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Found 3 papers, 2 papers with code
A Self-Organizing Clustering System for Unsupervised Distribution Shift Detection
Modeling non-stationary data is a challenging problem in the field of continual learning, and data distribution shifts may result in negative consequences on the performance of a machine learning model.
Pantypes: Diverse Representatives for Self-Explainable Models
Prototypical self-explainable classifiers have emerged to meet the growing demand for interpretable AI systems.
Deep learning for Chemometric and non-translational data
We propose a novel method to train deep convolutional neural networks which learn from multiple data sets of varying input sizes through weight sharing.
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