Michael Chang | DeepAI

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∙ 05/26/2023

Im-Promptu: In-Context Composition from Image Prompts

Large language models are few-shot learners that can solve diverse tasks...

0 Bhishma Dedhia, et al. ∙

∙ 03/20/2023

Neural Constraint Satisfaction: Hierarchical Abstraction for Combinatorial Generalization in Object Rearrangement

Object rearrangement is a challenge for embodied agents because solving ...

0 Michael Chang, et al. ∙

∙ 01/18/2023

Human-Timescale Adaptation in an Open-Ended Task Space

Foundation models have shown impressive adaptation and scalability in su...

0 Adaptive Agent Team, et al. ∙

∙ 10/23/2022

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

Using a single camera to estimate the distances of objects reduces costs...

0 Pedram Agand, et al. ∙

∙ 07/02/2022

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

Iterative refinement – start with a random guess, then iteratively impro...

0 Michael Chang, et al. ∙

∙ 05/13/2022

Blockaid: Data Access Policy Enforcement for Web Applications

Modern web applications serve large amounts of sensitive user data, acce...

0 Wen Zhang, et al. ∙

∙ 07/12/2021

Explore and Control with Adversarial Surprise

Reinforcement learning (RL) provides a framework for learning goal-direc...

4 Arnaud Fickinger, et al. ∙

∙ 06/28/2021

Modularity in Reinforcement Learning via Algorithmic Independence in Credit Assignment

Many transfer problems require re-using previously optimal decisions for...

0 Michael Chang, et al. ∙

∙ 07/05/2020

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

This paper seeks to establish a framework for directing a society of sim...

0 Michael Chang, et al. ∙

∙ 10/28/2019

Entity Abstraction in Visual Model-Based Reinforcement Learning

This paper tests the hypothesis that modeling a scene in terms of entiti...

33 Rishi Veerapaneni, et al. ∙

∙ 05/23/2019

MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies

Humans are able to perform a myriad of sophisticated tasks by drawing up...

5 Xue Bin Peng, et al. ∙

∙ 07/18/2018

Representational efficiency outweighs action efficiency in human program induction

The importance of hierarchically structured representations for tractabl...

0 Sophia Sanborn, et al. ∙

∙ 02/28/2018

Relational Neural Expectation Maximization: Unsupervised Discovery of Objects and their Interactions

Common-sense physical reasoning is an essential ingredient for any intel...

0 Sjoerd van Steenkiste, et al. ∙

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