26 citations · 155 across the 46 of their papers we have counts for
4 papers · 1 filter
Complexity of One-Dimensional ReLU DNNs
Jonathan Kogan, Hayden Jananthan, Jeremy Kepner
We study the expressivity of one-dimensional (1D) ReLU deep neural networks through the lens of their linear regions. For randomly initialized, fully connected 1D ReLU networks (He…
Testing RadiX-Nets: Advances in Viable Sparse Topologies
Kevin Kwak, Zack West, Hayden Jananthan +1
The exponential growth of data has sparked computational demands on ML research and industry use. Sparsification of hyper-parametrized deep neural networks (DNNs) creates simpler r…
Uncertainty Propagation in Deep Neural Networks Using Extended Kalman Filtering
Jessica S. Titensky, Hayden Jananthan, Jeremy Kepner
Extended Kalman Filtering (EKF) can be used to propagate and quantify input uncertainty through a Deep Neural Network (DNN) assuming mild hypotheses on the input distribution. This…
Sparse Deep Neural Network Exact Solutions
Jeremy Kepner, Vijay Gadepally, Hayden Jananthan +2
Deep neural networks (DNNs) have emerged as key enablers of machine learning. Applying larger DNNs to more diverse applications is an important challenge. The computations performe…