34 citations · 42 across the 13 of their papers we have counts for
5 papers · 1 filter
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…
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…