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Found 4 papers, 2 papers with code
Symbolic expression generation via Variational Auto-Encoder
There are many problems in physics, biology, and other natural sciences in which symbolic regression can provide valuable insights and discover new laws of nature.
Segmentation of EM showers for neutrino experiments with deep graph neural networks
For the clustering of overlapping showers, we use a modified hierarchical density-based clustering algorithm.
Online detection of failures generated by storage simulator
In this work, we challenge two problems: 1) lack of storage data in the methods above by creating a simulator and 2) applying existing online algorithms that can faster detect a failure occurred in one of the components.
Black-Box Optimization with Local Generative Surrogates
To address such cases, we introduce the use of deep generative models to iteratively approximate the simulator in local neighborhoods of the parameter space.
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