19 citations · 61 across the 8 of their papers we have counts for
9 papers
Manifold Regularization for Memory-Efficient Training of Deep Neural Networks
Shadi Sartipi, Edgar A. Bernal
One of the prevailing trends in the machine- and deep-learning community is to gravitate towards the use of increasingly larger models in order to keep pushing the state-of-the-art…
Training Deep Normalizing Flow Models in Highly Incomplete Data Scenarios with Prior Regularization
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…
Machine learning the real discriminant locus
Edgar A. Bernal, Jonathan D. Hauenstein, Dhagash Mehta +2
Parameterized systems of polynomial equations arise in many applications in science and engineering with the real solutions describing, for example, equilibria of a dynamical syste…
MCFlow: Monte Carlo Flow Models for Data Imputation
Trevor W. Richardson, Wencheng Wu, Lei Lin +2
We consider the topic of data imputation, a foundational task in machine learning that addresses issues with missing data. To that end, we propose MCFlow, a deep framework for impu…
Medical Time Series Classification with Hierarchical Attention-based Temporal Convolutional Networks: A Case Study of Myotonic Dystrophy Diagnosis
Lei Lin, Beilei Xu, Wencheng Wu +2
Myotonia, which refers to delayed muscle relaxation after contraction, is the main symptom of myotonic dystrophy patients. We propose a hierarchical attention-based temporal convol…
Towards Robust Deep Neural Networks
Timothy E. Wang, Yiming Gu, Dhagash Mehta +2
We investigate the topics of sensitivity and robustness in feedforward and convolutional neural networks. Combining energy landscape techniques developed in computational chemistry…
The Loss Surface of XOR Artificial Neural Networks
Dhagash Mehta, Xiaojun Zhao, Edgar A. Bernal +1
Training an artificial neural network involves an optimization process over the landscape defined by the cost (loss) as a function of the network parameters. We explore these lands…
Deep Multimodal Representation Learning from Temporal Data
Xitong Yang, Palghat Ramesh, Radha Chitta +3
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. Whe…
Two algorithms for compressed sensing of sparse tensors
Shmuel Friedland, Qun Li, Dan Schonfeld +1
Compressed sensing (CS) exploits the sparsity of a signal in order to integrate acquisition and compression. CS theory enables exact reconstruction of a sparse signal from relative…