35 citations · 42 across the 6 of their papers we have counts for
6 papers
Trojans in Artificial Intelligence (TrojAI) Final Report
Kristopher W. Reese, Taylor Kulp-McDowall, Michael Majurski +68
The Intelligence Advanced Research Projects Activity (IARPA) launched the TrojAI program to confront an emerging vulnerability in modern artificial intelligence: the threat of AI T…
A Guide to Failure in Machine Learning: Reliability and Robustness from Foundations to Practice
Eric Heim, Oren Wright, David Shriver
One of the main barriers to adoption of Machine Learning (ML) is that ML models can fail unexpectedly. In this work, we aim to provide practitioners a guide to better understand wh…
Concept-ROT: Poisoning Concepts in Large Language Models with Model Editing
Keltin Grimes, Marco Christiani, David Shriver +1
Model editing methods modify specific behaviors of Large Language Models by altering a small, targeted set of network weights and require very little data and compute. These method…
The SaTML '24 CNN Interpretability Competition: New Innovations for Concept-Level Interpretability
Stephen Casper, Jieun Yun, Joonhyuk Baek +13
Interpretability techniques are valuable for helping humans understand and oversee AI systems. The SaTML 2024 CNN Interpretability Competition solicited novel methods for studying…
DNNV: A Framework for Deep Neural Network Verification
David Shriver, Sebastian Elbaum, Matthew B. Dwyer
Despite the large number of sophisticated deep neural network (DNN) verification algorithms, DNN verifier developers, users, and researchers still face several challenges. First, v…
Refactoring Neural Networks for Verification
David Shriver, Dong Xu, Sebastian Elbaum +1
Deep neural networks (DNN) are growing in capability and applicability. Their effectiveness has led to their use in safety critical and autonomous systems, yet there is a dearth of…