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Found 10 papers, 9 papers with code
Simulation-based Inference with the Generalized Kullback-Leibler Divergence
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.
Balancing Simulation-based Inference for Conservative Posteriors
We show empirically that the balanced versions tend to produce conservative posterior approximations on a wide variety of benchmarks.
Contrastive Neural Ratio Estimation
Likelihood-to-evidence ratio estimation is usually cast as either a binary (NRE-A) or a multiclass (NRE-B) classification task.
Generative Coarse-Graining of Molecular Conformations
Coarse-graining (CG) of molecular simulations simplifies the particle representation by grouping selected atoms into pseudo-beads and drastically accelerates simulation.
Automatically detecting anomalous exoplanet transits
Raw light curve data from exoplanet transits is too complex to naively apply traditional outlier detection methods.
Fast and Credible Likelihood-Free Cosmology with Truncated Marginal Neural Ratio Estimation
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.
Truncated Marginal Neural Ratio Estimation
Parametric stochastic simulators are ubiquitous in science, often featuring high-dimensional input parameters and/or an intractable likelihood.
Simulation-efficient marginal posterior estimation with swyft: stop wasting your precious time
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.
Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Properties
Equivariant neural networks (ENNs) are graph neural networks embedded in $\mathbb{R}^3$ and are well suited for predicting molecular properties.
Finding Symmetry Breaking Order Parameters with Euclidean Neural Networks
Curie's principle states that "when effects show certain asymmetry, this asymmetry must be found in the causes that gave rise to them".
