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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 151–175 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
Stacked Penalized Logistic Regression for Selecting Views in Multi-View Learning—0
Steinmetz Neural Networks for Complex-Valued Data—0
ST-MVL: Filling Missing Values in Geo-Sensory Time Series Data—0
Streaming View Learning—0
Text-to-Text Multi-view Learning for Passage Re-ranking—0
Texture and Structure Two-view Classification of Images—0
The Similarity-Consensus Regularized Multi-view Learning for Dimension Reduction—0
Towards Disentangled Representations for Human Retargeting by Multi-view Learning—0
Towards the Generalization of Multi-view Learning: An Information-theoretical Analysis—0
Trace Ratio Optimization with an Application to Multi-view Learning—0
TSK Fuzzy System Towards Few Labeled Incomplete Multi-View Data Classification—0
Uncertainty-Aware Multi-View Representation Learning—0
Uncertainty Quantification via Hölder Divergence for Multi-View Representation Learning—0
Uncertainty-Weighted Mutual Distillation for Multi-View Fusion—0
Uncorrelated Semi-paired Subspace Learning—0
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