81 citations · 210 across the 12 of their papers we have counts for
12 papers
Characterizing out-of-distribution generalization of neural networks: application to the disordered Su-Schrieffer-Heeger model
Kacper Cybiński, Marcin Płodzień, Michał Tomza +3
Machine learning (ML) is a promising tool for the detection of phases of matter. However, ML models are also known for their black-box construction, which hinders understanding of…
Automated Graph-Based Detection of Quantum Control Schemes: Application to Molecular Laser Cooling
Anna Dawid, Niccolò Bigagli, Daniel W. Savin +1
One of the demanding frontiers in ultracold quantum science is identifying laser cooling schemes for complex atoms and molecules out of their vast spectra of internal states. Motiv…
Unveiling the Hessian's Connection to the Decision Boundary
Mahalakshmi Sabanayagam, Freya Behrens, Urte Adomaityte +1
Understanding the properties of well-generalizing minima is at the heart of deep learning research. On the one hand, the generalization of neural networks has been connected to the…
Introduction to Latent Variable Energy-Based Models: A Path Towards Autonomous Machine Intelligence
Anna Dawid, Yann LeCun
Current automated systems have crucial limitations that need to be addressed before artificial intelligence can reach human-like levels and bring new technological revolutions. Amo…
Two ultracold highly magnetic atoms in a one-dimensional harmonic trap
Michał Suchorowski, Anna Dawid, Michał Tomza
We theoretically investigate the properties of two interacting ultracold highly magnetic atoms trapped in a one-dimensional harmonic potential. The atoms interact via an anisotropi…
Modern applications of machine learning in quantum sciences
Anna Dawid, Julian Arnold, Borja Requena +26
In this book, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learn…
Controlling the dynamics of ultracold polar molecules in optical tweezers
Marta Sroczyńska, Anna Dawid, Michał Tomza +3
Ultracold molecules trapped in optical tweezers show great promise for the implementation of quantum technologies and precision measurements. We study a prototypical scenario where…
Hessian-based toolbox for reliable and interpretable machine learning in physics
Anna Dawid, Patrick Huembeli, Michał Tomza +2
Machine learning (ML) techniques applied to quantum many-body physics have emerged as a new research field. While the numerical power of this approach is undeniable, the most expre…
Unsupervised machine learning of topological phase transitions from experimental data
Niklas Käming, Anna Dawid, Korbinian Kottmann +4
Identifying phase transitions is one of the key challenges in quantum many-body physics. Recently, machine learning methods have been shown to be an alternative way of localising p…
Magnetic properties and quench dynamics of two interacting ultracold molecules in a trap
Anna Dawid, Michał Tomza
We theoretically investigate the magnetic properties and nonequilibrium dynamics of two interacting ultracold polar and paramagnetic molecules in a one-dimensional harmonic trap in…
Phase Detection with Neural Networks: Interpreting the Black Box
Anna Dawid, Patrick Huembeli, Michał Tomza +2
Neural networks (NNs) usually hinder any insight into the reasoning behind their predictions. We demonstrate how influence functions can unravel the black box of NN when trained to…
Two interacting ultracold molecules in a one-dimensional harmonic trap
Anna Dawid, Maciej Lewenstein, Michał Tomza
We investigate the properties of two interacting ultracold polar molecules described as distinguishable quantum rigid rotors, trapped in a one-dimensional harmonic potential. The m…