Michael Chang

Affiliations:
  • University of California Berkeley, Department of Electrical Engineering and Computer Science, CA, USA
  • Massachusetts Institute of Technology, Department of Brain and Cognitive Science, Cambridge, MA, USA


According to our database1, Michael Chang authored at least 14 papers between 2016 and 2023.

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Bibliography

2023
Neural Software Abstractions
PhD thesis, 2023

Neural Constraint Satisfaction: Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement.
CoRR, 2023

Im-Promptu: In-Context Composition from Image Prompts.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022
Object Representations as Fixed Points: Training Iterative Refinement Algorithms with Implicit Differentiation.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021
Explore and Control with Adversarial Surprise.
CoRR, 2021

Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment.
Proceedings of the 38th International Conference on Machine Learning, 2021

2020
Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions.
Proceedings of the 37th International Conference on Machine Learning, 2020

2019
MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Automatically Composing Representation Transformations as a Means for Generalization.
Proceedings of the 7th International Conference on Learning Representations, 2019

Entity Abstraction in Visual Model-Based Reinforcement Learning.
Proceedings of the 3rd Annual Conference on Robot Learning, 2019

2018
Representational efficiency outweighs action efficiency in human program induction.
Proceedings of the 40th Annual Meeting of the Cognitive Science Society, 2018

2017
A Compositional Object-Based Approach to Learning Physical Dynamics.
Proceedings of the 5th International Conference on Learning Representations, 2017

2016
Understanding Visual Concepts with Continuation Learning.
CoRR, 2016


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