1 citations · 2 across the 12 of their papers we have counts for
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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…
1-Lipschitz Network Initialization for Certifiably Robust Classification Applications: A Decay Problem
Marius F. R. Juston, Ramavarapu S. Sreenivas, William R. Norris +2
This paper discusses the weight parametrization of two standard 1-Lipschitz network architectures, the Almost-Orthogonal-Layers (AOL) and the SDP-based Lipschitz Layers (SLL). It e…
L-Lipschitz Gershgorin ResNet Network
Marius F. R. Juston, William R. Norris, 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…