| Image transformer N Parmar, A Vaswani, J Uszkoreit, L Kaiser, N Shazeer, A Ku, D Tran International Conference on Machine Learning, 4055-4064, 2018 | 2004 | 2018 |
| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ... arXiv preprint arXiv:2312.11805, 2023 | 1583 | 2023 |
| Automatic differentiation variational inference A Kucukelbir, D Tran, R Ranganath, A Gelman, DM Blei The Journal of Machine Learning Research 18 (1), 430-474, 2017 | 1149* | 2017 |
| Tensorflow distributions JV Dillon, I Langmore, D Tran, E Brevdo, S Vasudevan, D Moore, B Patton, ... arXiv preprint arXiv:1711.10604, 2017 | 651 | 2017 |
| Tensor2tensor for neural machine translation A Vaswani, S Bengio, E Brevdo, F Chollet, AN Gomez, S Gouws, L Jones, ... arXiv preprint arXiv:1803.07416, 2018 | 642 | 2018 |
| Measuring Calibration in Deep Learning. J Nixon, MW Dusenberry, L Zhang, G Jerfel, D Tran CVPR workshops 2 (7), 2019 | 482 | 2019 |
| BatchEnsemble: An alternative approach to efficient ensemble and lifelong learning Y Wen, D Tran, J Ba International Conference on Learning Representations, 2020 | 475 | 2020 |
| Simple and principled uncertainty estimation with deterministic deep learning via distance awareness J Liu, Z Lin, S Padhy, D Tran, T Bedrax Weiss, B Lakshminarayanan Advances in neural information processing systems 33, 7498-7512, 2020 | 469 | 2020 |
| Mesh-TensorFlow: Deep learning for supercomputers N Shazeer, Y Cheng, N Parmar, D Tran, A Vaswani, P Koanantakool, ... Neural Information Processing Systems, 2018 | 410 | 2018 |
| Scaling vision transformers to 22 billion parameters M Dehghani, J Djolonga, B Mustafa, P Padlewski, J Heek, J Gilmer, ... International Conference on Machine Learning, 7480-7512, 2023 | 397 | 2023 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context M Reid, N Savinov, D Teplyashin, D Lepikhin, T Lillicrap, J Alayrac, ... arXiv preprint arXiv:2403.05530, 2024 | 395 | 2024 |
| Hierarchical variational models R Ranganath, D Tran, D Blei International conference on machine learning, 324-333, 2016 | 388 | 2016 |
| Flipout: Efficient pseudo-independent weight perturbations on mini-batches Y Wen, P Vicol, J Ba, D Tran, R Grosse arXiv preprint arXiv:1803.04386, 2018 | 379 | 2018 |
| Edward: A library for probabilistic modeling, inference, and criticism D Tran, A Kucukelbir, AB Dieng, M Rudolph, D Liang, DM Blei arXiv preprint arXiv:1610.09787, 2016 | 370 | 2016 |
| Operator variational inference R Ranganath, D Tran, J Altosaar, D Blei Advances in Neural Information Processing Systems 29, 2016 | 367* | 2016 |
| Hierarchical implicit models and likelihood-free variational inference D Tran, R Ranganath, DM Blei Neural Information Processing Systems, 2017 | 362* | 2017 |
| Revisiting the calibration of modern neural networks M Minderer, J Djolonga, R Romijnders, F Hubis, X Zhai, N Houlsby, ... Advances in Neural Information Processing Systems 34, 15682-15694, 2021 | 317 | 2021 |
| Deep probabilistic programming D Tran, MD Hoffman, RA Saurous, E Brevdo, K Murphy, DM Blei International Conference on Learning Representations, 2017 | 243 | 2017 |
| Hyperparameter ensembles for robustness and uncertainty quantification F Wenzel, J Snoek, D Tran, R Jenatton Advances in Neural Information Processing Systems 33, 6514-6527, 2020 | 242 | 2020 |
| Efficient and scalable Bayesian neural nets with rank-1 factors M Dusenberry, G Jerfel, Y Wen, Y Ma, J Snoek, K Heller, ... International Conference on Machine Learning, 2782-2792, 2020 | 238 | 2020 |
David BleiProfessor of Statistics and Computer Science, Columbia University確認したメール アドレス: columbia.edu
