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MULTI-VIEW LEARNING

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

Papers

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Showing 151–160 of 256 papers

TitleStatusHype
Scalable and Effective Deep CCA via Soft Decorrelation—0
Scalable Multi-view Clustering via Explicit Kernel Features Maps—0
scICML: Information-theoretic Co-clustering-based Multi-view Learning for the Integrative Analysis of Single-cell Multi-omics data—0
SE(3) Equivariant Ray Embeddings for Implicit Multi-View Depth Estimation—0
Self-attention Multi-view Representation Learning with Diversity-promoting Complementarity—0
Self-boosted Time-series Forecasting with Multi-task and Multi-view Learning—0
Self-Supervised Multi-View Learning via Auto-Encoding 3D Transformations—0
Semi-supervised Classification using Attention-based Regularization on Coarse-resolution Data—0
Semi-supervised Multi-sensor Classification via Consensus-based Multi-View Maximum Entropy Discrimination—0
Speech representation learning: Learning bidirectional encoders with single-view, multi-view, and multi-task methods—0
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