95 citations · 221 across the 15 of their papers we have counts for
6 papers · 1 filter
Red Teaming Deep Neural Networks with Feature Synthesis Tools
Stephen Casper, Yuxiao Li, Jiawei Li +4
Interpretable AI tools are often motivated by the goal of understanding model behavior in out-of-distribution (OOD) contexts. Despite the attention this area of study receives, the…
Diagnostics for Deep Neural Networks with Automated Copy/Paste Attacks
Stephen Casper, Kaivalya Hariharan, Dylan Hadfield-Menell
This paper considers the problem of helping humans exercise scalable oversight over deep neural networks (DNNs). Adversarial examples can be useful by helping to reveal weaknesses…
Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networks
Tilman Räuker, Anson Ho, Stephen Casper +1
The last decade of machine learning has seen drastic increases in scale and capabilities. Deep neural networks (DNNs) are increasingly being deployed in the real world. However, th…
Quantifying Local Specialization in Deep Neural Networks
Shlomi Hod, Daniel Filan, Stephen Casper +2
A neural network is locally specialized to the extent that parts of its computational graph (i.e. structure) can be abstractly represented as performing some comprehensible sub-tas…
Robust Feature-Level Adversaries are Interpretability Tools
Stephen Casper, Max Nadeau, Dylan Hadfield-Menell +1
The literature on adversarial attacks in computer vision typically focuses on pixel-level perturbations. These tend to be very difficult to interpret. Recent work that manipulates…
Frivolous Units: Wider Networks Are Not Really That Wide
Stephen Casper, Xavier Boix, Vanessa D'Amario +4
A remarkable characteristic of overparameterized deep neural networks (DNNs) is that their accuracy does not degrade when the network's width is increased. Recent evidence suggests…