Search Results for author: Kevin Feigelis

Found 5 papers, 1 papers with code

Self-Supervised Learning of Motion Concepts by Optimizing Counterfactuals

no code implementations25 Mar 2025 Stefan Stojanov, David Wendt, Seungwoo Kim, Rahul Venkatesh, Kevin Feigelis, Jiajun Wu, Daniel LK Yamins

Estimating motion in videos is an essential computer vision problem with many downstream applications, including controllable video generation and robotics.

counterfactual Motion Estimation +3

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.

Deep Reinforcement Learning Imitation Learning +5

Cannot find the paper you are looking for? You can Submit a new open access paper.