1 citations · 2 across the 3 of their papers we have counts for
3 papers
stat.ML2024★ 1 cited
Likelihood approximations via Gaussian approximate inference
Thang D. Bui
Non-Gaussian likelihoods are essential for modelling complex real-world observations but pose significant computational challenges in learning and inference. Even with Gaussian pri…
cs.LG2024
Measuring Sharpness in Grokking
Jack Miller, Patrick Gleeson, Charles O'Neill +2
Neural networks sometimes exhibit grokking, a phenomenon where perfect or near-perfect performance is achieved on a validation set well after the same performance has been obtained…
cs.CL2023★ 1 cited
Adversarial Fine-Tuning of Language Models: An Iterative Optimisation Approach for the Generation and Detection of Problematic Content
Charles O'Neill, Jack Miller, Ioana Ciuca +2
In this paper, we tackle the emerging challenge of unintended harmful content generation in Large Language Models (LLMs) with a novel dual-stage optimisation technique using advers…