no code implementations • 3 Oct 2023 • Benjamin Kurt Miller, Marco Federici, Christoph Weniger, Patrick Forré
The objective recovers Neural Posterior Estimation when the model class is normalized and unifies it with Neural Ratio Estimation, combining both into a single objective.
1 code implementation • 21 Apr 2023 • Arnaud Delaunoy, Benjamin Kurt Miller, Patrick Forré, Christoph Weniger, Gilles Louppe
We show empirically that the balanced versions tend to produce conservative posterior approximations on a wide variety of benchmarks.
1 code implementation • 11 Oct 2022 • 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.
1 code implementation • 28 Jan 2022 • Wujie Wang, Minkai Xu, Chen Cai, Benjamin Kurt Miller, Tess Smidt, Yusu Wang, Jian Tang, Rafael Gómez-Bombarelli
Coarse-graining (CG) of molecular simulations simplifies the particle representation by grouping selected atoms into pseudo-beads and drastically accelerates simulation.
1 code implementation • 16 Nov 2021 • Christoph J. Hönes, Benjamin Kurt Miller, Ana M. Heras, Bernard H. Foing
Raw light curve data from exoplanet transits is too complex to naively apply traditional outlier detection methods.
4 code implementations • 15 Nov 2021 • Alex Cole, Benjamin Kurt Miller, Samuel J. Witte, Maxwell X. Cai, Meiert W. Grootes, Francesco Nattino, Christoph Weniger
Sampling-based inference techniques are central to modern cosmological data analysis; these methods, however, scale poorly with dimensionality and typically require approximate or intractable likelihoods.
2 code implementations • NeurIPS 2021 • Benjamin Kurt Miller, Alex Cole, Patrick Forré, Gilles Louppe, Christoph Weniger
Parametric stochastic simulators are ubiquitous in science, often featuring high-dimensional input parameters and/or an intractable likelihood.
1 code implementation • 27 Nov 2020 • Benjamin Kurt Miller, Alex Cole, Gilles Louppe, Christoph Weniger
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 corresponding simulations.
1 code implementation • 19 Aug 2020 • Benjamin Kurt Miller, Mario Geiger, Tess E. Smidt, Frank Noé
Equivariant neural networks (ENNs) are graph neural networks embedded in $\mathbb{R}^3$ and are well suited for predicting molecular properties.
1 code implementation • 4 Jul 2020 • 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".