Amir Joudaki: Entropy Maximization With Depth: A Variational Principle For Random Neural Networks

Amir Joudaki: Entropy Maximization with Depth: A Variational Principle for Random Neural Networks

Machine Learning & Algorithmic Information Theory

Jean-Louis Dessalles: Some current limits of Machine Learning and beyond, in the light of AIT

Machine Learning & Algorithmic Information Theory

Hopfield network: How are memories stored in neural networks? [Nobel Prize in Physics 2024] #SoME2

Layerwise Lectures

Machine Learning @ UIUC - Dan Roth: Expectation Maximization II

Cognitive Computation Group

Lecture 14 - Expectation-Maximization Algorithms | Stanford CS229: Machine Learning (Autumn 2018)

Stanford Online

David Dowe: Minimum Message Length, Solomonoff-Kolmogorov complexity, intelligence, deep learning...

Machine Learning & Algorithmic Information Theory

Grounded theory and topic modelling: extreme divergence or unlikely convergence - Dr David Mimno

The Alan Turing Institute

Lecture 25 - AI Model EfficiencyToolkit (AIMET) | MIT 6.S965

Basics Of Multivariate Analysis In Neuroimaging Data l Protocol Preview

JoVE (Journal of Visualized Experiments)

A Brain-Inspired Algorithm For Memory

A. Somazzi - Constraint choice and model selection in the generalized maximum entropy principle

SIFS - Società Italiana di Fisica Statistica