| Adam: A method for stochastic optimization D Kingma, J Ba International Conference on Learning Representations, 2015 | 88114 | 2015 |
| Show, attend and tell: Neural image caption generation with visual attention K Xu, J Ba, R Kiros, K Cho, A Courville, R Salakhudinov, R Zemel, ... International conference on machine learning, 2048-2057, 2015 | 7824 | 2015 |
| Layer normalization J Ba, JR Kiros, GE Hinton Advances in NIPS 2016 Deep Learning Symposium, arXiv preprint arXiv:1607.06450, 2016 | 3950 | 2016 |
| Do deep nets really need to be deep? J Ba, R Caruana Advances in neural information processing systems, 2654-2662, 2014 | 1622 | 2014 |
| Multiple object recognition with visual attention J Ba, V Mnih, K Kavukcuoglu International Conference on Learning Representations, 2015 | 944 | 2015 |
| Scalable trust-region method for deep reinforcement learning using kronecker-factored approximation Y Wu, E Mansimov, RB Grosse, S Liao, J Ba Advances in neural information processing systems, 5279-5288, 2017 | 479 | 2017 |
| Actor-mimic: Deep multitask and transfer reinforcement learning E Parisotto, J Ba, R Salakhutdinov International Conference on Learning Representations, arXiv preprint arXiv …, 2016 | 419 | 2016 |
| Predicting deep zero-shot convolutional neural networks using textual descriptions J Ba, K Swersky, S Fidler Proceedings of the IEEE International Conference on Computer Vision, 4247-4255, 2015 | 371 | 2015 |
| Classifying and segmenting microscopy images with deep multiple instance learning OZ Kraus, JL Ba, BJ Frey Bioinformatics 32 (12), i52-i59, 2016 | 316 | 2016 |
| Adaptive dropout for training deep neural networks J Ba, B Frey Advances in neural information processing systems, 3084-3092, 2013 | 315 | 2013 |
| Generating images from captions with attention E Mansimov, E Parisotto, J Ba, R Salakhutdinov International Conference on Learning Representations, arXiv preprint arXiv …, 2016 | 301 | 2016 |
| Lookahead Optimizer: k steps forward, 1 step back M Zhang, J Lucas, GE Hinton, J Ba Advances in Neural Information Processing Systems, 9593-9604, 2019 | 288 | 2019 |
| Dream to Control: Learning Behaviors by Latent Imagination D Hafner, T Lillicrap, J Ba, M Norouzi International Conference on Learning Representations, 2019 | 259 | 2019 |
| Automated analysis of high‐content microscopy data with deep learning OZ Kraus, BT Grys, J Ba, Y Chong, BJ Frey, C Boone, BJ Andrews Molecular systems biology 13 (4), 924, 2017 | 183 | 2017 |
| Benchmarking Model-Based Reinforcement Learning T Wang, X Bao, I Clavera, J Hoang, Y Wen, E Langlois, S Zhang, G Zhang, ... arXiv preprint arXiv:1907.02057, 2019 | 178* | 2019 |
| Adam: A method for stochastic gradient descent DP Kingma, JL Ba ICLR: International Conference on Learning Representations, 1-15, 2015 | 167 | 2015 |
| Using fast weights to attend to the recent past J Ba, GE Hinton, V Mnih, JZ Leibo, C Ionescu Advances in Neural Information Processing Systems, 4331-4339, 2016 | 151 | 2016 |
| Flipout: Efficient pseudo-independent weight perturbations on mini-batches Y Wen, P Vicol, J Ba, D Tran, R Grosse International Conference on Learning Representations, 2018 | 142 | 2018 |
| Nervenet: Learning structured policy with graph neural networks T Wang, R Liao, J Ba, S Fidler International Conference on Learning Representations, 2018 | 117 | 2018 |
| Adam: a method for stochastic optimization. CoRR DP Kingma, J Ba arXiv preprint arXiv:1412.6980, 2014 | 94 | 2014 |
Diederik P. KingmaResearch Scientist, Google BrainAdresse e-mail validée de google.com
