| Adam: A method for stochastic optimization D Kingma, J Ba International Conference on Learning Representations, 2015 | 195915 | 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 | 12742 | 2015 |
| Layer normalization J Ba, JR Kiros, GE Hinton Advances in NIPS 2016 Deep Learning Symposium, arXiv preprint arXiv:1607.06450, 2016 | 12681 | 2016 |
| Do deep nets really need to be deep? J Ba, R Caruana Advances in neural information processing systems, 2654-2662, 2014 | 2644 | 2014 |
| Dream to Control: Learning Behaviors by Latent Imagination D Hafner, T Lillicrap, J Ba, M Norouzi International Conference on Learning Representations, 2019 | 1302 | 2019 |
| Multiple object recognition with visual attention J Ba, V Mnih, K Kavukcuoglu International Conference on Learning Representations, 2015 | 1288 | 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 | 825 | 2017 |
| Mastering atari with discrete world models D Hafner, T Lillicrap, M Norouzi, J Ba International Conference on Learning Representations, 2020 | 800 | 2020 |
| 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 | 794 | 2019 |
| Large language models are human-level prompt engineers Y Zhou, AI Muresanu, Z Han, K Paster, S Pitis, H Chan, J Ba International Conference on Learning Representations, 2022 | 712 | 2022 |
| Actor-mimic: Deep multitask and transfer reinforcement learning E Parisotto, J Ba, R Salakhutdinov International Conference on Learning Representations, arXiv preprint arXiv …, 2016 | 684 | 2016 |
| Generating images from captions with attention E Mansimov, E Parisotto, J Ba, R Salakhutdinov International Conference on Learning Representations, arXiv preprint arXiv …, 2016 | 558 | 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 | 523 | 2015 |
| 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 | 518* | 2019 |
| Classifying and segmenting microscopy images with deep multiple instance learning OZ Kraus, JL Ba, BJ Frey Bioinformatics 32 (12), i52-i59, 2016 | 497 | 2016 |
| Adaptive dropout for training deep neural networks J Ba, B Frey Advances in neural information processing systems, 3084-3092, 2013 | 490 | 2013 |
| Layer normalization. arXiv JL Ba, JR Kiros, GE Hinton arXiv preprint arXiv:1607.06450 10, 2016 | 472 | 2016 |
| BatchEnsemble: an Alternative Approach to Efficient Ensemble and Lifelong Learning Y Wen, D Tran, J Ba International Conference on Learning Representations, https://openreview.net …, 2020 | 469 | 2020 |
| Mastering diverse domains through world models D Hafner, J Pasukonis, J Ba, T Lillicrap arXiv preprint arXiv:2301.04104, 2023 | 383 | 2023 |
| 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 | 379 | 2018 |
Diederik P. KingmaResearch Scientist, Google BrainGeverifieerd e-mailadres voor google.com
