Search Results for author: Kevin Feigelis

Found 4 papers, 1 papers with code

Counterfactual World Modeling for Physical Dynamics Understanding

no code implementations11 Dec 2023 Rahul Venkatesh, Honglin Chen, Kevin Feigelis, Daniel M. Bear, Khaled Jedoui, Klemen Kotar, Felix Binder, Wanhee Lee, Sherry Liu, Kevin A. Smith, Judith E. Fan, Daniel L. K. Yamins

Third, the counterfactual modeling capability enables the design of counterfactual queries to extract vision structures similar to keypoints, optical flows, and segmentations, which are useful for dynamics understanding.


Unifying (Machine) Vision via Counterfactual World Modeling

no code implementations2 Jun 2023 Daniel M. Bear, Kevin Feigelis, Honglin Chen, Wanhee Lee, Rahul Venkatesh, Klemen Kotar, Alex Durango, Daniel L. K. Yamins

Leading approaches in machine vision employ different architectures for different tasks, trained on costly task-specific labeled datasets.

counterfactual Optical Flow Estimation

Flexible and Efficient Long-Range Planning Through Curious Exploration

no code implementations ICML 2020 Aidan Curtis, Minjian Xin, Dilip Arumugam, Kevin Feigelis, Daniel Yamins

In contrast, deep reinforcement learning (DRL) methods use flexible neural-network-based function approximators to discover policies that generalize naturally to unseen circumstances.

Imitation Learning Model-based Reinforcement Learning +4

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