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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 51–100 of 256 papers

TitleStatusHype
PAC-Bayesian Domain Adaptation Bounds for Multi-view learning—0
Multi-view learning for automatic classification of multi-wavelength auroral images—0
Debunking Free Fusion Myth: Online Multi-view Anomaly Detection with Disentangled Product-of-Experts Modeling—0
Unconstrained Stochastic CCA: Unifying Multiview and Self-Supervised LearningCode0
A smoothed-Bayesian approach to frequency recovery from sketched data—0
Multi-view Instance Attention Fusion Network for classification—0
Approaching human 3D shape perception with neurally mappable models—0
Information Theory-Guided Heuristic Progressive Multi-View Coding—0
Explainable Multi-View Deep Networks Methodology for Experimental PhysicsCode0
A Comparative Assessment of Multi-view fusion learning for Crop ClassificationCode1
Multi-View Fusion and Distillation for Subgrade Distresses Detection based on 3D-GPRCode1
Crowdsourcing Fraud Detection over Heterogeneous Temporal MMMA GraphCode0
Speech representation learning: Learning bidirectional encoders with single-view, multi-view, and multi-task methods—0
A Reliable and Interpretable Framework of Multi-view Learning for Liver Fibrosis Staging—0
MultiEarth 2023 Deforestation Challenge -- Team FOREVER—0
Multi-View Class Incremental Learning—0
One-step Multi-view Clustering with Diverse Representation—0
DualHGNN: A Dual Hypergraph Neural Network for Semi-Supervised Node Classification based on Multi-View Learning and Density Awareness—0
Semantic Invariant Multi-view Clustering with Fully Incomplete InformationCode0
Dual Contrastive Prediction for Incomplete Multi-view Representation LearningCode1
Siamese DETRCode1
Reliable Representations Learning for Incomplete Multi-View Partial Multi-Label Classification—0
Deep Double Incomplete Multi-view Multi-label Learning with Incomplete Labels and Missing ViewsCode0
MetaViewer: Towards A Unified Multi-View Representation—0
Deep Transfer Tensor Factorization for Multi-View Learning—0
ConsRec: Learning Consensus Behind Interactions for Group RecommendationCode1
A Multi-View Joint Learning Framework for Embedding Clinical Codes and Text Using Graph Neural Networks—0
Heterogeneous Domain Adaptation and Equipment Matching: DANN-based Alignment with Cyclic Supervision (DBACS)—0
MHCN: A Hyperbolic Neural Network Model for Multi-view Hierarchical Clustering—0
A Clustering-guided Contrastive Fusion for Multi-view Representation LearningCode1
Common Practices and Taxonomy in Deep Multi-view Fusion for Remote Sensing ApplicationsCode1
EIT: Enhanced Interactive TransformerCode0
Syntactic Multi-view Learning for Open Information ExtractionCode0
Learnable Graph Convolutional Network and Feature Fusion for Multi-view Learning—0
Imputation of missing values in multi-view data—0
Multi-view Representation Learning from Malware to Defend Against Adversarial Variants—0
MV-HAN: A Hybrid Attentive Networks based Multi-View Learning Model for Large-scale Contents Recommendation—0
Heterogeneous Graph Contrastive Multi-view LearningCode1
Geolocation of Cultural Heritage using Multi-View Knowledge Graph EmbeddingCode0
Latent Heterogeneous Graph Network for Incomplete Multi-View Learning—0
Adaptive incomplete multi-view learning via tensor graph completion—0
Localized Sparse Incomplete Multi-view ClusteringCode1
A Multi-View Learning Approach to Enhance Automatic 12-Lead ECG Diagnosis Performance—0
Exploring the Value of Multi-View Learning for Session-Aware Query Representation—0
Variational Distillation for Multi-View LearningCode1
Alternately Optimized Graph Neural Networks—0
scICML: Information-theoretic Co-clustering-based Multi-view Learning for the Integrative Analysis of Single-cell Multi-omics data—0
Trusted Multi-View Classification with Dynamic Evidential FusionCode2
Multi-view Information Bottleneck Without Variational ApproximationCode0
Multi-view graph structure learning using subspace merging on Grassmann manifold—0
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