no code implementations • 6 Feb 2024 • Zach Furman, Edmund Lau
The \textit{local learning coefficient} (LLC) is a principled way of quantifying model complexity, originally derived in the context of Bayesian statistics using singular learning theory (SLT).
no code implementations • 10 Oct 2023 • Zhongtian Chen, Edmund Lau, Jake Mendel, Susan Wei, Daniel Murfet
We investigate phase transitions in a Toy Model of Superposition (TMS) using Singular Learning Theory (SLT).
1 code implementation • 23 Aug 2023 • Edmund Lau, Daniel Murfet, Susan Wei
Deep neural networks (DNN) are singular statistical models which exhibit complex degeneracies.
1 code implementation • 13 Feb 2023 • Susan Wei, Edmund Lau
In this work, we advocate for the importance of singular learning theory (SLT) as it pertains to the theory and practice of variational inference in Bayesian neural networks (BNNs).