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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
Adaptive Fusion of Multi-view Remote Sensing data for Optimal Sub-field Crop Yield PredictionCode0
Conformal Prediction for Ensembles: Improving Efficiency via Score-Based AggregationCode0
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 brain state classification of MEG dataCode0
Neural News Recommendation with Attentive Multi-View LearningCode0
Deep Collective Matrix Factorization for Augmented Multi-View LearningCode0
Multi-view Temporal Alignment for Non-parallel Articulatory-to-Acoustic Speech SynthesisCode0
OpenViewer: Openness-Aware Multi-View LearningCode0
Multi-View Broad Learning System for Primate Oculomotor Decision DecodingCode0
A Survey on Multi-Task LearningCode0
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
Increasing the Robustness of Model Predictions to Missing Sensors in Earth ObservationCode0
Discovering Common Information in Multi-view DataCode0
A Tweet-based Dataset for Company-Level Stock Return PredictionCode0
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
Fine-Tuning Language Models with Reward Learning on Policy—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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