Search Results for author:
Found 7 papers, 5 papers with code
Evidence Networks: simple losses for fast, amortized, neural Bayesian model comparison
Multiple real-world and synthetic examples illustrate that Evidence Networks are explicitly independent of dimensionality of the parameter space and scale mildly with the complexity of the posterior probability density function.
Rediscovering orbital mechanics with machine learning
We then use symbolic regression to discover an analytical expression for the force law implicitly learned by the neural network, which our results showed is equivalent to Newton's law of gravitation.
Probabilistic Mass Mapping with Neural Score Estimation
We introduce a novel methodology allowing for efficient sampling of the high-dimensional Bayesian posterior of the weak lensing mass-mapping problem, and relying on simulations for defining a fully non-Gaussian prior.
Solving high-dimensional parameter inference: marginal posterior densities & Moment Networks
High-dimensional probability density estimation for inference suffers from the "curse of dimensionality".
Likelihood-free inference with neural compression of DES SV weak lensing map statistics
We present likelihood-free cosmological parameter inference using weak lensing maps from the Dark Energy Survey (DES) SV data, using neural data compression of weak lensing map summary statistics.
Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics
Deep learning dark matter map reconstructions from DES SV weak lensing data
With a validation set of 8000 simulated DES SV data realisations, compared to Wiener filtering with a fixed power spectrum, the DeepMass method improved the mean-square-error (MSE) by 11 per cent.
Cosmology and Nongalactic Astrophysics
Fast Sampling from Wiener Posteriors for Image Data with Dataflow Engines
We use Dataflow Engines (DFE) to construct an efficient Wiener filter of noisy and incomplete image data, and to quickly draw probabilistic samples of the compatible true underlying images from the Wiener posterior.
Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics
