18 citations · 18 across the 9 of their papers we have counts for
9 papers
The Spacetime of Diffusion Models: An Information Geometry Perspective
Rafał Karczewski, Markus Heinonen, Alison Pouplin +2
We present a novel geometric perspective on the latent space of diffusion models. We first show that the standard pullback approach, utilizing the deterministic probability flow OD…
Riemannian Variational Flow Matching for Material and Protein Design
Olga Zaghen, Floor Eijkelboom, Alison Pouplin +4
We present Riemannian Gaussian Variational Flow Matching (RG-VFM), a geometric extension of Variational Flow Matching (VFM) for generative modeling on manifolds. Motivated by the b…
On the curvature of the loss landscape
Alison Pouplin, Hrittik Roy, Sidak Pal Singh +1
One of the main challenges in modern deep learning is to understand why such over-parameterized models perform so well when trained on finite data. A way to analyze this generaliza…
Identifying latent distances with Finslerian geometry
Alison Pouplin, David Eklund, Carl Henrik Ek +1
Riemannian geometry provides us with powerful tools to explore the latent space of generative models while preserving the underlying structure of the data. The latent space can be…
PyRelationAL: a python library for active learning research and development
Paul Scherer, Alison Pouplin, Alice Del Vecchio +6
Active learning (AL) is a sub-field of ML focused on the development of methods to iteratively and economically acquire data by strategically querying new data points that are the…
Pulling back information geometry
Georgios Arvanitidis, Miguel González-Duque, Alison Pouplin +2
Latent space geometry has shown itself to provide a rich and rigorous framework for interacting with the latent variables of deep generative models. The existing theory, however, r…
Density estimation on smooth manifolds with normalizing flows
Dimitris Kalatzis, Johan Ziruo Ye, Alison Pouplin +2
We present a framework for learning probability distributions on topologically non-trivial manifolds, utilizing normalizing flows. Current methods focus on manifolds that are homeo…
Approximating the solution to wave propagation using deep neural networks
Wilhelm E. Sorteberg, Stef Garasto, Alison S. Pouplin +2
Humans gain an implicit understanding of physical laws through observing and interacting with the world. Endowing an autonomous agent with an understanding of physical laws through…
Denoising Adversarial Autoencoders: Classifying Skin Lesions Using Limited Labelled Training Data
Antonia Creswell, Alison Pouplin, Anil A Bharath
We propose a novel deep learning model for classifying medical images in the setting where there is a large amount of unlabelled medical data available, but labelled data is in lim…