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Found 8 papers, 5 papers with code
Scalable Optimal Transport Methods in Machine Learning: A Contemporary Survey
This paper is about where and how optimal transport is used in machine learning with a focus on the question of scalable optimal transport.
A Practitioner's Guide to Bayesian Inference in Pharmacometrics using Pumas
Many of the algorithms, codes, and ideas presented in this paper are highly applicable to clinical research and statistical learning at large but we chose to focus our discussions on pharmacometrics in this paper to have a narrower scope in mind and given the nature of Pumas as a software primarily for pharmacometricians.
Simplifying deflation for non-convex optimization with applications in Bayesian inference and topology optimization
Finally, a number of applications of the proposed methodology in the fields of approximate Bayesian inference and topology optimization are presented.
AbstractDifferentiation.jl: Backend-Agnostic Differentiable Programming in Julia
No single Automatic Differentiation (AD) system is the optimal choice for all problems.
Bayesian Neural Ordinary Differential Equations
We demonstrate the successful integration of Neural ODEs with the above Bayesian inference frameworks on classical physical systems, as well as on standard machine learning datasets like MNIST, using GPU acceleration.
DynamicPPL: Stan-like Speed for Dynamic Probabilistic Models
Since DynamicPPL is a modular, stand-alone library, any probabilistic programming system written in Julia, such as Turing. jl, can use DynamicPPL to specify models and trace their model parameters.
Bijectors.jl: Flexible transformations for probability distributions
Transforming one probability distribution to another is a powerful tool in Bayesian inference and machine learning.
AdvancedHMC.jl: A robust, modular and efficient implementation of advanced HMC algorithms
Stan's Hamilton Monte Carlo (HMC) has demonstrated remarkable sampling robustness and efficiency in a wide range of Bayesian inference problems through carefully crafted adaption schemes to the celebrated No-U-Turn sampler (NUTS) algorithm.
