| Show, Attend and Tell: Neural Image Caption Generation with Visual Attention K Xu, J Ba, R Kiros, K Cho, A Courville, R Salakhutdinov, R Zemel, ... International Conference on Machine Learning (ICML) 2 (3), 5, 2015 | 12962 | 2015 |
| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ... arXiv preprint arXiv:2312.11805, 2023 | 2070 | 2023 |
| Palm 2 technical report R Anil, AM Dai, O Firat, M Johnson, D Lepikhin, A Passos, S Shakeri, ... arXiv preprint arXiv:2305.10403, 2023 | 1400 | 2023 |
| Theano: A Python framework for fast computation of mathematical expressions R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, N Ballas, ... arXiv e-prints, arXiv: 1605.02688, 2016 | 930 | 2016 |
| Probabilistic model-agnostic meta-learning C Finn*, K Xu*, S Levine Advances in neural information processing systems 31, 2018 | 841 | 2018 |
| An actor-critic algorithm for sequence prediction D Bahdanau, P Brakel, K Xu, A Goyal, R Lowe, J Pineau, A Courville, ... International Conference on Learning Representations (2017), 2016 | 734 | 2016 |
| Meta-dataset: A dataset of datasets for learning to learn from few examples E Triantafillou, T Zhu, V Dumoulin, P Lamblin, U Evci, K Xu, R Goroshin, ... International Conference on Learning Representations (2020), 2019 | 727 | 2019 |
| On using monolingual corpora in neural machine translation C Gulcehre, O Firat, K Xu, K Cho, L Barrault, HC Lin, F Bougares, ... arXiv preprint arXiv:1503.03535, 2015 | 659 | 2015 |
| 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 | 614 | 2024 |
| Bridging the gap between value and policy based reinforcement learning O Nachum, M Norouzi, K Xu, D Schuurmans Advances in neural information processing systems 30, 2017 | 537 | 2017 |
| Theano: A Python framework for fast computation of mathematical expressions TTD Team, R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, ... arXiv preprint arXiv:1605.02688, 2016 | 216 | 2016 |
| Unsupervised perceptual rewards for imitation learning P Sermanet*, K Xu*, S Levine Robotics: Science and Systems (RSS 2017), 2016 | 186 | 2016 |
| Trust-pcl: An off-policy trust region method for continuous control O Nachum, M Norouzi, K Xu, D Schuurmans International Conference on Learning Representations (2018), 2018 | 144 | 2018 |
| On integrating a language model into neural machine translation C Gulcehre, O Firat, K Xu, K Cho, Y Bengio Computer Speech & Language 45, 137-148, 2017 | 134 | 2017 |
| Reset-free reinforcement learning via multi-task learning: Learning dexterous manipulation behaviors without human intervention A Gupta, J Yu, TZ Zhao, V Kumar, A Rovinsky, K Xu, T Devlin, S Levine 2021 IEEE International Conference on Robotics and Automation (ICRA), 6664-6671, 2021 | 101 | 2021 |
| Learning a prior over intent via meta-inverse reinforcement learning K Xu, E Ratner, A Dragan, S Levine, C Finn International conference on machine learning, 6952-6962, 2019 | 84 | 2019 |
| Beyond human data: Scaling self-training for problem-solving with language models A Singh, JD Co-Reyes, R Agarwal, A Anand, P Patil, PJ Liu, J Harrison, ... arXiv preprint arXiv:2312.06585, 2023 | 62 | 2023 |
| Scaling llm test-time compute optimally can be more effective than scaling model parameters C Snell, J Lee, K Xu, A Kumar arXiv preprint arXiv:2408.03314, 2024 | 49 | 2024 |
| Continual Learning of Control Primitives: Skill Discovery via Reset-Games K Xu*, S Verma*, C Finn, S Levine Advances in neural information processing systems, 2020, 2020 | 42 | 2020 |
| Small-scale proxies for large-scale transformer training instabilities M Wortsman, PJ Liu, L Xiao, K Everett, A Alemi, B Adlam, JD Co-Reyes, ... arXiv preprint arXiv:2309.14322, 2023 | 38 | 2023 |
Sergey LevineUC Berkeley, Physical IntelligenceDirección de correo verificada de eecs.berkeley.edu