Description
Minimum Description Length provides a criterion for the selection of models, regardless of their complexity, without the restrictive assumption that the data form a sample from a 'true' distribution.
Extracted from scholarpedia
Source:
Paper: J. Rissanen (1978) Modeling by the shortest data description. Automatica 14, 465-471
Book: P. D. Grünwald (2007) The Minimum Description Length Principle, MIT Press, June 2007, 570 pages
Papers Using This Method
Bridging Predictive Coding and MDL: A Two-Part Code Framework for Deep Learning2025-05-20A Minimum Description Length Approach to Regularization in Neural Networks2025-05-19A Theory of Machine Understanding via the Minimum Description Length Principle2025-04-01A Materials Map Integrating Experimental and Computational Data via Graph-Based Machine Learning for Enhanced Materials Discovery2025-03-10Comparative Analysis of MDL-VAE vs. Standard VAE on 202 Years of Gynecological Data2025-02-25On Calibration in Multi-Distribution Learning2024-12-18AdaptiveMDL-GenClust: A Robust Clustering Framework Integrating Normalized Mutual Information and Evolutionary Algorithms2024-11-26Interpretability as Compression: Reconsidering SAE Explanations of Neural Activations with MDL-SAEs2024-10-15Neural Networks Generalize on Low Complexity Data2024-09-19MDL-Pool: Adaptive Multilevel Graph Pooling Based on Minimum Description Length2024-09-16A Systematic Review of Intermediate Fusion in Multimodal Deep Learning for Biomedical Applications2024-08-02Coding for Intelligence from the Perspective of Category2024-07-01Crocodile: Cross Experts Covariance for Disentangled Learning in Multi-Domain Recommendation2024-05-21Probabilistic Truly Unordered Rule Sets2024-01-18Skin cancer diagnosis using NIR spectroscopy data of skin lesions in vivo using machine learning algorithms2024-01-02Distribution-Dependent Rates for Multi-Distribution Learning2023-12-20Optimal Multi-Distribution Learning2023-12-08Exploring the hierarchical structure of human plans via program generation2023-11-30Bridging Algorithmic Information Theory and Machine Learning: A New Approach to Kernel Learning2023-11-21Improved MDL Estimators Using Fiber Bundle of Local Exponential Families for Non-exponential Families2023-11-07