Search Results for author: Honglin Chen

Found 6 papers, 2 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

Implicit Neural Spatial Representations for Time-dependent PDEs

no code implementations30 Sep 2022 Honglin Chen, Rundi Wu, Eitan Grinspun, Changxi Zheng, Peter Yichen Chen

Whereas classical solvers can dynamically adapt their spatial representation only by resorting to complex remeshing algorithms, our INSR approach is intrinsically adaptive.

Contact mechanics

Semi-Supervised First-Person Activity Recognition in Body-Worn Video

no code implementations19 Apr 2019 Honglin Chen, Hao Li, Alexander Song, Matt Haberland, Osman Akar, Adam Dhillon, Tiankuang Zhou, Andrea L. Bertozzi, P. Jeffrey Brantingham

Body-worn cameras are now commonly used for logging daily life, sports, and law enforcement activities, creating a large volume of archived footage.

Activity Recognition

Biologically-plausible learning algorithms can scale to large datasets

2 code implementations ICLR 2019 Will Xiao, Honglin Chen, Qianli Liao, Tomaso Poggio

These results complement the study by Bartunov et al. (2018), and establish a new benchmark for future biologically plausible learning algorithms on more difficult datasets and more complex architectures.

Biologically-plausible Training

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