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05/02/2023
Slow Kill for Big Data Learning
Big-data applications often involve a vast number of observations and fe...
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04/06/2023
Training a Two Layer ReLU Network Analytically
Neural networks are usually trained with different variants of gradient ...
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03/03/2023
Feature Selection for Forecasting
This work investigates the importance of feature selection for improving...
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02/28/2023
Scalable Clustering: Large Scale Unsupervised Learning of Gaussian Mixture Models with Outliers
Clustering is a widely used technique with a long and rich history in a ...
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04/07/2021
Online Feature Screening for Data Streams with Concept Drift
Screening feature selection methods are often used as a preprocessing st...
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04/01/2021
The Compact Support Neural Network
Neural networks are popular and useful in many fields, but they have the...
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02/11/2020
A study of local optima for learning feature interactions using neural networks
In many fields such as bioinformatics, high energy physics, power distri...
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02/11/2020
Neural Rule Ensembles: Encoding Sparse Feature Interactions into Neural Networks
Artificial Neural Networks form the basis of very powerful learning meth...
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02/11/2020
Training Efficient Network Architecture and Weights via Direct Sparsity Control
Artificial neural networks (ANNs) especially deep convolutional networks...
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09/27/2019
Playing Atari Ball Games with Hierarchical Reinforcement Learning
Human beings are particularly good at reasoning and inference from just ...
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06/09/2019
The Generalization-Stability Tradeoff in Neural Network Pruning
Pruning neural network parameters to reduce model size is an area of muc...
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09/14/2018
Are screening methods useful in feature selection? An empirical study
Filter or screening methods are often used as a preprocessing step for r...
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05/04/2018
Enhancing the Regularization Effect of Weight Pruning in Artificial Neural Networks
Artificial neural networks (ANNs) may not be worth their computational/m...
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04/08/2018
Unsupervised Learning of Mixture Models with a Uniform Background Component
Gaussian Mixture Models are one of the most studied and mature models in...
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03/30/2018
Online Regression with Model Selection
Online learning algorithms have a wide variety of applications in large ...
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02/12/2018
Random Hinge Forest for Differentiable Learning
We propose random hinge forests, a simple, efficient, and novel variant ...
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09/16/2017
Relevant Ensemble of Trees
Tree ensembles are flexible predictive models that can capture relevant ...
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08/16/2016
Parameterized Principal Component Analysis
When modeling multivariate data, one might have an extra parameter of co...
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09/29/2014
RENOIR - A Dataset for Real Low-Light Image Noise Reduction
Image denoising algorithms are evaluated using images corrupted by artif...
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10/10/2013
Feature Selection with Annealing for Computer Vision and Big Data Learning
Many computer vision and medical imaging problems are faced with learnin...
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05/02/2013
Learning Mixtures of Bernoulli Templates by Two-Round EM with Performance Guarantee
Dasgupta and Shulman showed that a two-round variant of the EM algorithm...
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04/18/2012
The Artificial Regression Market
The Artificial Prediction Market is a recent machine learning technique ...
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08/18/2011
Hierarchical Object Parsing from Structured Noisy Point Clouds
Object parsing and segmentation from point clouds are challenging tasks ...
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02/07/2011
