56 citations · 194 across the 11 of their papers we have counts for
6 papers · 1 filter
A Study on Graph-Structured Recurrent Neural Networks and Sparsification with Application to Epidemic Forecasting
Zhijian Li, Xiyang Luo, Bao Wang +2
We study epidemic forecasting on real-world health data by a graph-structured recurrent neural network (GSRNN). We achieve state-of-the-art forecasting accuracy on the benchmark CD…
Laplacian Smoothing Gradient Descent
Stanley Osher, Bao Wang, Penghang Yin +4
We propose a class of very simple modifications of gradient descent and stochastic gradient descent. We show that when applied to a large variety of machine learning problems, rang…
Graph-Based Deep Modeling and Real Time Forecasting of Sparse Spatio-Temporal Data
Bao Wang, Xiyang Luo, Fangbo Zhang +3
We present a generic framework for spatio-temporal (ST) data modeling, analysis, and forecasting, with a special focus on data that is sparse in both space and time. Our multi-scal…
Constrained Classification and Ranking via Quantiles
Alan Mackey, Xiyang Luo, Elad Eban
In most machine learning applications, classification accuracy is not the primary metric of interest. Binary classifiers which face class imbalance are often evaluated by the …
Deep Neural Nets with Interpolating Function as Output Activation
Bao Wang, Xiyang Luo, Zhen Li +3
We replace the output layer of deep neural nets, typically the softmax function, by a novel interpolating function. And we propose end-to-end training and testing algorithms for th…
Uncertainty quantification in graph-based classification of high dimensional data
Andrea L. Bertozzi, Xiyang Luo, Andrew M. Stuart +1
Classification of high dimensional data finds wide-ranging applications. In many of these applications equipping the resulting classification with a measure of uncertainty may be a…