43 citations · 110 across the 12 of their papers we have counts for
Showing 2018 · stat.MLShow all
3 papers · 2 filters
stat.ML2018
Physics-informed deep generative models
Yibo Yang, Paris Perdikaris
We consider the application of deep generative models in propagating uncertainty through complex physical systems. Specifically, we put forth an implicit variational inference form…
stat.ML2018
Adversarial Uncertainty Quantification in Physics-Informed Neural Networks
Yibo Yang, Paris Perdikaris
We present a deep learning framework for quantifying and propagating uncertainty in systems governed by non-linear differential equations using physics-informed neural networks. Sp…
stat.ML2018
Scalable Neural Network Compression and Pruning Using Hard Clustering and L1 Regularization
Yibo Yang, Nicholas Ruozzi, Vibhav Gogate
We propose a simple and easy to implement neural network compression algorithm that achieves results competitive with more complicated state-of-the-art methods. The key idea is to…