2 citations · 3 across the 2 of their papers we have counts for
6 papers
Hyperbolic Convolution via Kernel Point Aggregation
Eric Qu, Dongmian Zou
Learning representations according to the underlying geometry is of vital importance for non-Euclidean data. Studies have revealed that the hyperbolic space can effectively embed h…
Regularized Variational Data Assimilation for Bias Treatment using the Wasserstein Metric
Sagar K. Tamang, Ardeshir Ebtehaj, Dongmian Zou +1
This paper presents a new variational data assimilation (VDA) approach for the formal treatment of bias in both model outputs and observations. This approach relies on the Wasserst…
Encoding Robust Representation for Graph Generation
Dongmian Zou, Gilad Lerman
Generative networks have made it possible to generate meaningful signals such as images and texts from simple noise. Recently, generative methods based on GAN and VAE were develope…
On Lipschitz Bounds of General Convolutional Neural Networks
Dongmian Zou, Radu Balan, Maneesh Singh
Many convolutional neural networks (CNNs) have a feed-forward structure. In this paper, a linear program that estimates the Lipschitz bound of such CNNs is proposed. Several CNNs,…
Graph Convolutional Neural Networks via Scattering
Dongmian Zou, Gilad Lerman
We generalize the scattering transform to graphs and consequently construct a convolutional neural network on graphs. We show that under certain conditions, any feature generated b…
On Lipschitz Analysis and Lipschitz Synthesis for the Phase Retrieval Problem
Radu Balan, Dongmian Zou
In this paper we prove two results regarding reconstruction from magnitudes of frame coefficients (the so called "phase retrieval problem"). First we show that phase retrievability…