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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 151–200 of 256 papers

TitleStatusHype
Uncorrelated Semi-paired Subspace Learning—0
Deep Partial Multi-View Learning—0
Layer-Wise Multi-View Learning for Neural Machine Translation—0
Generative View-Correlation Adaptation for Semi-Supervised Multi-View Learning—0
Random Forest for Dissimilarity-based Multi-view Learning—0
Embedded Deep Bilinear Interactive Information and Selective Fusion for Multi-view Learning—0
Multi-view Orthonormalized Partial Least Squares: Regularizations and Deep Extensions—0
A Novel Random Forest Dissimilarity Measure for Multi-View Learning—0
Deep brain state classification of MEG dataCode0
A Tweet-based Dataset for Company-Level Stock Return PredictionCode0
Multi-view Low-rank Preserving Embedding: A Novel Method for Multi-view Representation—0
Hierarchical Optimal Transport for Robust Multi-View Learning—0
Bayesian Sparse Factor Analysis with Kernelized Observations—0
Generalized Multi-view Shared Subspace Learning using View Bootstrapping—0
A Solution for Large Scale Nonlinear Regression with High Rank and Degree at Constant Memory Complexity via Latent Tensor Reconstruction—0
Heterogeneous Representation Learning: A Review—0
Modal Regression based Structured Low-rank Matrix Recovery for Multi-view Learning—0
Variational Inference for Deep Probabilistic Canonical Correlation Analysis—0
Multi-View Learning for Vision-and-Language Navigation—0
A Multi-view Perspective of Self-supervised Learning—0
Multi-Participant Multi-Class Vertical Federated Learning—0
Adaptive Similarity Embedding for Unsupervised Multi-View Feature Selection—0
Semi-supervised Classification using Attention-based Regularization on Coarse-resolution Data—0
Towards Disentangled Representations for Human Retargeting by Multi-view Learning—0
Potential Passenger Flow Prediction: A Novel Study for Urban Transportation Development—0
The Similarity-Consensus Regularized Multi-view Learning for Dimension Reduction—0
Variable Star Classification Using Multi-View Metric Learning—0
Community-preserving Graph Convolutions for Structural and Functional Joint Embedding of Brain Networks—0
Neural News Recommendation with Heterogeneous User Behavior—0
Partners in Crime: Multi-view Sequential Inference for Movie Understanding—0
Reviews Meet Graphs: Enhancing User and Item Representations for Recommendation with Hierarchical Attentive Graph Neural Network—0
Efficient and Adaptive Kernelization for Nonlinear Max-margin Multi-view Learning—0
Self-boosted Time-series Forecasting with Multi-task and Multi-view Learning—0
Texture and Structure Two-view Classification of Images—0
MEGAN: A Generative Adversarial Network for Multi-View Network Embedding—0
Multi-View Broad Learning System for Primate Oculomotor Decision DecodingCode0
A Multi-View Discriminant Learning Approach for Indoor Localization Using Bimodal Features of CSI—0
Neural News Recommendation with Attentive Multi-View LearningCode0
Probabilistic CCA with Implicit Distributions—0
Canonical Correlation Analysis (CCA) Based Multi-View Learning: An Overview—0
Multimodal and Multi-view Models for Emotion Recognition—0
Recurrent Neural Network for (Un-)Supervised Learning of Monocular Video Visual Odometry and Depth—0
Variational recurrent models for representation learning—0
Improving Sentence Representations with Multi-view Frameworks—0
Deep Multi-View Learning using Neuron-Wise Correlation-Maximizing Regularizers—0
Recurrent Neural Network for (Un-)supervised Learning of Monocular VideoVisual Odometry and DepthCode0
Multi-View Intact Space Learning—0
Everything old is new again: A multi-view learning approach to learning using privileged information and distillation—0
A Nonparametric Multi-view Model for Estimating Cell Type-Specific Gene Regulatory Networks—0
Learning Dual Retrieval Module for Semi-supervised Relation ExtractionCode0
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