Introduces graph-to-image prediction of per-node dynamic stability landscapes in oscillator networks from topology, releases two 10k-graph datasets, and shows GNN-CNN models achieve good accuracy with cross-size generalization.
Fashionable Modelling with Flux
6 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
Machine learning as a discipline has seen an incredible surge of interest in recent years due in large part to a perfect storm of new theory, superior tooling, renewed interest in its capabilities. We present in this paper a framework named Flux that shows how further refinement of the core ideas of machine learning, built upon the foundation of the Julia programming language, can yield an environment that is simple, easily modifiable, and performant. We detail the fundamental principles of Flux as a framework for differentiable programming, give examples of models that are implemented within Flux to display many of the language and framework-level features that contribute to its ease of use and high productivity, display internal compiler techniques used to enable the acceleration and performance that lies at the heart of Flux, and finally give an overview of the larger ecosystem that Flux fits inside of.
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Universal Differential Equations unify scientific models with machine learning by embedding flexible approximators into differential equations, enabling applications from biological mechanism discovery to high-dimensional optimization.
Supervised learning approaches including kernel estimation, random forests, additive models, and deep learning are proposed to estimate conditional covariance matrices for removing multivariate environmental influences in SHM beyond standard response surface modeling.
Zygote is a differentiable programming system in Julia that supports gradients for nearly all language constructs while generating high-performance code without user refactoring.
Design guidelines and a Go library (Infergo) for deploying probabilistic programming in production systems, with benchmark comparisons.