| 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 | 6995 | 2023 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context G Team, P Georgiev, VI Lei, R Burnell, L Bai, A Gulati, G Tanzer, ... arXiv preprint arXiv:2403.05530, 2024 | 3439 | 2024 |
| Rt-2: Vision-language-action models transfer web knowledge to robotic control B Zitkovich, T Yu, S Xu, P Xu, T Xiao, F Xia, J Wu, P Wohlhart, S Welker, ... Conference on Robot Learning, 2165-2183, 2023 | 2427 | 2023 |
| Gemma: Open models based on gemini research and technology G Team, T Mesnard, C Hardin, R Dadashi, S Bhupatiraju, S Pathak, ... arXiv preprint arXiv:2403.08295, 2024 | 2255 | 2024 |
| Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities G Comanici, E Bieber, M Schaekermann, I Pasupat, N Sachdeva, I Dhillon, ... arXiv preprint arXiv:2507.06261, 2025 | 1337 | 2025 |
| Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration0 A O’Neill, A Rehman, A Maddukuri, A Gupta, A Padalkar, A Lee, A Pooley, ... 2024 IEEE International Conference on Robotics and Automation (ICRA), 6892-6903, 2024 | 919 | 2024 |
| Dual Motion GAN for Future-Flow Embedded Video Prediction X Liang, L Lee, W Dai, EP Xing International Conference on Computer Vision, 2017 | 485 | 2017 |
| Open x-embodiment: Robotic learning datasets and rt-x models A Padalkar, A Pooley, A Jain, A Bewley, A Herzog, A Irpan, A Khazatsky, ... arXiv preprint arXiv:2310.08864 1 (2), 2023 | 438 | 2023 |
| Efficient Exploration via State Marginal Matching L Lee, B Eysenbach, E Parisotto, R Salakhutdinov, S Levine arXiv preprint arXiv:1906.05274, 2019 | 336 | 2019 |
| Multi-game decision transformers KH Lee, O Nachum, MS Yang, L Lee, D Freeman, S Guadarrama, ... Advances in neural information processing systems 35, 27921-27936, 2022 | 328 | 2022 |
| Deep Variation-structured Reinforcement Learning for Visual Relationship and Attribute Detection X Liang, L Lee, E Xing Conference on Computer Vision and Pattern Recognition, 2017 | 320 | 2017 |
| Multimodal masked autoencoders learn transferable representations X Geng, H Liu, L Lee, D Schuurmans, S Levine, P Abbeel arXiv preprint arXiv:2205.14204, 2022 | 144 | 2022 |
| Gated Path Planning Networks L Lee, E Parisotto, D Chaplot, E Xing, R Salakhutdinov International Conference on Machine Learning (ICML), 2018 | 120 | 2018 |
| Open x-embodiment: Robotic learning datasets and rt-x models Q Vuong, S Levine, HR Walke, K Pertsch, A Singh, R Doshi, C Xu, J Luo, ... Towards Generalist Robots: Learning Paradigms for Scalable Skill Acquisition …, 2023 | 105 | 2023 |
| f-irl: Inverse reinforcement learning via state marginal matching T Ni, H Sikchi, Y Wang, T Gupta, L Lee, B Eysenbach Conference on Robot Learning, 529-551, 2021 | 104 | 2021 |
| Decomposing the generalization gap in imitation learning for visual robotic manipulation A Xie, L Lee, T Xiao, C Finn 2024 IEEE International Conference on Robotics and Automation (ICRA), 3153-3160, 2024 | 99 | 2024 |
| Barkour: Benchmarking animal-level agility with quadruped robots K Caluwaerts, A Iscen, JC Kew, W Yu, T Zhang, D Freeman, KH Lee, ... arXiv preprint arXiv:2305.14654, 2023 | 89 | 2023 |
| Rt-2: Vision-language-action models transfer web knowledge to robotic control, 2023 A Brohan, N Brown, J Carbajal, Y Chebotar, X Chen, K Choromanski, ... URL https://arxiv. org/abs/2307.15818, 2024 | 79 | 2024 |
| External vs. internal: An essay on machine learning agents for autonomous database management systems A Pavlo, M Butrovich, A Joshi, L Ma, P Menon, D Van Aken, L Lee, ... IEEE bulletin 42 (2), 2019 | 50 | 2019 |
| Combining LSTM and latent topic modeling for mortality prediction Y Jo, L Lee, S Palaskar arXiv preprint arXiv:1709.02842, 2017 | 48 | 2017 |
Ruslan SalakhutdinovUPMC Professor, Machine Learning Department, CMUVerified email at cs.cmu.edu
