Align, then memorise: the dynamics of learning with feedback alignment*

DOI10.1088/1742-5468/ac9826OpenAlexW3168722425MaRDI QIDQ5055410

Ruben Ohana, Stéphane D'Ascoli, Maria Refinetti, Sebastian Goldt

Publication date: 13 December 2022

Published in: Journal of Statistical Mechanics: Theory and Experiment (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1088/1742-5468/ac9826

zbMATH Keywords

machine learningdeep learning

Mathematics Subject Classification ID

Statistical mechanics, structure of matter (82-XX)

Cites Work

• High-dimensional dynamics of generalization error in neural networks
• Mean field analysis of neural networks: a central limit theorem
• Statistical Mechanics of Learning
• Generalization in a linear perceptron in the presence of noise
• Theoretical Insights Into the Optimization Landscape of Over-Parameterized Shallow Neural Networks
• Learning by on-line gradient descent
• A mean field view of the landscape of two-layer neural networks
• Mean-field inference methods for neural networks
• Learning representations by back-propagating errors
• Dynamics of stochastic gradient descent for two-layer neural networks in the teacher–student setup*

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