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Found 6 papers, 1 papers with code
Multiscale Flow for Robust and Optimal Cosmological Analysis
We propose Multiscale Flow, a generative Normalizing Flow that creates samples and models the field-level likelihood of two-dimensional cosmological data such as weak lensing.
Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian Inference
We propose a general purpose Bayesian inference algorithm for expensive likelihoods, replacing the stochastic term in the Langevin equation with a deterministic density gradient term.
Translation and Rotation Equivariant Normalizing Flow (TRENF) for Optimal Cosmological Analysis
TRENF is also a generative model of the data, and we show that TRENF samples agree well with the N-body simulations it trained on, and that the inverse mapping of the data agrees well with a Gaussian white noise both visually and on various summary statistics: when this is perfectly achieved the resulting p(x|y) likelihood analysis becomes optimal.
Unsupervised in-distribution anomaly detection of new physics through conditional density estimation
Anomaly detection is a key application of machine learning, but is generally focused on the detection of outlying samples in the low probability density regions of data.
Learning effective physical laws for generating cosmological hydrodynamics with Lagrangian Deep Learning
In this work we propose Lagrangian Deep Learning (LDL) for this purpose, applying it to learn outputs of cosmological hydrodynamical simulations.
Sliced Iterative Normalizing Flows
We develop an iterative (greedy) deep learning (DL) algorithm which is able to transform an arbitrary probability distribution function (PDF) into the target PDF.
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on Fashion-MNIST
