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Mean Field Analysis of Neural Networks: A Central Limit Theorem
28 August 2018
Justin A. Sirignano
K. Spiliopoulos
MLT
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Papers citing
"Mean Field Analysis of Neural Networks: A Central Limit Theorem"
50 / 139 papers shown
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Gaussian mixture layers for neural networks
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Asymptotics of SGD in Sequence-Single Index Models and Single-Layer Attention Networks
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Scalable Complexity Control Facilitates Reasoning Ability of LLMs
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Mirror Mean-Field Langevin Dynamics
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Global Convergence and Rich Feature Learning in
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Mean-Field Analysis for Learning Subspace-Sparse Polynomials with Gaussian Input
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487
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A Random Matrix Theory Perspective on the Spectrum of Learned Features and Asymptotic Generalization Capabilities
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Luca Pesce
Hugo Cui
Florent Krzakala
Yue M. Lu
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391
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Theodor Misiakiewicz
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Coding schemes in neural networks learning classification tasks
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Central Limit Theorem for Bayesian Neural Network trained with Variational Inference
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Error Bounds of Supervised Classification from Information-Theoretic Perspective
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Wei Gong
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319
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Online Learning and Information Exponents: On The Importance of Batch size, and Time/Complexity Tradeoffs
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Florent Krzakala
Bruno Loureiro
Luca Pesce
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339
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Generalization of Scaled Deep ResNets in the Mean-Field Regime
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Luca Pesce
Yatin Dandi
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Yue M. Lu
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Bruno Loureiro
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352
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07 Feb 2024
The Benefits of Reusing Batches for Gradient Descent in Two-Layer Networks: Breaking the Curse of Information and Leap Exponents
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Emanuele Troiani
Luca Arnaboldi
Luca Pesce
Lenka Zdeborová
Florent Krzakala
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384
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05 Feb 2024
Mean-field underdamped Langevin dynamics and its spacetime discretization
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Ashia Wilson
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Weight fluctuations in (deep) linear neural networks and a derivation of the inverse-variance flatness relation
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A. Raulf
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Jiyoung Park
Ian Pelakh
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A Theory of Non-Linear Feature Learning with One Gradient Step in Two-Layer Neural Networks
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Behrad Moniri
Donghwan Lee
Hamed Hassani
Guang Cheng
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591
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Six Lectures on Linearized Neural Networks
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Andrea Montanari
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Quantitative CLTs in Deep Neural Networks
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Fundamental limits of overparametrized shallow neural networks for supervised learning
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Convergence of mean-field Langevin dynamics: Time and space discretization, stochastic gradient, and variance reduction
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Escaping mediocrity: how two-layer networks learn hard generalized linear models with SGD
Luca Arnaboldi
Florent Krzakala
Bruno Loureiro
Ludovic Stephan
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366
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How Two-Layer Neural Networks Learn, One (Giant) Step at a Time
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Florent Krzakala
Bruno Loureiro
Luca Pesce
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596
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Understanding the Initial Condensation of Convolutional Neural Networks
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Zhangchen Zhou
Hanxu Zhou
Yuqing Li
Zhi-Qin John Xu
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211
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Leveraging the two timescale regime to demonstrate convergence of neural networks
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312
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Depth Separation with Multilayer Mean-Field Networks
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Rong Ge
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High-dimensional scaling limits and fluctuations of online least-squares SGD with smooth covariance
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Ye He
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Phase Diagram of Initial Condensation for Two-layer Neural Networks
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Primal and Dual Analysis of Entropic Fictitious Play for Finite-sum Problems
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Bayesian Interpolation with Deep Linear Networks
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