Adrian Barbu

verfied profile

∙

0 followers

∙

Florida State University

Professor, Department of Statistics, Florida State University.

Working in machine learning for cognition, computer vision and medical imaging.

research

∙ 04/07/2021

Online Feature Screening for Data Streams with Concept Drift

Screening feature selection methods are often used as a preprocessing st...

Mingyuan Wang, et al. ∙

research

∙ 04/01/2021

The Compact Support Neural Network

Neural networks are popular and useful in many fields, but they have the...

Adrian Barbu, et al. ∙

research

∙ 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...

Yangzi Guo, et al. ∙

research

∙ 02/11/2020

Neural Rule Ensembles: Encoding Sparse Feature Interactions into Neural Networks

Artificial Neural Networks form the basis of very powerful learning meth...

Gitesh Dawer, et al. ∙

research

∙ 02/11/2020

Training Efficient Network Architecture and Weights via Direct Sparsity Control

Artificial neural networks (ANNs) especially deep convolutional networks...

Yangzi Guo, et al. ∙

research

∙ 09/27/2019

Playing Atari Ball Games with Hierarchical Reinforcement Learning

Human beings are particularly good at reasoning and inference from just ...

Hua Huang, et al. ∙

research

∙ 06/09/2019

The Generalization-Stability Tradeoff in Neural Network Pruning

Pruning neural network parameters to reduce model size is an area of muc...

Brian R. Bartoldson, et al. ∙

research

∙ 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...

Mingyuan Wang, et al. ∙

research

∙ 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...

Brian Bartoldson, et al. ∙

research

∙ 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...

Sida Liu, et al. ∙

research

∙ 03/30/2018

Online Regression with Model Selection

Online learning algorithms have a wide variety of applications in large ...

Lizhe Sun, et al. ∙

research

∙ 02/12/2018

Random Hinge Forest for Differentiable Learning

We propose random hinge forests, a simple, efficient, and novel variant ...

Nathan Lay, et al. ∙

research

∙ 09/16/2017

Relevant Ensemble of Trees

Tree ensembles are flexible predictive models that can capture relevant ...

Gitesh Dawer, et al. ∙

research

∙ 08/16/2016

Parameterized Principal Component Analysis

When modeling multivariate data, one might have an extra parameter of co...

Ajay Gupta, et al. ∙

research

∙ 09/29/2014

RENOIR - A Dataset for Real Low-Light Image Noise Reduction

Image denoising algorithms are evaluated using images corrupted by artif...

Josue Anaya, et al. ∙

research

∙ 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...

Adrian Barbu, et al. ∙

research

∙ 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...

Adrian Barbu, et al. ∙

research

∙ 04/18/2012

The Artificial Regression Market

The Artificial Prediction Market is a recent machine learning technique ...

Nathan Lay, et al. ∙

research

∙ 08/18/2011

Hierarchical Object Parsing from Structured Noisy Point Clouds

Object parsing and segmentation from point clouds are challenging tasks ...

Adrian Barbu, et al. ∙

research

∙ 02/07/2011

An Introduction to Artificial Prediction Markets for Classification

Prediction markets are used in real life to predict outcomes of interest...

Adrian Barbu, et al. ∙

Adrian Barbu

Featured Co-authors

Ying Nian Wu

Adam P. Harrison

Tianfu Wu

Ari S. Morcos

Yiyuan She

Hua Huang

Ajay Gupta

Nathan Lay

Yangzi Guo

Mingyuan Wang

Gitesh Dawer

Online Feature Screening for Data Streams with Concept Drift

The Compact Support Neural Network

A study of local optima for learning feature interactions using neural networks

Neural Rule Ensembles: Encoding Sparse Feature Interactions into Neural Networks

Training Efficient Network Architecture and Weights via Direct Sparsity Control

Playing Atari Ball Games with Hierarchical Reinforcement Learning

The Generalization-Stability Tradeoff in Neural Network Pruning

Are screening methods useful in feature selection? An empirical study

Enhancing the Regularization Effect of Weight Pruning in Artificial Neural Networks

Unsupervised Learning of Mixture Models with a Uniform Background Component

Online Regression with Model Selection

Random Hinge Forest for Differentiable Learning

Relevant Ensemble of Trees

Parameterized Principal Component Analysis

RENOIR - A Dataset for Real Low-Light Image Noise Reduction

Feature Selection with Annealing for Computer Vision and Big Data Learning

Learning Mixtures of Bernoulli Templates by Two-Round EM with Performance Guarantee

The Artificial Regression Market

Hierarchical Object Parsing from Structured Noisy Point Clouds

An Introduction to Artificial Prediction Markets for Classification