34 citations · 42 across the 13 of their papers we have counts for
6 papers · 1 filter
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…
Towards scientific discovery with dictionary learning: Extracting biological concepts from microscopy foundation models
Konstantin Donhauser, Kristina Ulicna, Gemma Elyse Moran +4
Sparse dictionary learning (DL) has emerged as a powerful approach to extract semantically meaningful concepts from the internals of large language models (LLMs) trained mainly in…
The GeometricKernels Package: Heat and Matérn Kernels for Geometric Learning on Manifolds, Meshes, and Graphs
Peter Mostowsky, Vincent Dutordoir, Iskander Azangulov +6
Kernels are a fundamental technical primitive in machine learning. In recent years, kernel-based methods such as Gaussian processes are becoming increasingly important in applicati…
Modelling Technical and Biological Effects in scRNA-seq data with Scalable GPLVMs
Vidhi Lalchand, Aditya Ravuri, Emma Dann +6
Single-cell RNA-seq datasets are growing in size and complexity, enabling the study of cellular composition changes in various biological/clinical contexts. Scalable dimensionality…
Generalised Gaussian Process Latent Variable Models (GPLVM) with Stochastic Variational Inference
Vidhi Lalchand, Aditya Ravuri, Neil D. Lawrence
Gaussian process latent variable models (GPLVM) are a flexible and non-linear approach to dimensionality reduction, extending classical Gaussian processes to an unsupervised learni…