6 citations · 8 across the 2 of their papers we have counts for
4 papers
Truncated Marginal Neural Ratio Estimation
Benjamin Kurt Miller, Alex Cole, Patrick Forré +2
Parametric stochastic simulators are ubiquitous in science, often featuring high-dimensional input parameters and/or an intractable likelihood. Performing Bayesian parameter infere…
Simulation-efficient marginal posterior estimation with swyft: stop wasting your precious time
Benjamin Kurt Miller, Alex Cole, Gilles Louppe +1
We present algorithms (a) for nested neural likelihood-to-evidence ratio estimation, and (b) for simulation reuse via an inhomogeneous Poisson point process cache of parameters and…
Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Properties
Benjamin Kurt Miller, Mario Geiger, Tess E. Smidt +1
Equivariant neural networks (ENNs) are graph neural networks embedded in and are well suited for predicting molecular properties. The ENN library e3nn has customizab…
Finding Symmetry Breaking Order Parameters with Euclidean Neural Networks
Tess E. Smidt, Mario Geiger, Benjamin Kurt Miller
Curie's principle states that "when effects show certain asymmetry, this asymmetry must be found in the causes that gave rise to them". We demonstrate that symmetry equivariant neu…