Abstract. Gradient descent during the learning process of a neural network can be subject to many instabilities. The spectral density of the Jacobian is a ...
Nov 1, 2021 · Gradient descent during the learning process of a neural network can be subject to many instabilities. The spectral density of the Jacobian is a ...
This method has a controlled and proven convergence. Our technique is based on an adaptative Newton-Raphson scheme, by finding and chaining basins of attraction ...
This method has a controlled and proven convergence. Our technique is based on an adaptative Newton-Raphson scheme, by finding and chaining basins of attraction ...
Oct 31, 2022 · This work extends the free probability theory in the rectangular setup and gives a new method for computing the spectral density of the Jacobian ...
Nov 1, 2021 · Following the works of Pennington et al., such Jacobians are modeled using free multiplicative convolutions from Free Probability Theory (FPT).
Gradient descent during the learning process of a neural network can be subject to many instabilities. The spectral density of the Jacobian is a key ...
The benchmarked methods are Newton lilypads in pure. Python (blue), Newton lilypads with Cython optimizations (orange), Pennington et al.'s Algorithm 1 using ...
Oct 14, 2022 · Following the works of Pennington et al., such Jacobians are modeled using free multiplicative convolutions from Free Probability Theory (FPT).
Aug 5, 2024 · Free Probability, Newton lilypads and Jacobians of neural networks. CoRR abs/2111.00841 (2021); 2020. [i3]. view. electronic edition @ arxiv.org ...
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