Search Results for author:
Found 2 papers, 0 papers with code
Group-disentangled Representation Learning with Weakly-Supervised Regularization
Learning interpretable and human-controllable representations that uncover factors of variation in data remains an ongoing key challenge in representation learning.
Learning Task-Relevant Features via Contrastive Input Morphing
A fundamental challenge in artificial intelligence is learning useful representations of data that yield good performance on a downstream classification task, without overfitting to spurious input features.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
