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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 1–25 of 256 papers

TitleStatusHype
Reliable Disentanglement Multi-view Learning Against View Adversarial AttacksCode0
Multi-View Learning with Context-Guided Receptance for Image DenoisingCode1
An introduction to R package `mvs`—0
AI-Powered Prediction of Nanoparticle Pharmacokinetics: A Multi-View Learning Approach—0
FedMSGL: A Self-Expressive Hypergraph Based Federated Multi-View Learning—0
Molecule Generation for Target Protein Binding with Hierarchical Consistency Diffusion ModelCode1
A graph neural network-based multispectral-view learning model for diabetic macular ischemia detection from color fundus photographs—0
Deep Incomplete Multi-view Learning via Cyclic Permutation of VAEs—0
Advanced Assessment of Stroke in Retinal Fundus Imaging with Deep Multi-view Learning—0
Towards the Generalization of Multi-view Learning: An Information-theoretical Analysis—0
Balanced Multi-view ClusteringCode0
Multi-view Bayesian optimisation in reduced dimension for engineering design—0
Missing Data as Augmentation in the Earth Observation Domain: A Multi-View Learning ApproachCode0
ROLL: Robust Noisy Pseudo-label Learning for Multi-View Clustering with Noisy Correspondence—0
OpenViewer: Openness-Aware Multi-View LearningCode0
Multi-View Incremental Learning with Structured Hebbian Plasticity for Enhanced Fusion Efficiency—0
Multi-View Incongruity Learning for Multimodal Sarcasm Detection—0
Uncertainty-Weighted Mutual Distillation for Multi-View Fusion—0
SE(3) Equivariant Ray Embeddings for Implicit Multi-View Depth Estimation—0
Multi-View Majority Vote Learning Algorithms: Direct Minimization of PAC-Bayesian Bounds—0
Generalized Trusted Multi-view Classification Framework with Hierarchical Opinion AggregationCode0
Generalizable and Robust Spectral Method for Multi-view Representation LearningCode0
Uncertainty Quantification via Hölder Divergence for Multi-View Representation Learning—0
Robust Variational Contrastive Learning for Partially View-unaligned ClusteringCode1
Multi-View Multi-Task Modeling with Speech Foundation Models for Speech Forensic Tasks—0
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