Christopher Iliffe Sprague — accepted AI-conference papers

2025

Energy-Based Flow Matching for Generating 3D Molecular Structure

Wenyin Zhou, Christopher Iliffe Sprague, Vsevolod Viliuga, Matteo Tadiello, Arne Elofsson, Hossein Azizpour

ICML 2025poster

Molecular structure generation is a fundamental problem that involves determining the 3D positions of molecules' constituents. It has crucial biological applications, such as molecular docking, protein folding, and molecular design. Recent advances in generative modeling, such as diffusion models an…

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2022

Fully-Probabilistic Terrain Modelling and Localization With Stochastic Variational Gaussian Process Maps

Ignacio Torroba, Christopher Iliffe Sprague, John Folkesson

RA-L 2022

Gaussian processes (GPs) are becoming a standard tool to build terrain representations thanks to their capacity to model map uncertainty. This effectively yields a reliability measure of the areas of the map, which can be directly utilized by Bayes filtering algorithms in robot localization problems

Cited by 15 Source ↗Scholar ↗

2020

PointNetKL: Deep Inference for GICP Covariance Estimation in Bathymetric SLAM

Ignacio Torroba, Christopher Iliffe Sprague, Nils Bore, John Folkesson

RA-L 2020

Registration methods for point clouds have become a key component of many SLAM systems on autonomous vehicles. However, an accurate estimate of the uncertainty of such registration is a key requirement to a consistent fusion of this kind of measurements in a SLAM filter. This estimate, which is norm

Cited by 21 Source ↗Scholar ↗