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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
Uncertainty Quantification via Hölder Divergence for Multi-View Representation Learning—0
Multi-View Multi-Task Modeling with Speech Foundation Models for Speech Forensic Tasks—0
Dynamic Evidence Decoupling for Trusted Multi-view LearningCode0
Mammo-Clustering:A Weakly Supervised Multi-view Global-Local Context Clustering Network for Detection and Classification in Mammography—0
Steinmetz Neural Networks for Complex-Valued Data—0
Reliable Multi-View Learning with Conformal Prediction for Aortic Stenosis Classification in EchocardiographyCode0
GRVFL-MV: Graph Random Vector Functional Link Based on Multi-View Learning—0
Deep Code Search with Naming-Agnostic Contrastive Multi-View Learning—0
Bundle Recommendation with Item-level Causation-enhanced Multi-view LearningCode0
Increasing the Robustness of Model Predictions to Missing Sensors in Earth ObservationCode0
Discovering Common Information in Multi-view DataCode0
Conformal Prediction for Ensembles: Improving Efficiency via Score-Based AggregationCode0
Generalized Cauchy-Schwarz Divergence and Its Deep Learning Applications—0
MERIT: Multi-view Evidential learning for Reliable and Interpretable liver fibrosis sTaging—0
Masked Two-channel Decoupling Framework for Incomplete Multi-view Weak Multi-label Learning—0
URL: Universal Referential Knowledge Linking via Task-instructed Representation CompressionCode0
Trusted Multi-view Learning with Label NoiseCode0
Fine-Tuning Language Models with Reward Learning on PolicyCode0
In the Search for Optimal Multi-view Learning Models for Crop Classification with Global Remote Sensing DataCode0
Impact Assessment of Missing Data in Model Predictions for Earth Observation ApplicationsCode0
RIS-empowered Topology Control for Distributed Learning in Urban Air Mobility—0
Scalable Multi-view Clustering via Explicit Kernel Features Maps—0
Adaptive Fusion of Multi-view Remote Sensing data for Optimal Sub-field Crop Yield PredictionCode0
A Deep Network for Explainable Prediction of Non-Imaging Phenotypes using Anatomical Multi-View Data—0
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
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
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
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
EIT: Enhanced Interactive TransformerCode0
Syntactic Multi-view Learning for Open Information ExtractionCode0
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