| 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 | 1361 | 2024 |
| Visual dialog A Das, S Kottur, K Gupta, A Singh, D Yadav, JMF Moura, D Parikh, ... Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017 | 1231 | 2017 |
| Recurrent neural networks for driver activity anticipation via sensory-fusion architecture A Jain, A Singh, HS Koppula, S Soh, A Saxena 2016 IEEE International Conference on Robotics and Automation (ICRA), 3118-3125, 2016 | 354 | 2016 |
| End-to-End Robotic Reinforcement Learning without Reward Engineering A Singh, L Yang, K Hartikainen, C Finn, S Levine RSS 2019, 2019 | 329 | 2019 |
| The Ingredients of Real-World Robotic Reinforcement Learning H Zhu, J Yu, A Gupta, D Shah, K Hartikainen, A Singh, V Kumar, S Levine International Conference on Learning Representations (ICLR), 2020 | 217 | 2020 |
| Parrot: Data-Driven Behavioral Priors for Reinforcement Learning A Singh, H Liu, G Zhou, A Yu, N Rhinehart, S Levine International Conference on Learning Representations (ICLR), 2020 | 163 | 2020 |
| Variational Inverse Control with Events: A General Framework for Data-Driven Reward Definition J Fu, A Singh, D Ghosh, L Yang, S Levine NIPS 2018, 2018 | 154 | 2018 |
| Brain4Cars: Car That Knows Before You Do via Sensory-Fusion Deep Learning Architecture A Jain, HS Koppula, S Soh, B Raghavan, A Singh, A Saxena IJRR, 2016 | 150 | 2016 |
| Divide-and-Conquer Reinforcement Learning D Ghosh, A Singh, A Rajeswaran, V Kumar, S Levine ICLR 2018, 2017 | 131 | 2017 |
| COG: Connecting New Skills to Past Experience with Offline Reinforcement Learning A Singh, A Yu, J Yang, J Zhang, A Kumar, S Levine Conference on Robot Learning (CoRL), 2020 | 116 | 2020 |
| 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 | 108 | 2023 |
| Many-Shot In-Context Learning R Agarwal, A Singh, LM Zhang, B Bohnet, S Chan, A Anand, Z Abbas, ... arXiv preprint arXiv:2404.11018, 2024 | 106 | 2024 |
| Few-Shot Goal Inference for Visuomotor Learning and Planning A Xie, A Singh, S Levine, C Finn CoRL 2018, 2018 | 89 | 2018 |
| i-sim2real: Reinforcement learning of robotic policies in tight human-robot interaction loops SW Abeyruwan, L Graesser, DB D’Ambrosio, A Singh, A Shankar, ... Conference on Robot Learning, 212-224, 2023 | 59 | 2023 |
| Scalable multi-task imitation learning with autonomous improvement A Singh, E Jang, A Irpan, D Kappler, M Dalal, S Levinev, M Khansari, ... 2020 IEEE International Conference on Robotics and Automation (ICRA), 2167-2173, 2020 | 45 | 2020 |
| Don’t start from scratch: Leveraging prior data to automate robotic reinforcement learning HR Walke, JH Yang, A Yu, A Kumar, J Orbik, A Singh, S Levine Conference on Robot Learning, 1652-1662, 2023 | 42 | 2023 |
| Improving Large Language Model Fine-tuning for Solving Math Problems Y Liu, A Singh, CD Freeman, JD Co-Reyes, PJ Liu arXiv preprint arXiv:2310.10047, 2023 | 40 | 2023 |
| GPLAC: Generalizing Vision-Based Robotic Skills using Weakly Labeled Images A Singh, L Yang, S Levine ICCV 2017, 2017 | 29 | 2017 |
| Visual backtracking teleoperation: A data collection protocol for offline image-based reinforcement learning D Brandfonbrener, S Tu, A Singh, S Welker, C Boodoo, N Matni, J Varley 2023 IEEE International Conference on Robotics and Automation (ICRA), 11336 …, 2023 | 10 | 2023 |
Sergey LevineUC Berkeley, Physical IntelligenceBestätigte E-Mail-Adresse bei eecs.berkeley.edu
