5 citations · 5 across the 1 of their papers we have counts for
5 papers
DPM: A deep learning PDE augmentation method (with application to large-eddy simulation)
Jonathan B. Freund, Jonathan F. MacArt, Justin Sirignano
Machine learning for scientific applications faces the challenge of limited data. We propose a framework that leverages a priori known physics to reduce overfitting when training o…
Asymptotics of Reinforcement Learning with Neural Networks
Justin Sirignano, Konstantinos Spiliopoulos
We prove that a single-layer neural network trained with the Q-learning algorithm converges in distribution to a random ordinary differential equation as the size of the model and…
Mean Field Analysis of Deep Neural Networks
Justin Sirignano, Konstantinos Spiliopoulos
We analyze multi-layer neural networks in the asymptotic regime of simultaneously (A) large network sizes and (B) large numbers of stochastic gradient descent training iterations.…
Mean Field Analysis of Neural Networks: A Central Limit Theorem
Justin Sirignano, Konstantinos Spiliopoulos
We rigorously prove a central limit theorem for neural network models with a single hidden layer. The central limit theorem is proven in the asymptotic regime of simultaneously (A)…
Mean Field Analysis of Neural Networks: A Law of Large Numbers
Justin Sirignano, Konstantinos Spiliopoulos
Machine learning, and in particular neural network models, have revolutionized fields such as image, text, and speech recognition. Today, many important real-world applications in…