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Found 6 papers, 4 papers with code
How Flawed is ECE? An Analysis via Logit Smoothing
Informally, a model is calibrated if its predictions are correct with a probability that matches the confidence of the prediction.
For Better or For Worse? Learning Minimum Variance Features With Label Augmentation
Data augmentation has been pivotal in successfully training deep learning models on classification tasks over the past decade.
On the Limitations of Temperature Scaling for Distributions with Overlaps
Despite the impressive generalization capabilities of deep neural networks, they have been repeatedly shown to be overconfident when they are wrong.
Hiding Data Helps: On the Benefits of Masking for Sparse Coding
Furthermore, drawing from the growing body of work on self-supervised learning, we propose a novel masking objective for which recovering the ground-truth dictionary is in fact optimal as the signal increases for a large class of data-generating processes.
Provably Learning Diverse Features in Multi-View Data with Midpoint Mixup
Mixup is a data augmentation technique that relies on training using random convex combinations of data points and their labels.
Towards Understanding the Data Dependency of Mixup-style Training
Despite seeing very few true data points during training, models trained using Mixup seem to still minimize the original empirical risk and exhibit better generalization and robustness on various tasks when compared to standard training.
