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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
MultiEarth 2023 Deforestation Challenge -- Team FOREVER—0
Multimodal and Multi-view Models for Emotion Recognition—0
Multi-Participant Multi-Class Vertical Federated Learning—0
Multi-view Audio and Music Classification—0
Multi-view Bayesian optimisation in reduced dimension for engineering design—0
Multi-View Class Incremental Learning—0
Multi-view Data Classification with a Label-driven Auto-weighted Strategy—0
Multi-View Fuzzy Logic System with the Cooperation between Visible and Hidden Views—0
Multi-view graph structure learning using subspace merging on Grassmann manifold—0
Multi-View Incongruity Learning for Multimodal Sarcasm Detection—0
Multi-View Incremental Learning with Structured Hebbian Plasticity for Enhanced Fusion Efficiency—0
Multi-view Instance Attention Fusion Network for classification—0
Multi-View Intact Space Learning—0
Multi-view Learning as a Nonparametric Nonlinear Inter-Battery Factor Analysis—0
Multi-view learning for automatic classification of multi-wavelength auroral images—0
Multi-view learning for multivariate performance measures optimization—0
Multi-View Learning for Vision-and-Language Navigation—0
Multi-View Learning for Web Spam Detection—0
Multi-View Learning of Word Embeddings via CCA—0
Multi-view learning with privileged weighted twin support vector machine—0
Multi-view Low-rank Preserving Embedding: A Novel Method for Multi-view Representation—0
Multi-View Majority Vote Learning Algorithms: Direct Minimization of PAC-Bayesian Bounds—0
Multi-view metric learning for multi-instance image classification—0
Multi-View Multi-Task Modeling with Speech Foundation Models for Speech Forensic Tasks—0
Multi-View Non-negative Matrix Factorization Discriminant Learning via Cross Entropy Loss—0
Multi-view Orthonormalized Partial Least Squares: Regularizations and Deep Extensions—0
Multi-view Regularized Gaussian Processes—0
Multi-view Representation Learning from Malware to Defend Against Adversarial Variants—0
Multi-View representation learning in Multi-Task Scene—0
Multi-view Sentence Representation Learning—0
Multi-view Subspace Adaptive Learning via Autoencoder and Attention—0
Multi-view Unsupervised Feature Selection by Cross-diffused Matrix Alignment—0
MV-HAN: A Hybrid Attentive Networks based Multi-View Learning Model for Large-scale Contents Recommendation—0
Neural News Recommendation with Heterogeneous User Behavior—0
One Size Fits Many: Column Bundle for Multi-X Learning—0
One-step Multi-view Clustering with Diverse Representation—0
Debunking Free Fusion Myth: Online Multi-view Anomaly Detection with Disentangled Product-of-Experts Modeling—0
Overlapping Trace Norms in Multi-View Learning—0
PAC-Bayes Analysis of Multi-view Learning—0
PAC-Bayesian Domain Adaptation Bounds for Multi-view learning—0
Partners in Crime: Multi-view Sequential Inference for Movie Understanding—0
Potential Passenger Flow Prediction: A Novel Study for Urban Transportation Development—0
Probabilistic CCA with Implicit Distributions—0
Random Forest for Dissimilarity-based Multi-view Learning—0
Recurrent Neural Network for (Un-)Supervised Learning of Monocular Video Visual Odometry and Depth—0
Reviews Meet Graphs: Enhancing User and Item Representations for Recommendation with Hierarchical Attentive Graph Neural Network—0
RIS-empowered Topology Control for Distributed Learning in Urban Air Mobility—0
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
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