46 papers with code • 0 benchmarks • 1 datasets
Multi-View Learning is a machine learning framework where data are represented by multiple distinct feature groups, and each feature group is referred to as a particular view.
These leaderboards are used to track progress in MULTI-VIEW LEARNING
LibrariesUse these libraries to find MULTI-VIEW LEARNING models and implementations
In the user encoder we learn the representations of users based on their browsed news and apply attention mechanism to select informative news for user representation learning.
As a consequence, the high order correlation information contained in the different views is explored and thus a more reliable common subspace shared by all features can be obtained.
The Information Bottleneck (IB) provides an information theoretic principle for representation learning, by retaining all information relevant for predicting label while minimizing the redundancy.
We introduce a framework for unsupervised learning of structured predictors with overlapping, global features.