Search Results for author: Sebastian J. Wetzel

Found 6 papers, 0 papers with code

How to get the most out of Twinned Regression Methods

no code implementations3 Jan 2023 Sebastian J. Wetzel

Twinned regression methods are designed to solve the dual problem to the original regression problem, predicting differences between regression targets rather then the targets themselves.

regression

Unsupervised Learning of Rydberg Atom Array Phase Diagram with Siamese Neural Networks

no code implementations9 May 2022 Zakaria Patel, Ejaaz Merali, Sebastian J. Wetzel

We introduce an unsupervised machine learning method based on Siamese Neural Networks (SNN) to detect phase boundaries.

Twin Neural Network Regression is a Semi-Supervised Regression Algorithm

no code implementations11 Jun 2021 Sebastian J. Wetzel, Roger G. Melko, Isaac Tamblyn

Twin neural network regression (TNNR) is a semi-supervised regression algorithm, it can be trained on unlabelled data points as long as other, labelled anchor data points, are present.

regression

Twin Neural Network Regression

no code implementations29 Dec 2020 Sebastian J. Wetzel, Kevin Ryczko, Roger G. Melko, Isaac Tamblyn

The solution of a traditional regression problem is then obtained by averaging over an ensemble of all predicted differences between the targets of an unseen data point and all training data points.

regression

Discovering Symmetry Invariants and Conserved Quantities by Interpreting Siamese Neural Networks

no code implementations9 Mar 2020 Sebastian J. Wetzel, Roger G. Melko, Joseph Scott, Maysum Panju, Vijay Ganesh

It turns out that in the process of learning which datapoints belong to the same event or field configuration, these SNNs also learn the relevant symmetry invariants and conserved quantities.

Spectral Reconstruction with Deep Neural Networks

no code implementations10 May 2019 Lukas Kades, Jan M. Pawlowski, Alexander Rothkopf, Manuel Scherzer, Julian M. Urban, Sebastian J. Wetzel, Nicolas Wink, Felix P. G. Ziegler

We explore artificial neural networks as a tool for the reconstruction of spectral functions from imaginary time Green's functions, a classic ill-conditioned inverse problem.

Bayesian Inference Spectral Reconstruction

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