115 citations · 331 across the 37 of their papers we have counts for
6 papers · 1 filter
Representation Engineering: A Top-Down Approach to AI Transparency
Andy Zou, Long Phan, Sarah Chen +18
In this paper, we identify and characterize the emerging area of representation engineering (RepE), an approach to enhancing the transparency of AI systems that draws on insights f…
Importance of equivariant and invariant symmetries for fluid flow modeling
Varun Shankar, Shivam Barwey, Zico Kolter +2
Graph neural networks (GNNs) have shown promise in learning unstructured mesh-based simulations of physical systems, including fluid dynamics. In tandem, geometric deep learning pr…
Understanding Augmentation-based Self-Supervised Representation Learning via RKHS Approximation and Regression
Runtian Zhai, Bingbin Liu, Andrej Risteski +2
Data augmentation is critical to the empirical success of modern self-supervised representation learning, such as contrastive learning and masked language modeling. However, a theo…
Low-Variance Gradient Estimation in Unrolled Computation Graphs with ES-Single
Paul Vicol, Zico Kolter, Kevin Swersky
We propose an evolution strategies-based algorithm for estimating gradients in unrolled computation graphs, called ES-Single. Similarly to the recently-proposed Persistent Evolutio…
Single Image Backdoor Inversion via Robust Smoothed Classifiers
Mingjie Sun, J. Zico Kolter
Backdoor inversion, a central step in many backdoor defenses, is a reverse-engineering process to recover the hidden backdoor trigger inserted into a machine learning model. Existi…
Provably Bounding Neural Network Preimages
Suhas Kotha, Christopher Brix, Zico Kolter +2
Most work on the formal verification of neural networks has focused on bounding the set of outputs that correspond to a given set of inputs (for example, bounded perturbations of a…