1 citations · 1 across the 7 of their papers we have counts for
7 papers
On the Value of Tokeniser Pretraining in Physics Foundation Models
Hadi Sotoudeh, Payel Mukhopadhyay, Ruben Ohana +4
We investigate the impact of tokeniser pretraining on the accuracy and efficiency of physics emulation. Modern high-resolution simulations produce vast volumes of data spanning div…
Revisiting Deep AC-OPF
Oluwatomisin I. Dada, Neil D. Lawrence
Recent work has proposed machine learning (ML) approaches as fast surrogates for solving AC optimal power flow (AC-OPF), with claims of significant speed-ups and high accuracy. In…
Transformers as Unrolled Inference in Probabilistic Laplacian Eigenmaps: An Interpretation and Potential Improvements
Aditya Ravuri, Neil D. Lawrence
We propose a probabilistic interpretation of transformers as unrolled inference steps assuming a probabilistic Laplacian Eigenmaps model from the ProbDR framework. Our derivation s…
Protein Language Model Zero-Shot Fitness Predictions are Improved by Inference-only Dropout
Aditya Ravuri, Neil D. Lawrence
Protein Language Models (PLMs) such as ESM2 have been shown to be capable of zero-shot prediction of critical scalar properties of proteins (fitness). In this work, we show that in…
On Feature Learning for Titi Monkey Activity Detection
Aditya Ravuri, Jen Muir, Neil D. Lawrence
This paper, a technical summary of our preceding publication, introduces a robust machine learning framework for the detection of vocal activities of Coppery titi monkeys. Utilizin…
Scalable Amortized GPLVMs for Single Cell Transcriptomics Data
Sarah Zhao, Aditya Ravuri, Vidhi Lalchand +1
Dimensionality reduction is crucial for analyzing large-scale single-cell RNA-seq data. Gaussian Process Latent Variable Models (GPLVMs) offer an interpretable dimensionality reduc…
Towards One Model for Classical Dimensionality Reduction: A Probabilistic Perspective on UMAP and t-SNE
Aditya Ravuri, Neil D. Lawrence
This paper shows that dimensionality reduction methods such as UMAP and t-SNE, can be approximately recast as MAP inference methods corresponding to a model introduced in Ravuri et…