most citedPruning Bayesian Networks for Efficient Computation

31 citations · 32 across the 2 of their papers we have counts for

collaborators

7 papers

cs.CR2017

Automated U.S Diplomatic Cables Security Classification: Topic Model Pruning vs. Classification Based on Clusters

Khudran Alzhrani, Ethan M. Rudd, C. Edward Chow +1

The U.S Government has been the target for cyber-attacks from all over the world. Just recently, former President Obama accused the Russian government of the leaking emails to Wiki…

cs.CR2017

Open Set Intrusion Recognition for Fine-Grained Attack Categorization

Steve Cruz, Cora Coleman, Ethan M. Rudd +1

Confidently distinguishing a malicious intrusion over a network is an important challenge. Most intrusion detection system evaluations have been performed in a closed set protocol…

cs.CV2016

Adversarial Diversity and Hard Positive Generation

Andras Rozsa, Ethan M. Rudd, Terrance E. Boult

State-of-the-art deep neural networks suffer from a fundamental problem - they misclassify adversarial examples formed by applying small perturbations to inputs. In this paper, we…

cs.CV2016

PARAPH: Presentation Attack Rejection by Analyzing Polarization Hypotheses

Ethan M. Rudd, Manuel Gunther, Terrance E. Boult

For applications such as airport border control, biometric technologies that can process many capture subjects quickly, efficiently, with weak supervision, and with minimal discomf…

cs.CR2016

CALIPER: Continuous Authentication Layered with Integrated PKI Encoding Recognition

Ethan M. Rudd, Terrance E. Boult

Architectures relying on continuous authentication require a secure way to challenge the user's identity without trusting that the Continuous Authentication Subsystem (CAS) has not…

cs.AI201331 cited

Pruning Bayesian Networks for Efficient Computation

Michelle Baker, Terrance E. Boult

This paper analyzes the circumstances under which Bayesian networks can be pruned in order to reduce computational complexity without altering the computation for variables of inte…