661 citations · 1.6k across the 30 of their papers we have counts for
6 papers · 1 filter
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…
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…
Joint Sub-bands Learning with Clique Structures for Wavelet Domain Super-Resolution
Zhisheng Zhong, Tiancheng Shen, Yibo Yang +2
Convolutional neural networks (CNNs) have recently achieved great success in single-image super-resolution (SISR). However, these methods tend to produce over-smoothed outputs and…
Optimization Algorithm Inspired Deep Neural Network Structure Design
Huan Li, Yibo Yang, Dongmin Chen +1
Deep neural networks have been one of the dominant machine learning approaches in recent years. Several new network structures are proposed and have better performance than the tra…
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…
Convolutional Neural Networks with Alternately Updated Clique
Yibo Yang, Zhisheng Zhong, Tiancheng Shen +1
Improving information flow in deep networks helps to ease the training difficulties and utilize parameters more efficiently. Here we propose a new convolutional neural network arch…