research
∙
07/27/2023
Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback
Reinforcement learning from human feedback (RLHF) is a technique for tra...
share
research
∙
07/08/2023
Measuring the Success of Diffusion Models at Imitating Human Artists
Modern diffusion models have set the state-of-the-art in AI image genera...
research
∙
06/15/2023
Explore, Establish, Exploit: Red Teaming Language Models from Scratch
Deploying Large language models (LLMs) can pose hazards from harmful out...
research
∙
11/18/2022
Diagnostics for Deep Neural Networks with Automated Copy/Paste Attacks
Deep neural networks (DNNs) are powerful, but they can make mistakes tha...
research
∙
09/05/2022
White-Box Adversarial Policies in Deep Reinforcement Learning
Adversarial examples against AI systems pose both risks via malicious at...
research
∙
07/27/2022
Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networks
The last decade of machine learning has seen drastic increases in scale ...
research
∙
10/13/2021
Detecting Modularity in Deep Neural Networks
A neural network is modular to the extent that parts of its computationa...
research
∙
10/07/2021
One Thing to Fool them All: Generating Interpretable, Universal, and Physically-Realizable Adversarial Features
It is well understood that modern deep networks are vulnerable to advers...
research
∙
03/04/2021
Clusterability in Neural Networks
The learned weights of a neural network have often been considered devoi...
research
∙
10/12/2020
The Achilles Heel Hypothesis: Pitfalls for AI Systems via Decision Theoretic Adversaries
As progress in AI continues to advance at a rapid pace, it is crucial to...
research
∙
06/07/2020
Probing Neural Dialog Models for Conversational Understanding
The predominant approach to open-domain dialog generation relies on end-...
research
∙
12/10/2019