Search Results for author: Edgar A. Bernal

Found 7 papers, 1 papers with code

Training Deep Normalizing Flow Models in Highly Incomplete Data Scenarios with Prior Regularization

no code implementations3 Apr 2021 Edgar A. Bernal

Deep generative frameworks including GANs and normalizing flow models have proven successful at filling in missing values in partially observed data samples by effectively learning -- either explicitly or implicitly -- complex, high-dimensional statistical distributions.

Unity

Machine learning the real discriminant locus

no code implementations24 Jun 2020 Edgar A. Bernal, Jonathan D. Hauenstein, Dhagash Mehta, Margaret H. Regan, Tingting Tang

This article views locating the real discriminant locus as a supervised classification problem in machine learning where the goal is to determine classification boundaries over the parameter space, with the classes being the number of real solutions.

MCFlow: Monte Carlo Flow Models for Data Imputation

1 code implementation CVPR 2020 Trevor W. Richardson, Wencheng Wu, Lei Lin, Beilei Xu, Edgar A. Bernal

We consider the topic of data imputation, a foundational task in machine learning that addresses issues with missing data.

Imputation

Towards Robust Deep Neural Networks

no code implementations27 Oct 2018 Timothy E. Wang, Yiming Gu, Dhagash Mehta, Xiaojun Zhao, Edgar A. Bernal

We investigate the topics of sensitivity and robustness in feedforward and convolutional neural networks.

The Loss Surface of XOR Artificial Neural Networks

no code implementations6 Apr 2018 Dhagash Mehta, Xiaojun Zhao, Edgar A. Bernal, David J. Wales

Training an artificial neural network involves an optimization process over the landscape defined by the cost (loss) as a function of the network parameters.

Deep Multimodal Representation Learning from Temporal Data

no code implementations CVPR 2017 Xitong Yang, Palghat Ramesh, Radha Chitta, Sriganesh Madhvanath, Edgar A. Bernal, Jiebo Luo

In recent years, Deep Learning has been successfully applied to multimodal learning problems, with the aim of learning useful joint representations in data fusion applications.

Audio-Visual Speech Recognition Representation Learning +2

Cannot find the paper you are looking for? You can Submit a new open access paper.