Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems | Paper Citation Record

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This paper cites Adam with model exponential moving average is effective for nonconvex optimization.

Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems Adam with model exponential moving average is effective for nonconvex optimization

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Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems General framework for online-to-nonconvex conversion: Schedule-free SGD is also effective for nonconvex optimization

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Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems Unresolved cited work

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Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems Learning Theory from First Principles

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Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems Convergence and dynamical behavior of the Adam algorithm for nonconvex stochastic optimization

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This paper cites Solving the Kolmogorov PDE by means of deep learning.

Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems Solving the Kolmogorov PDE by means of deep learning

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This paper cites An overview on deep learning-based approximation methods for partial differential equations.

Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems An overview on deep learning-based approximation methods for partial differential equations

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Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems Unresolved cited work

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Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems G., Suau Cuadros, X., and Webb, R

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This paper cites Scientific machine learning through physics-informed neural networks: where we are and what’s next.

Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems Scientific machine learning through physics-informed neural networks: where we are and what’s next

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Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems The Road Less Scheduled

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Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems A Simple Convergence Proof of Adam and Adagrad

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This paper cites General multilevel adaptations for stochastic approximation algorithms II: CLTs.

Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems General multilevel adaptations for stochastic approximation algorithms II: CLTs

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Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems Convergence rates for the Adam optimizer

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This paper cites Central limit theorems for stochastic gradient descent with averaging for stable manifolds.

Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems Central limit theorems for stochastic gradient descent with averaging for stable manifolds

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Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems General multilevel adaptations for stochastic approximation algorithms of Robbins-Monro and Polyak-Ruppert type.Numer

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Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems Deep learning-based numerical methods for high- dimensional parabolic partial differential equations and backward stochastic differential equations

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Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems Algorithms for solving high dimensional PDEs: from nonlinear Monte Carlo to machine learning

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This paper cites Optimal non-asymptotic bound of the Ruppert-Polyak averaging without strong convexity.

Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems Optimal non-asymptotic bound of the Ruppert-Polyak averaging without strong convexity

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Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems Neural networks-based algorithms for stochastic control and PDEs in finance

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Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems Stochastic weight averaging revisited

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Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems Unresolved cited work

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Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems Deep residual learning for image recogni- tion

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Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems Recent developments in machine learning methods for stochastic control and games

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Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems Averaging Weights Leads to Wider Optima and Better Generalization

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Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory

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Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems Adam: A Method for Stochastic Optimization

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Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems Convergence of Adam Under Relaxed Assumptions

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Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models

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Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems Stochastic Gradient Descent as Approximate Bayesian Inference

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Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems Exponential moving average of weights in deep learning: Dynamics and benefits

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Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems Automatic differentiation in PyTorch

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Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems PyTorch: An Imperative Style, High-Performance Deep Learning Library

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Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems Unresolved cited work

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Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems T., and Juditsky, A

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Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems Adam - PyTorch 2.5 documentation

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Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems Zero-Shot Text-to-Image Generation

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Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems On the Convergence of Adam and Beyond

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Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems High-Resolution Image Synthesis with Latent Diffusion Models

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Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems An overview of gradient descent optimization algorithms

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Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems Efficient estimations from a slowly convergent Robbins-Monro process.Cor- nell University Operations Research and Industrial Engineering, hdl.handle.net/1813/8664 (1988), 1–34

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Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding

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Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems Training trajectories, mini-batch losses and the curious role of the learning rate

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Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems Optimization for deep learning: theory and algorithms

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Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems Deep learning with Elastic Averaging SGD

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