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Found 15 papers, 9 papers with code
Iterated Denoising Energy Matching for Sampling from Boltzmann Densities
Efficiently generating statistically independent samples from an unnormalized probability distribution, such as equilibrium samples of many-body systems, is a foundational problem in science.
On diffusion models for amortized inference: Benchmarking and improving stochastic control and sampling
We study the problem of training diffusion models to sample from a distribution with a given unnormalized density or energy function.
PQMass: Probabilistic Assessment of the Quality of Generative Models using Probability Mass Estimation
The proposed approach enables the estimation of the probability that two sets of samples are drawn from the same distribution, providing a statistically rigorous method for assessing the performance of a single generative model or the comparison of multiple competing models trained on the same dataset.
LtU-ILI: An All-in-One Framework for Implicit Inference in Astrophysics and Cosmology
This paper presents the Learning the Universe Implicit Likelihood Inference (LtU-ILI) pipeline, a codebase for rapid, user-friendly, and cutting-edge machine learning (ML) inference in astrophysics and cosmology.
Improving Gradient-guided Nested Sampling for Posterior Inference
We present a performant, general-purpose gradient-guided nested sampling algorithm, ${\tt GGNS}$, combining the state of the art in differentiable programming, Hamiltonian slice sampling, clustering, mode separation, dynamic nested sampling, and parallelization.
Bayesian Imaging for Radio Interferometry with Score-Based Priors
The inverse imaging task in radio interferometry is a key limiting factor to retrieving Bayesian uncertainties in radio astronomy in a computationally effective manner.
SimBIG: Field-level Simulation-Based Inference of Galaxy Clustering
We demonstrate the robustness of our analysis by showcasing our ability to infer unbiased cosmological constraints from a series of test simulations that are constructed using different forward models than the one used in our training dataset.
Towards equilibrium molecular conformation generation with GFlowNets
Sampling diverse, thermodynamically feasible molecular conformations plays a crucial role in predicting properties of a molecule.
Sampling-Based Accuracy Testing of Posterior Estimators for General Inference
We demonstrate the method on a variety of synthetic examples, and show that TARP can be used to test the results of posterior inference analyses in high-dimensional spaces.
A theory of continuous generative flow networks
Generative flow networks (GFlowNets) are amortized variational inference algorithms that are trained to sample from unnormalized target distributions over compositional objects.
Robust Simulation-Based Inference in Cosmology with Bayesian Neural Networks
Simulation-based inference (SBI) is rapidly establishing itself as a standard machine learning technique for analyzing data in cosmological surveys.
The Cosmic Graph: Optimal Information Extraction from Large-Scale Structure using Catalogues
We a) demonstrate the high sensitivity of modular graph structure to the underlying cosmology in the noise-free limit, b) show that graph neural network summaries automatically combine mass and clustering information through comparisons to traditional statistics, c) demonstrate that networks can still extract information when catalogues are subject to noisy survey cuts, and d) illustrate how nonlinear IMNN summaries can be used as asymptotically optimal compressed statistics for Bayesian simulation-based inference.
Marginal Post Processing of Bayesian Inference Products with Normalizing Flows and Kernel Density Estimators
Bayesian analysis has become an indispensable tool across many different cosmological fields including the study of gravitational waves, the Cosmic Microwave Background and the 21-cm signal from the Cosmic Dawn among other phenomena.
Split personalities in Bayesian Neural Networks: the case for full marginalisation
The true posterior distribution of a Bayesian neural network is massively multimodal.
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.
