MULTI-VIEW LEARNING
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.
Source: Dissimilarity-based representation for radiomics applications
Benchmarks
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Libraries
Use these libraries to find MULTI-VIEW LEARNING models and implementations
CarFusion
Most implemented papers
Neural News Recommendation with Attentive Multi-View Learning
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.
Trusted Multi-View Classification
To this end, we propose a novel multi-view classification method, termed trusted multi-view classification, which provides a new paradigm for multi-view learning by dynamically integrating different views at an evidence level.
Tensor Canonical Correlation Analysis for Multi-view Dimension Reduction
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.
Variational Distillation for Multi-View Learning
Information Bottleneck (IB) based multi-view learning provides an information theoretic principle for seeking shared information contained in heterogeneous data descriptions.
Learning Autoencoders with Relational Regularization
HongtengXu/Relational-AutoEncoders
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ICML 2020
A new algorithmic framework is proposed for learning autoencoders of data distributions.
Deep brain state classification of MEG data
SMehrkanoon/Deep-brain-state-classification-of-MEG-data
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The experimental results of cross subject multi-class classification on the studied MEG dataset show that the inclusion of attention improves the generalization of the models across subjects.
Farewell to Mutual Information: Variational Distillation for Cross-Modal Person Re-Identification
FutabaSakuraXD/Farewell-to-Mutual-Information-Variational-Distiilation-for-Cross-Modal-Person-Re-identification
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CVPR 2021
The Information Bottleneck (IB) provides an information theoretic principle for representation learning, by retaining all information relevant for predicting label while minimizing the redundancy.
Conditional Random Field Autoencoders for Unsupervised Structured Prediction
We introduce a framework for unsupervised learning of structured predictors with overlapping, global features.
Patterns for Learning with Side Information
Supervised, semi-supervised, and unsupervised learning estimate a function given input/output samples.
A Survey on Multi-Task Learning
Multi-Task Learning (MTL) is a learning paradigm in machine learning and its aim is to leverage useful information contained in multiple related tasks to help improve the generalization performance of all the tasks.
