activity
20182022
most citedSafe Predictors for Enforcing Input-Output Specifications

4 citations · 9 across the 5 of their papers we have counts for

collaborators
Showing cs.LGShow all

5 papers · 1 filter

cs.LG2022

Tools and Practices for Responsible AI Engineering

Ryan Soklaski, Justin Goodwin, Olivia Brown +2

Responsible Artificial Intelligence (AI) - the practice of developing, evaluating, and maintaining accurate AI systems that also exhibit essential properties such as robustness and…

cs.LG20213 cited

Principles for Evaluation of AI/ML Model Performance and Robustness

Olivia Brown, Andrew Curtis, Justin Goodwin

The Department of Defense (DoD) has significantly increased its investment in the design, evaluation, and deployment of Artificial Intelligence and Machine Learning (AI/ML) capabil…

cs.LG2020

Fast Training of Deep Neural Networks Robust to Adversarial Perturbations

Justin Goodwin, Olivia Brown, Victoria Helus

Deep neural networks are capable of training fast and generalizing well within many domains. Despite their promising performance, deep networks have shown sensitivities to perturba…

cs.LG20204 cited

Safe Predictors for Enforcing Input-Output Specifications

Stephen Mell, Olivia Brown, Justin Goodwin +1

We present an approach for designing correct-by-construction neural networks (and other machine learning models) that are guaranteed to be consistent with a collection of input-out…

cs.LG2018

Learning Robust Representations for Automatic Target Recognition

Justin A. Goodwin, Olivia M. Brown, Taylor W. Killian +1

Radio frequency (RF) sensors are used alongside other sensing modalities to provide rich representations of the world. Given the high variability of complex-valued target responses…