no code implementations • 26 May 2023 • Shadi Sartipi, Edgar A. Bernal
Use of the framework results in improved absolute performance and empirical generalization error relative to traditional learning techniques.
no code implementations • 3 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.
no code implementations • 24 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.
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.
no code implementations • 28 Mar 2019 • Lei Lin, Beilei Xu, Wencheng Wu, Trevor Richardson, Edgar A. Bernal
Myotonia, which refers to delayed muscle relaxation after contraction, is the main symptom of myotonic dystrophy patients.
no code implementations • 27 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.
no code implementations • 6 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.
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.