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cs.LG2026
Stochastic learning flow: moment equations for stochastic gradient descent at finite step size
Christian Schmid, James M Murray
Analytical theories of learning in deep networks often assume gradient flow, discarding the finite step size and batch noise of stochastic gradient descent (SGD). Starting from the…
cs.LG2024
Dynamics of Supervised and Reinforcement Learning in the Non-Linear Perceptron
Christian Schmid, James M. Murray
The ability of a brain or a neural network to efficiently learn depends crucially on both the task structure and the learning rule. Previous works have analyzed the dynamical equat…
cs.LG2022
Gradient Descent Temporal Difference-difference Learning
Rong J. B. Zhu, James M. Murray
Off-policy algorithms, in which a behavior policy differs from the target policy and is used to gain experience for learning, have proven to be of great practical value in reinforc…