2 citations · 3 across the 5 of their papers we have counts for
5 papers
Spectral Effects Of Heavy-Tailed Vertex Noise In Geometric Graphs
Ben Cardoen, Jeremy Budd, Enrico Amico +2
We characterize which local matrix structures saturate Weyl's eigenvalue perturbation bound for graph Laplacians under geometrically constrained vertex displacements. Geometric gra…
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…
Weakly Convex Regularisers for Inverse Problems: Convergence of Critical Points and Primal-Dual Optimisation
Zakhar Shumaylov, Jeremy Budd, Subhadip Mukherjee +1
Variational regularisation is the primary method for solving inverse problems, and recently there has been considerable work leveraging deeply learned regularisation for enhanced p…
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…