23 citations · 34 across the 9 of their papers we have counts for
11 papers
Color Teams for Machine Learning Development
Josh Kalin, David Noever, Matthew Ciolino
Machine learning and software development share processes and methodologies for reliably delivering products to customers. This work proposes the use of a new teaming construct for…
Automating Defense Against Adversarial Attacks: Discovery of Vulnerabilities and Application of Multi-INT Imagery to Protect Deployed Models
Josh Kalin, David Noever, Matthew Ciolino +2
Image classification is a common step in image recognition for machine learning in overhead applications. When applying popular model architectures like MobileNetV2, known vulnerab…
A Modified Drake Equation for Assessing Adversarial Risk to Machine Learning Models
Josh Kalin, David Noever, Matthew Ciolino
Machine learning models present a risk of adversarial attack when deployed in production. Quantifying the contributing factors and uncertainties using empirical measures could assi…
Back Translation Survey for Improving Text Augmentation
Matthew Ciolino, David Noever, Josh Kalin
Natural Language Processing (NLP) relies heavily on training data. Transformers, as they have gotten bigger, have required massive amounts of training data. To satisfy this require…
Fortify Machine Learning Production Systems: Detect and Classify Adversarial Attacks
Matthew Ciolino, Josh Kalin, David Noever
Production machine learning systems are consistently under attack by adversarial actors. Various deep learning models must be capable of accurately detecting fake or adversarial in…
Local Translation Services for Neglected Languages
David Noever, Josh Kalin, Matt Ciolino +2
Taking advantage of computationally lightweight, but high-quality translators prompt consideration of new applications that address neglected languages. Locally run translators for…
The Chess Transformer: Mastering Play using Generative Language Models
David Noever, Matt Ciolino, Josh Kalin
This work demonstrates that natural language transformers can support more generic strategic modeling, particularly for text-archived games. In addition to learning natural languag…
Black Box to White Box: Discover Model Characteristics Based on Strategic Probing
Josh Kalin, Matthew Ciolino, David Noever +1
In Machine Learning, White Box Adversarial Attacks rely on knowing underlying knowledge about the model attributes. This works focuses on discovering to distrinct pieces of model i…
The Go Transformer: Natural Language Modeling for Game Play
Matthew Ciolino, David Noever, Josh Kalin
This work applies natural language modeling to generate plausible strategic moves in the ancient game of Go. We train the Generative Pretrained Transformer (GPT-2) to mimic the sty…
Systematic Attack Surface Reduction For Deployed Sentiment Analysis Models
Josh Kalin, David Noever, Gerry Dozier
This work proposes a structured approach to baselining a model, identifying attack vectors, and securing the machine learning models after deployment. This method for securing each…
Discoverability in Satellite Imagery: A Good Sentence is Worth a Thousand Pictures
David Noever, Wes Regian, Matt Ciolino +3
Small satellite constellations provide daily global coverage of the earth's landmass, but image enrichment relies on automating key tasks like change detection or feature searches.…