| 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 | 3181 | 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 | 1151 | 2024 |
| 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 | 1012 | 2024 |
| Rt-2: Vision-language-action models transfer web knowledge to robotic control A Brohan, N Brown, J Carbajal, Y Chebotar, X Chen, K Choromanski, ... arXiv preprint arXiv:2307.15818, 2023 | 807 | 2023 |
| Open x-embodiment: Robotic learning datasets and rt-x models JJ Lim IEEE International Conference on Robotics and Automation, 2024 | 467* | 2024 |
| Dual Motion GAN for Future-Flow Embedded Video Prediction X Liang, L Lee, W Dai, EP Xing International Conference on Computer Vision, 2017 | 458 | 2017 |
| 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 | 310 | 2017 |
| Efficient Exploration via State Marginal Matching L Lee, B Eysenbach, E Parisotto, R Salakhutdinov, S Levine arXiv preprint arXiv:1906.05274, 2019 | 297 | 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 | 244 | 2022 |
| 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 | 213 | 2023 |
| Multimodal masked autoencoders learn transferable representations X Geng, H Liu, L Lee, D Schuurmans, S Levine, P Abbeel arXiv preprint arXiv:2205.14204, 2022 | 110 | 2022 |
| Gated Path Planning Networks L Lee, E Parisotto, D Chaplot, E Xing, R Salakhutdinov International Conference on Machine Learning (ICML), 2018 | 110 | 2018 |
| 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 | 82 | 2021 |
| 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 | 53 | 2023 |
| 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 | 47 | 2019 |
| 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 | 46 | 2024 |
| Combining LSTM and latent topic modeling for mortality prediction Y Jo, L Lee, S Palaskar arXiv preprint arXiv:1709.02842, 2017 | 45 | 2017 |
| Instruction-following agents with jointly pre-trained vision-language models H Liu, L Lee, K Lee, P Abbeel | 38 | 2022 |
| Embodied Multimodal Multitask Learning DS Chaplot, L Lee, R Salakhutdinov, D Parikh, D Batra arXiv preprint arXiv:1902.01385, 2019 | 38 | 2019 |
| 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 | 33 | 2023 |
Ruslan SalakhutdinovUPMC Professor, Machine Learning Department, CMUVerifierad e-postadress på cs.cmu.edu