4 citations · 4 across the 4 of their papers we have counts for
5 papers
Deep Layer-wise Networks Have Closed-Form Weights
Chieh Wu, Aria Masoomi, Arthur Gretton +1
There is currently a debate within the neuroscience community over the likelihood of the brain performing backpropagation (BP). To better mimic the brain, training a network \texti…
Valid belief updates for prequentially additive loss functions arising in Semi-Modular Inference
Geoff K. Nicholls, Jeong Eun Lee, Chieh-Hsi Wu +1
Model-based Bayesian evidence combination leads to models with multiple parameteric modules. In this setting the effects of model misspecification in one of the modules may in some…
Kernel Dependence Network
Chieh Wu, Aria Masoomi, Arthur Gretton +1
We propose a greedy strategy to spectrally train a deep network for multi-class classification. Each layer is defined as a composition of linear weights with the feature map of a G…
Using Undersampling with Ensemble Learning to Identify Factors Contributing to Preterm Birth
Shi Dong, Zlatan Feric, Guangyu Li +10
In this paper, we propose Ensemble Learning models to identify factors contributing to preterm birth. Our work leverages a rich dataset collected by a NIEHS P42 Center that is tryi…
Spectral Non-Convex Optimization for Dimension Reduction with Hilbert-Schmidt Independence Criterion
Chieh Wu, Jared Miller, Yale Chang +2
The Hilbert Schmidt Independence Criterion (HSIC) is a kernel dependence measure that has applications in various aspects of machine learning. Conveniently, the objectives of diffe…