
DPM: A deep learning PDE augmentation method (with application to largeeddy simulation)
Machine learning for scientific applications faces the challenge of limi...
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Asymptotics of Reinforcement Learning with Neural Networks
We prove that a singlelayer neural network trained with the Qlearning ...
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Scaling Limit of Neural Networks with the Xavier Initialization and Convergence to a Global Minimum
We analyze singlelayer neural networks with the Xavier initialization i...
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Mean Field Analysis of Deep Neural Networks
We analyze multilayer neural networks in the asymptotic regime of simul...
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Mean Field Analysis of Neural Networks: A Central Limit Theorem
Machine learning has revolutionized fields such as image, text, and spee...
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Universal features of price formation in financial markets: perspectives from Deep Learning
Using a largescale Deep Learning approach applied to a highfrequency d...
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Stochastic Gradient Descent in Continuous Time: A Central Limit Theorem
Stochastic gradient descent in continuous time (SGDCT) provides a comput...
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DGM: A deep learning algorithm for solving partial differential equations
Highdimensional PDEs have been a longstanding computational challenge. ...
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Stochastic Gradient Descent in Continuous Time
Stochastic gradient descent in continuous time (SGDCT) provides a comput...
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Justin Sirignano
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