
Dimension independent bounds for general shallow networks
This paper proves an abstract theorem addressing in a unified manner two...
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Dimensionality reduction for acoustic vehicle classification with spectral clustering
Classification of vehicles has broad applications, ranging from traffic ...
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Multiclass SemiSupervised Learning on Graphs using GinzburgLandau Functional Minimization
We present a graphbased variational algorithm for classification of hig...
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Multiclass Diffuse Interface Models for SemiSupervised Learning on Graphs
We present a graphbased variational algorithm for multiclass classifica...
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Improving Image Clustering using Sparse Text and the Wisdom of the Crowds
We propose a method to improve image clustering using sparse text and th...
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Covariancebased Dissimilarity Measures Applied to Clustering Widesense Stationary Ergodic Processes
We introduce a new unsupervised learning problem: clustering widesense ...
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Analysis of Fast Alternating Minimization for Structured Dictionary Learning
Methods exploiting sparsity have been popular in imaging and signal proc...
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Unsupervised vehicle recognition using incremental reseeding of acoustic signatures
Vehicle recognition and classification have broad applications, ranging ...
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An analysis of training and generalization errors in shallow and deep networks
An open problem around deep networks is the apparent absence of overfit...
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Clustering Analysis on Locally Asymptotically Selfsimilar Processes
In this paper, we design algorithms for clustering locally asymptoticall...
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Deep Algorithms: designs for networks
A new design methodology for neural networks that is guided by tradition...
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Learning to fail: Predicting fracture evolution in brittle materials using recurrent graph convolutional neural networks
Understanding dynamic fracture propagation is essential to predicting ho...
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Function approximation by deep networks
We show that deep networks are better than shallow networks at approxima...
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Superresolution meets machine learning: approximation of measures
The problem of superresolution in general terms is to recuperate a fini...
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Deep Gaussian networks for function approximation on data defined manifolds
In much of the literature on function approximation by deep networks, th...
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Some Developments in Clustering Analysis on Stochastic Processes
We review some developments on clustering stochastic processes and come ...
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Theory inspired deep network for instantaneousfrequency extraction and signal components recovery from discrete blindsource data
This paper is concerned with the inverse problem of recovering the unkno...
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Claremont Graduate University
Claremont Graduate University is a private, allgraduate research university in Claremont, California.