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Found 4 papers, 1 papers with code
Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts
In this paper, we discover that there exist cases with distribution shifts unobservable in the time domain while observable in the spectral domain, and propose to study distribution shifts on dynamic graphs in the spectral domain for the first time.
PIAT: Parameter Interpolation based Adversarial Training for Image Classification
Besides, we suggest to use the Normalized Mean Square Error (NMSE) to further improve the robustness by aligning the clean and adversarial examples.
Compare learning: bi-attention network for few-shot learning
Learning with few labeled data is a key challenge for visual recognition, as deep neural networks tend to overfit using a few samples only.
InfoBehavior: Self-supervised Representation Learning for Ultra-long Behavior Sequence via Hierarchical Grouping
Typically, the risk can be identified by jointly considering product content (e. g., title and image) and seller behavior.
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