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Found 4 papers, 1 papers with code
Dynamics of stochastic gradient descent for two-layer neural networks in the teacher-student setup
Deep neural networks achieve stellar generalisation even when they have enough parameters to easily fit all their training data.
Generalisation dynamics of online learning in over-parameterised neural networks
Deep neural networks achieve stellar generalisation on a variety of problems, despite often being large enough to easily fit all their training data.
Energy-entropy competition and the effectiveness of stochastic gradient descent in machine learning
We derive a correspondence between parameter inference and free energy minimisation in statistical physics.
High-dimensional dynamics of generalization error in neural networks
We study the practically-relevant "high-dimensional" regime where the number of free parameters in the network is on the order of or even larger than the number of examples in the dataset.
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