21 citations · 92 across the 20 of their papers we have counts for
4 papers · 1 filter
Robust Classification using Contractive Hamiltonian Neural ODEs
Muhammad Zakwan, Liang Xu, Giancarlo Ferrari-Trecate
Deep neural networks can be fragile and sensitive to small input perturbations that might cause a significant change in the output. In this paper, we employ contraction theory to i…
Hamiltonian Deep Neural Networks Guaranteeing Non-vanishing Gradients by Design
Clara Lucía Galimberti, Luca Furieri, Liang Xu +1
Deep Neural Networks (DNNs) training can be difficult due to vanishing and exploding gradients during weight optimization through backpropagation. To address this problem, we propo…
A unified framework for Hamiltonian deep neural networks
Clara L. Galimberti, Liang Xu, Giancarlo Ferrari Trecate
Training deep neural networks (DNNs) can be difficult due to the occurrence of vanishing/exploding gradients during weight optimization. To avoid this problem, we propose a class o…
NVAE-GAN Based Approach for Unsupervised Time Series Anomaly Detection
Liang Xu, Liying Zheng, Weijun Li +6
In recent studies, Lots of work has been done to solve time series anomaly detection by applying Variational Auto-Encoders (VAEs). Time series anomaly detection is a very common bu…