27 | 2014 |
| Direct Optimization through for Discrete Variational Auto-Encoder G Lorberbom, A Gane, T Jaakkola, T Hazan Conference on Neural Information Processing Systems, 2018 | 20 | 2018 |
| 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 | 7 | 2020 |
| 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, 2020 | 6 | 2020 |
| A Comparison of Generative Models for Sequence Design A Gane, B Alipanahi, C Angermueller, D Belanger, D Dohan, L Colwell, ... Machine Learning in Computational Biology Workshop, 2019 | 1 | 2019 |
| Perturbations, Optimization, and Statistics AL Yuille, R Adams, RS Zemel, Y Chen, M Welling, A Gane, T Jaakkola, ... MIT Press, 2017 | | 2017 |
