25 citations · 33 across the 5 of their papers we have counts for
5 papers
Is In-Context Learning in Large Language Models Bayesian? A Martingale Perspective
Fabian Falck, Ziyu Wang, Chris Holmes
In-context learning (ICL) has emerged as a particularly remarkable characteristic of Large Language Models (LLM): given a pretrained LLM and an observed dataset, LLMs can make pred…
Approximations to the Fisher Information Metric of Deep Generative Models for Out-Of-Distribution Detection
Sam Dauncey, Chris Holmes, Christopher Williams +1
Likelihood-based deep generative models such as score-based diffusion models and variational autoencoders are state-of-the-art machine learning models approximating high-dimensiona…
A Unified Framework for U-Net Design and Analysis
Christopher Williams, Fabian Falck, George Deligiannidis +3
U-Nets are a go-to, state-of-the-art neural architecture across numerous tasks for continuous signals on a square such as images and Partial Differential Equations (PDE), however t…
A Multi-Resolution Framework for U-Nets with Applications to Hierarchical VAEs
Fabian Falck, Christopher Williams, Dominic Danks +5
U-Net architectures are ubiquitous in state-of-the-art deep learning, however their regularisation properties and relationship to wavelets are understudied. In this paper, we formu…
Neural Score Matching for High-Dimensional Causal Inference
Oscar Clivio, Fabian Falck, Brieuc Lehmann +2
Traditional methods for matching in causal inference are impractical for high-dimensional datasets. They suffer from the curse of dimensionality: exact matching and coarsened exact…