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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
Reliable Multi-View Learning with Conformal Prediction for Aortic Stenosis Classification in EchocardiographyCode0
EIT: Enhanced Interactive TransformerCode0
Unconstrained Stochastic CCA: Unifying Multiview and Self-Supervised LearningCode0
Adaptive Fusion of Multi-view Remote Sensing data for Optimal Sub-field Crop Yield PredictionCode0
Dynamic Evidence Decoupling for Trusted Multi-view LearningCode0
Recurrent Neural Network for (Un-)supervised Learning of Monocular VideoVisual Odometry and DepthCode0
Reliable Disentanglement Multi-view Learning Against View Adversarial AttacksCode0
Deep Collective Matrix Factorization for Augmented Multi-View LearningCode0
Neural News Recommendation with Attentive Multi-View LearningCode0
Multi-view Temporal Alignment for Non-parallel Articulatory-to-Acoustic Speech SynthesisCode0
OpenViewer: Openness-Aware Multi-View LearningCode0
A Survey on Multi-Task LearningCode0
Multi-View Broad Learning System for Primate Oculomotor Decision DecodingCode0
Multi-Multi-View Learning: Multilingual and Multi-Representation Entity TypingCode0
Multi-view Information Bottleneck Without Variational ApproximationCode0
Partially Shared Semi-supervised Deep Matrix Factorization with Multi-view DataCode0
Learning Dual Retrieval Module for Semi-supervised Relation ExtractionCode0
In the Search for Optimal Multi-view Learning Models for Crop Classification with Global Remote Sensing DataCode0
Missing Data as Augmentation in the Earth Observation Domain: A Multi-View Learning ApproachCode0
Impact Assessment of Missing Data in Model Predictions for Earth Observation ApplicationsCode0
Discovering Common Information in Multi-view DataCode0
A Tweet-based Dataset for Company-Level Stock Return PredictionCode0
Increasing the Robustness of Model Predictions to Missing Sensors in Earth ObservationCode0
Geolocation of Cultural Heritage using Multi-View Knowledge Graph EmbeddingCode0
Integrative Multi-View Reduced-Rank Regression: Bridging Group-Sparse and Low-Rank ModelsCode0
Patterns for Learning with Side InformationCode0
Deep Multi-View Learning for Tire Recommendation—0
Deep Incomplete Multi-view Learning via Cyclic Permutation of VAEs—0
A Solution for Large Scale Nonlinear Regression with High Rank and Degree at Constant Memory Complexity via Latent Tensor Reconstruction—0
A Multi-view Context-aware Approach to Android Malware Detection and Malicious Code Localization—0
Deep Code Search with Naming-Agnostic Contrastive Multi-View Learning—0
ARMOURED: Adversarially Robust MOdels using Unlabeled data by REgularizing Diversity—0
Alternately Optimized Graph Neural Networks—0
Adaptive Multi-View ICA: Estimation of noise levels for optimal inference—0
A Reliable and Interpretable Framework of Multi-view Learning for Liver Fibrosis Staging—0
Approaching human 3D shape perception with neurally mappable models—0
AI-Powered Prediction of Nanoparticle Pharmacokinetics: A Multi-View Learning Approach—0
Community-preserving Graph Convolutions for Structural and Functional Joint Embedding of Brain Networks—0
A Novel TSK Fuzzy System Incorporating Multi-view Collaborative Transfer Learning for Personalized Epileptic EEG Detection—0
Generalized Multi-view Shared Subspace Learning using View Bootstrapping—0
Generalized Cauchy-Schwarz Divergence and Its Deep Learning Applications—0
A smoothed-Bayesian approach to frequency recovery from sketched data—0
A Novel Random Forest Dissimilarity Measure for Multi-View Learning—0
A graph neural network-based multispectral-view learning model for diabetic macular ischemia detection from color fundus photographs—0
Adaptive incomplete multi-view learning via tensor graph completion—0
FedMSGL: A Self-Expressive Hypergraph Based Federated Multi-View Learning—0
Classification of weak multi-view signals by sharing factors in a mixture of Bayesian group factor analyzers—0
Federated Multi-View Learning for Private Medical Data Integration and Analysis—0
Generative View-Correlation Adaptation for Semi-Supervised Multi-View Learning—0
Canonical Correlation Analysis with Implicit Distributions—0
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