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Found 4 papers, 1 papers with code
Anomaly Detection with Test Time Augmentation and Consistency Evaluation
Deep neural networks are known to be vulnerable to unseen data: they may wrongly assign high confidence stcores to out-distribuion samples.
EVBattery: A Large-Scale Electric Vehicle Dataset for Battery Health and Capacity Estimation
In addition to demonstrating how existing deep learning algorithms can be applied to this task, we further develop an algorithm that exploits the data structure of battery systems.
Can Pretext-Based Self-Supervised Learning Be Boosted by Downstream Data? A Theoretical Analysis
Pretext-based self-supervised learning learns the semantic representation via a handcrafted pretext task over unlabeled data and then uses the learned representation for downstream tasks, which effectively reduces the sample complexity of downstream tasks under Conditional Independence (CI) condition.
Asymmetric Valleys: Beyond Sharp and Flat Local Minima
Specifically, at a local minimum there exist many asymmetric directions such that the loss increases abruptly along one side, and slowly along the opposite side--we formally define such minima as asymmetric valleys.
