715 citations · 1.4k across the 35 of their papers we have counts for
3 papers · 1 filter
AutoDrop: Training Deep Learning Models with Automatic Learning Rate Drop
Yunfei Teng, Jing Wang, Anna Choromanska
Modern deep learning (DL) architectures are trained using variants of the SGD algorithm that is run with a defined learning rate schedule, i.e., the learning ra…
ESAFE: Enterprise Security and Forensics at Scale
Bernard McShea, Kevin Wright, Denley Lam +7
Securing enterprise networks presents challenges in terms of both their size and distributed structure. Data required to detect and characterize malicious activities may be diffuse…
A Theoretical-Empirical Approach to Estimating Sample Complexity of DNNs
Devansh Bisla, Apoorva Nandini Saridena, Anna Choromanska
This paper focuses on understanding how the generalization error scales with the amount of the training data for deep neural networks (DNNs). Existing techniques in statistical lea…