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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 126–150 of 256 papers

TitleStatusHype
Uncertainty-Weighted Mutual Distillation for Multi-View Fusion—0
Uncorrelated Semi-paired Subspace Learning—0
Variable Star Classification Using Multi-View Metric Learning—0
Variational Inference for Deep Probabilistic Canonical Correlation Analysis—0
Variational Interpretable Learning from Multi-view Data—0
Variational recurrent models for representation learning—0
Visual-Texual Emotion Analysis with Deep Coupled Video and Danmu Neural Networks—0
Weak Multi-View Supervision for Surface Mapping Estimation—0
Exploring the Value of Multi-View Learning for Session-Aware Query Representation—0
Exploring the Value of Multi-View Learning for Session-Aware Query Representation—0
Factorized Latent Spaces with Structured Sparsity—0
Federated Multi-View Learning for Private Medical Data Integration and Analysis—0
FedMSGL: A Self-Expressive Hypergraph Based Federated Multi-View Learning—0
A smoothed-Bayesian approach to frequency recovery from sketched data—0
Generalized Cauchy-Schwarz Divergence and Its Deep Learning Applications—0
Generalized Multi-view Shared Subspace Learning using View Bootstrapping—0
Generative View-Correlation Adaptation for Semi-Supervised Multi-View Learning—0
Group-sparse Embeddings in Collective Matrix Factorization—0
GRVFL-MV: Graph Random Vector Functional Link Based on Multi-View Learning—0
Heterogeneous Domain Adaptation and Equipment Matching: DANN-based Alignment with Cyclic Supervision (DBACS)—0
Heterogeneous Representation Learning: A Review—0
Hierarchical Optimal Transport for Robust Multi-View Learning—0
Higher Order Correlation Analysis for Multi-View Learning—0
Improve the performance of transfer learning without fine-tuning using dissimilarity-based multi-view learning for breast cancer histology images—0
Improving Sentence Representations with Multi-view Frameworks—0
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