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Found 3 papers, 2 papers with code
Can Transformers Learn Optimal Filtering for Unknown Systems?
Particularly, we train the transformer using various distinct systems and then evaluate the performance on unseen systems with unknown dynamics.
Koopman-inspired Implicit Backward Reachable Sets for Unknown Nonlinear Systems
Koopman liftings have been successfully used to learn high dimensional linear approximations for autonomous systems for prediction purposes, or for control systems for leveraging linear control techniques to control nonlinear dynamics.
EFE: End-to-end Frame-to-Gaze Estimation
In this paper, we propose a frame-to-gaze network that directly predicts both 3D gaze origin and 3D gaze direction from the raw frame out of the camera without any face or eye cropping.
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