2 citations · 3 across the 5 of their papers we have counts for
3 papers · 1 filter
SplInterp: Improving our Understanding and Training of Sparse Autoencoders
Jeremy Budd, Javier Ideami, Benjamin Macdowall Rynne +2
Sparse autoencoders (SAEs) have received considerable recent attention as tools for mechanistic interpretability, showing success at extracting interpretable features even from ver…
Graph Laplacian-based Bayesian Multi-fidelity Modeling
Orazio Pinti, Jeremy M. Budd, Franca Hoffmann +1
We present a novel probabilistic approach for generating multi-fidelity data while accounting for errors inherent in both low- and high-fidelity data. In this approach a graph Lapl…
Provably Convergent Data-Driven Convex-Nonconvex Regularization
Zakhar Shumaylov, Jeremy Budd, Subhadip Mukherjee +1
An emerging new paradigm for solving inverse problems is via the use of deep learning to learn a regularizer from data. This leads to high-quality results, but often at the cost of…