4 citations · 5 across the 5 of their papers we have counts for
5 papers
LDLT L-Lipschitz Network Weight Parameterization Initialization
Marius F. R. Juston, Ramavarapu S. Sreenivas, Dustin Nottage +1
We analyze initialization dynamics for LDLT-based -Lipschitz layers by deriving the exact marginal output variance when the underlying parameter matrix $W_0\in \mathbb…
LDLT -Lipschitz Network: Generalized Deep End-To-End Lipschitz Network Construction
Marius F. R. Juston, Ramavarapu S. Sreenivas, Dustin Nottage +1
Deep residual networks (ResNets) have demonstrated outstanding success in computer vision tasks, attributed to their ability to maintain gradient flow through deep architectures. S…
On the Enumeration of all Unique Paths of Recombining Trinomial Trees
Ethan Torres, Ramavarapu Sreenivas, Richard Sowers
Recombining trinomial trees are a workhorse for modeling discrete-event systems in option pricing, logistics, and feedback control. Because each node stores a state-dependent quant…
Comparison of Spatiotemporal Networks for Learning Video Related Tasks
Logan Courtney, Ramavarapu Sreenivas
Many methods for learning from video sequences involve temporally processing 2D CNN features from the individual frames or directly utilizing 3D convolutions within high-performing…
Learning from Videos with Deep Convolutional LSTM Networks
Logan Courtney, Ramavarapu Sreenivas
This paper explores the use of convolution LSTMs to simultaneously learn spatial- and temporal-information in videos. A deep network of convolutional LSTMs allows the model to acce…