Search Results for author: Alexandre Dauphin

Found 5 papers, 4 papers with code

Unsupervised machine learning of topological phase transitions from experimental data

1 code implementation14 Jan 2021 Niklas Käming, Anna Dawid, Korbinian Kottmann, Maciej Lewenstein, Klaus Sengstock, Alexandre Dauphin, Christof Weitenberg

Recently, machine learning methods have been shown to be an alternative way of localising phase boundaries also from noisy and imperfect data and without the knowledge of the order parameter.

Anomaly Detection Quantum Gases Disordered Systems and Neural Networks Mesoscale and Nanoscale Physics Quantum Physics

Phase Detection with Neural Networks: Interpreting the Black Box

1 code implementation9 Apr 2020 Anna Dawid, Patrick Huembeli, Michał Tomza, Maciej Lewenstein, Alexandre Dauphin

Neural networks (NNs) normally do not allow any insight into the reasoning behind their predictions.

Quantum Physics Disordered Systems and Neural Networks

Circular dichroism in high-order harmonic generation: Heralding topological phases and transitions in Chern insulators

1 code implementation4 Jul 2018 Alexis Chacón, Dasol Kim, Wei Zhu, Shane P. Kelly, Alexandre Dauphin, Emilio Pisanty, Andrew S. Maxwell, Antonio Picón, Marcelo F. Ciappina, Dong Eon Kim, Christopher Ticknor, Avadh Saxena, Maciej Lewenstein

Topological materials are of interest to both fundamental science and advanced technologies, because topological states are robust with respect to perturbations and dissipation.

Mesoscale and Nanoscale Physics Quantum Physics

Automated discovery of characteristic features of phase transitions in many-body localization

no code implementations1 Jun 2018 Patrick Huembeli, Alexandre Dauphin, Peter Wittek, Christian Gogolin

We identify a new "order parameter" for the disorder driven many-body localization (MBL) transition by leveraging artificial intelligence.

Quantum Physics Disordered Systems and Neural Networks

Identifying Quantum Phase Transitions with Adversarial Neural Networks

1 code implementation11 Oct 2017 Patrick Huembeli, Alexandre Dauphin, Peter Wittek

Adversarial domain adaptation uses both types of data to create invariant feature extracting layers in a deep learning architecture.

Domain Adaptation Feature Engineering

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