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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 101–150 of 256 papers

TitleStatusHype
ROLL: Robust Noisy Pseudo-label Learning for Multi-View Clustering with Noisy Correspondence—0
Saliency-based Multi-View Mixed Language Training for Zero-shot Cross-lingual Classification—0
Scalable and Effective Deep CCA via Soft Decorrelation—0
Scalable Multi-view Clustering via Explicit Kernel Features Maps—0
scICML: Information-theoretic Co-clustering-based Multi-view Learning for the Integrative Analysis of Single-cell Multi-omics data—0
SE(3) Equivariant Ray Embeddings for Implicit Multi-View Depth Estimation—0
Self-attention Multi-view Representation Learning with Diversity-promoting Complementarity—0
Self-boosted Time-series Forecasting with Multi-task and Multi-view Learning—0
Self-Supervised Multi-View Learning via Auto-Encoding 3D Transformations—0
Semi-supervised Classification using Attention-based Regularization on Coarse-resolution Data—0
Semi-supervised Multi-sensor Classification via Consensus-based Multi-View Maximum Entropy Discrimination—0
Speech representation learning: Learning bidirectional encoders with single-view, multi-view, and multi-task methods—0
Stacked Penalized Logistic Regression for Selecting Views in Multi-View Learning—0
Steinmetz Neural Networks for Complex-Valued Data—0
ST-MVL: Filling Missing Values in Geo-Sensory Time Series Data—0
Streaming View Learning—0
Text-to-Text Multi-view Learning for Passage Re-ranking—0
Texture and Structure Two-view Classification of Images—0
The Similarity-Consensus Regularized Multi-view Learning for Dimension Reduction—0
Towards Disentangled Representations for Human Retargeting by Multi-view Learning—0
Towards the Generalization of Multi-view Learning: An Information-theoretical Analysis—0
Trace Ratio Optimization with an Application to Multi-view Learning—0
TSK Fuzzy System Towards Few Labeled Incomplete Multi-View Data Classification—0
Uncertainty-Aware Multi-View Representation Learning—0
Uncertainty Quantification via Hölder Divergence for Multi-View Representation Learning—0
Uncertainty-Weighted Mutual Distillation for Multi-View Fusion—0
Uncorrelated Semi-paired Subspace Learning—0
Variable Star Classification Using Multi-View Metric Learning—0
Variational Inference for Deep Probabilistic Canonical Correlation Analysis—0
Variational Interpretable Learning from Multi-view Data—0
Variational recurrent models for representation learning—0
Visual-Texual Emotion Analysis with Deep Coupled Video and Danmu Neural Networks—0
Weak Multi-View Supervision for Surface Mapping Estimation—0
Exploring the Value of Multi-View Learning for Session-Aware Query Representation—0
Exploring the Value of Multi-View Learning for Session-Aware Query Representation—0
Factorized Latent Spaces with Structured Sparsity—0
Federated Multi-View Learning for Private Medical Data Integration and Analysis—0
FedMSGL: A Self-Expressive Hypergraph Based Federated Multi-View Learning—0
A smoothed-Bayesian approach to frequency recovery from sketched data—0
Generalized Cauchy-Schwarz Divergence and Its Deep Learning Applications—0
Generalized Multi-view Shared Subspace Learning using View Bootstrapping—0
Generative View-Correlation Adaptation for Semi-Supervised Multi-View Learning—0
Group-sparse Embeddings in Collective Matrix Factorization—0
GRVFL-MV: Graph Random Vector Functional Link Based on Multi-View Learning—0
Heterogeneous Domain Adaptation and Equipment Matching: DANN-based Alignment with Cyclic Supervision (DBACS)—0
Heterogeneous Representation Learning: A Review—0
Hierarchical Optimal Transport for Robust Multi-View Learning—0
Higher Order Correlation Analysis for Multi-View Learning—0
Improve the performance of transfer learning without fine-tuning using dissimilarity-based multi-view learning for breast cancer histology images—0
Improving Sentence Representations with Multi-view Frameworks—0
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