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Found 4 papers, 3 papers with code
Navigating Complexity: Toward Lossless Graph Condensation via Expanding Window Matching
Specifically, we employ a curriculum learning strategy to train expert trajectories with more diverse supervision signals from the original graph, and then effectively transfer the information into the condensed graph with expanding window matching.
Towards Lossless Dataset Distillation via Difficulty-Aligned Trajectory Matching
The ultimate goal of Dataset Distillation is to synthesize a small synthetic dataset such that a model trained on this synthetic set will perform equally well as a model trained on the full, real dataset.
Class Attention Transfer Based Knowledge Distillation
Previous knowledge distillation methods have shown their impressive performance on model compression tasks, however, it is hard to explain how the knowledge they transferred helps to improve the performance of the student network.
Guaranteed Nonlinear Tracking in the Presence of DNN-Learned Dynamics With Contraction Metrics and Disturbance Estimation
This paper presents an approach to trajectory-centric learning control based on contraction metrics and disturbance estimation for nonlinear systems subject to matched uncertainties.
