103 citations · 193 across the 10 of their papers we have counts for
10 papers
SE(3) Equivariant Augmented Coupling Flows
Laurence I. Midgley, Vincent Stimper, Javier Antorán +3
Coupling normalizing flows allow for fast sampling and density evaluation, making them the tool of choice for probabilistic modeling of physical systems. However, the standard coup…
normflows: A PyTorch Package for Normalizing Flows
Vincent Stimper, David Liu, Andrew Campbell +4
Normalizing flows model probability distributions through an expressive tractable density. They transform a simple base distribution, such as a Gaussian, through a sequence of inve…
Flow Annealed Importance Sampling Bootstrap
Laurence Illing Midgley, Vincent Stimper, Gregor N. C. Simm +2
Normalizing flows are tractable density models that can approximate complicated target distributions, e.g. Boltzmann distributions of physical systems. However, current methods for…
Probing the Robustness of Independent Mechanism Analysis for Representation Learning
Joanna Sliwa, Shubhangi Ghosh, Vincent Stimper +2
One aim of representation learning is to recover the original latent code that generated the data, a task which requires additional information or inductive biases. A recently prop…
AutoML Two-Sample Test
Jonas M. Kübler, Vincent Stimper, Simon Buchholz +2
Two-sample tests are important in statistics and machine learning, both as tools for scientific discovery as well as to detect distribution shifts. This led to the development of m…
Bootstrap Your Flow
Laurence Illing Midgley, Vincent Stimper, Gregor N. C. Simm +1
Normalizing flows are flexible, parameterized distributions that can be used to approximate expectations from intractable distributions via importance sampling. However, current fl…
Resampling Base Distributions of Normalizing Flows
Vincent Stimper, Bernhard Schölkopf, José Miguel Hernández-Lobato
Normalizing flows are a popular class of models for approximating probability distributions. However, their invertible nature limits their ability to model target distributions who…
Independent mechanism analysis, a new concept?
Luigi Gresele, Julius von Kügelgen, Vincent Stimper +2
Independent component analysis provides a principled framework for unsupervised representation learning, with solid theory on the identifiability of the latent code that generated…
A machine learning route between band mapping and band structure
Rui Patrick Xian, Vincent Stimper, Marios Zacharias +9
Electronic band structure (BS) and crystal structure are the two complementary identifiers of solid state materials. While convenient instruments and reconstruction algorithms have…
Multidimensional Contrast Limited Adaptive Histogram Equalization
Vincent Stimper, Stefan Bauer, Ralph Ernstorfer +2
Contrast enhancement is an important preprocessing technique for improving the performance of downstream tasks in image processing and computer vision. Among the existing approache…