6 papers
Joint Representation Learning and Clustering via Gradient-Based Manifold Optimization
Sida Liu, Yangzi Guo, Mingyuan Wang
Clustering and dimensionality reduction have been crucial topics in machine learning and computer vision. Clustering high-dimensional data has been challenging for a long time due…
Exploring Vision Neural Network Pruning via Screening Methodology
Mingyuan Wang, Yangzi Guo, Sida Liu +1
The remarkable performance of modern deep neural networks (DNNs) is largely driven by their massive scale, often comprising tens to hundreds of millions-or even billions-of paramet…
A study of local optima for learning feature interactions using neural networks
Yangzi Guo, Adrian Barbu
In many fields such as bioinformatics, high energy physics, power distribution, etc., it is desirable to learn non-linear models where a small number of variables are selected and…
Neural Rule Ensembles: Encoding Sparse Feature Interactions into Neural Networks
Gitesh Dawer, Yangzi Guo, Sida Liu +1
Artificial Neural Networks form the basis of very powerful learning methods. It has been observed that a naive application of fully connected neural networks to data with many irre…
Network Pruning via Annealing and Direct Sparsity Control
Yangzi Guo, Yiyuan She, Adrian Barbu
Artificial neural networks (ANNs) especially deep convolutional networks are very popular these days and have been proved to successfully offer quite reliable solutions to many vis…
Generating Compact Tree Ensembles via Annealing
Gitesh Dawer, Yangzi Guo, Adrian Barbu
Tree ensembles are flexible predictive models that can capture relevant variables and to some extent their interactions in a compact and interpretable manner. Most algorithms for o…