An algorithm for learning representations of models with scarce data

DOI10.1007/s41884-024-00153-0MaRDI QIDQ6660916

Adrian de Wynter

Publication date: 10 January 2025

Published in: Information Geometry (Search for Journal in Brave)

zbMATH Keywords

data augmentationsemi-supervised learning

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Computational aspects of data analysis and big data (68T09)

Cites Work

• Nonparametric ridge estimation
• On surrogate loss functions and \(f\)-divergences
• The true sample complexity of active learning
• Finding the homology of submanifolds with high confidence from random samples
• Cryptographic hardness for learning intersections of halfspaces
• On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities
• Active Learning
• A Topological View of Unsupervised Learning from Noisy Data
• Efficient noise-tolerant learning from statistical queries
• Online Learning and Online Convex Optimization
• Curvature Measures
• A theory of the learnable
• Cryptographic limitations on learning Boolean formulae and finite automata
• Scale-sensitive dimensions, uniform convergence, and learnability
• Robust Topological Inference: Distance To a Measure and Kernel Distance
• Inference of Curvature Using Tubular Neighborhoods
• Overcoming catastrophic forgetting in neural networks
• Breast Cancer Diagnosis and Prognosis Via Linear Programming
• Complexity theoretic limitations on learning halfspaces
• Understanding Machine Learning
• Convexity, Classification, and Risk Bounds
• Generalization in Deep Learning
• Logistic regression, AdaBoost and Bregman distances
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: An algorithm for learning representations of models with scarce data