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stat.ML2024★ 1 cited
Bayesian Inference with Deep Weakly Nonlinear Networks
Boris Hanin, Alexander Zlokapa
We show at a physics level of rigor that Bayesian inference with a fully connected neural network and a shaped nonlinearity of the form is (perturbatively) solv…
stat.ML2023
Depthwise Hyperparameter Transfer in Residual Networks: Dynamics and Scaling Limit
Blake Bordelon, Lorenzo Noci, Mufan Bill Li +2
The cost of hyperparameter tuning in deep learning has been rising with model sizes, prompting practitioners to find new tuning methods using a proxy of smaller networks. One such…
stat.ML2023
Les Houches Lectures on Deep Learning at Large & Infinite Width
Yasaman Bahri, Boris Hanin, Antonin Brossollet +4
These lectures, presented at the 2022 Les Houches Summer School on Statistical Physics and Machine Learning, focus on the infinite-width limit and large-width regime of deep neural…