Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ... arXiv preprint arXiv:2312.11805, 2023 | 971 | 2023 |
A Compositional Object-Based Approach To Learning Physical Dynamics MB Chang, T Ullman, A Torralba, JB Tenenbaum International Conference on Learning Representations 5, 2016 | 485 | 2016 |
Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions S van Steenkiste, M Chang, K Greff, J Schmidhuber International Conference on Learning Representations 6, 2018 | 308 | 2018 |
Entity Abstraction in Visual Model-Based Reinforcement Learning R Veerapaneni*, JD Co-Reyes*, M Chang*, M Janner, C Finn, J Wu, ... Conference on Robot Learning, 2019 | 209 | 2019 |
Mcp: Learning composable hierarchical control with multiplicative compositional policies XB Peng, M Chang, G Zhang, P Abbeel, S Levine Advances in neural information processing systems 32, 2019 | 201 | 2019 |
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 | 174 | 2024 |
Doing more with less: Meta-reasoning and meta-learning in humans and machines TL Griffiths, F Callaway, MB Chang, E Grant, PM Krueger, F Lieder Current Opinion in Behavioral Sciences 29, 24-30, 2019 | 132 | 2019 |
Automatically composing representation transformations as a means for generalization MB Chang, A Gupta, S Levine, TL Griffiths International Conference on Learning Representations 7, 2018 | 85 | 2018 |
Understanding visual concepts with continuation learning WF Whitney, M Chang, T Kulkarni, JB Tenenbaum arXiv preprint arXiv:1602.06822, 2016 | 46 | 2016 |
Object representations as fixed points: Training iterative refinement algorithms with implicit differentiation M Chang, T Griffiths, S Levine Advances in Neural Information Processing Systems 35, 32694-32708, 2022 | 41 | 2022 |
Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions M Chang, S Kaushik, SM Weinberg, TL Griffiths, S Levine International Conference on Machine Learning 37, 2020 | 14 | 2020 |
Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment M Chang, S Kaushik, S Levine, TL Griffiths International Conference on Machine Learning 139, 1452-1462, 2021 | 10 | 2021 |
Representational efficiency outweighs action efficiency in human program induction S Sanborn, DD Bourgin, M Chang, TL Griffiths Annual Meeting of the Cognitive Science Society (CogSci), 2018 | 10 | 2018 |
Explore and control with adversarial surprise A Fickinger, N Jaques, S Parajuli, M Chang, N Rhinehart, G Berseth, ... arXiv preprint arXiv:2107.07394, 2021 | 9 | 2021 |
Neural constraint satisfaction: Hierarchical abstraction for combinatorial generalization in object rearrangement M Chang, AL Dayan, F Meier, TL Griffiths, S Levine, A Zhang arXiv preprint arXiv:2303.11373, 2023 | 3 | 2023 |
Im-promptu: in-context composition from image prompts B Dedhia, M Chang, J Snell, T Griffiths, N Jha Advances in Neural Information Processing Systems 36, 2024 | 1 | 2024 |