55 citations · 184 across the 13 of their papers we have counts for
13 papers
Fast Inference on Astronomical Time Series with Trans-Dimensional Flow Matching Posterior Estimation
Nina van der Meulen, Tin Hadži Veljković, Daniela Huppenkothen +2
The analysis of time series plays an important part in the study of (fast) transient events, including gamma-ray bursts, magnetar bursts, fast radio bursts, and solar flares. A com…
Few-Step Boltzmann Generators via Scalable Likelihood Flow Maps
RuiKang OuYang, Hanlin Yu, Xinyue Ai +7
Recent progress in flow-based generative modeling has led to models that output high-quality samples while using only a small number of function evaluations. However, at present, t…
Simulation-based Inference with the Generalized Kullback-Leibler Divergence
Benjamin Kurt Miller, Marco Federici, Christoph Weniger +1
In Simulation-based Inference, the goal is to solve the inverse problem when the likelihood is only known implicitly. Neural Posterior Estimation commonly fits a normalized density…
Balancing Simulation-based Inference for Conservative Posteriors
Arnaud Delaunoy, Benjamin Kurt Miller, Patrick Forré +2
Conservative inference is a major concern in simulation-based inference. It has been shown that commonly used algorithms can produce overconfident posterior approximations. Balanci…
Peregrine: Sequential simulation-based inference for gravitational wave signals
Uddipta Bhardwaj, James Alvey, Benjamin Kurt Miller +2
The current and upcoming generations of gravitational wave experiments represent an exciting step forward in terms of detector sensitivity and performance. For example, key upgrade…
Contrastive Neural Ratio Estimation for Simulation-based Inference
Benjamin Kurt Miller, Christoph Weniger, Patrick Forré
Likelihood-to-evidence ratio estimation is usually cast as either a binary (NRE-A) or a multiclass (NRE-B) classification task. In contrast to the binary classification framework,…
Generative Coarse-Graining of Molecular Conformations
Wujie Wang, Minkai Xu, Chen Cai +5
Coarse-graining (CG) of molecular simulations simplifies the particle representation by grouping selected atoms into pseudo-beads and drastically accelerates simulation. However, s…
Automatically detecting anomalous exoplanet transits
Christoph J. Hönes, Benjamin Kurt Miller, Ana M. Heras +1
Raw light curve data from exoplanet transits is too complex to naively apply traditional outlier detection methods. We propose an architecture which estimates a latent representati…
Fast and Credible Likelihood-Free Cosmology with Truncated Marginal Neural Ratio Estimation
Alex Cole, Benjamin Kurt Miller, Samuel J. Witte +4
Sampling-based inference techniques are central to modern cosmological data analysis; these methods, however, scale poorly with dimensionality and typically require approximate or…
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…