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Michael Chang

DMODE: Differential Monocular Object Distance Estimation Module without Class Specific Information

Oct 23, 2022
Pedram Agand, Michael Chang, Mo Chen

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* 9 pages, 3 figures, 3 tables

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Object Representations as Fixed Points: Training Iterative Refinement Algorithms with Implicit Differentiation

Jul 02, 2022
Michael Chang, Thomas L. Griffiths, Sergey Levine

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* 15 pages, 10 figures, Oral Presentation at the ICLR 2022 Workshop on Elements of Reasoning: Objects, Structure, and Causality

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Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment

Jul 21, 2021
Michael Chang, Sidhant Kaushik, Sergey Levine, Thomas L. Griffiths

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* Long Presentation at the Thirty-eighth International Conference on Machine Learning (ICML) 2021. 21 pages, 11 figures. v2: updated acknowledgments. v3: clarified that the internal function nodes of the credit assignment mechanism are not considered O(1)

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Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions

Jul 05, 2020
Michael Chang, Sidhant Kaushik, S. Matthew Weinberg, Thomas L. Griffiths, Sergey Levine

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* 17 pages, 12 figures, accepted to the International Conference on Machine Learning (ICML) 2020

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Entity Abstraction in Visual Model-Based Reinforcement Learning

Dec 04, 2019
Rishi Veerapaneni, John D. Co-Reyes, Michael Chang, Michael Janner, Chelsea Finn, Jiajun Wu, Joshua B. Tenenbaum, Sergey Levine

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* Accepted at CoRL 2019

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