Search Results for author:
Found 3 papers, 1 papers with code
Integral Probability Metrics PAC-Bayes Bounds
We present a PAC-Bayes-style generalization bound which enables the replacement of the KL-divergence with a variety of Integral Probability Metrics (IPM).
Generalization Bounds For Unsupervised and Semi-Supervised Learning With Autoencoders
Autoencoders are widely used for unsupervised learning and as a regularization scheme in semi-supervised learning.
Joint autoencoders: a flexible meta-learning framework
Ideally one would like to allow both the data for the current task and for previous related tasks to self-organize the learning system in such a way that commonalities and differences between the tasks are learned in a data-driven fashion.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
