Zobrazeno 1 - 10
of 68
pro vyhledávání: '"Dawid, Anna"'
Autor:
Cybiński, Kacper, Płodzień, Marcin, Tomza, Michał, Lewenstein, Maciej, Dauphin, Alexandre, Dawid, Anna
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 what they learn from the data and makes their application to novel dat
Externí odkaz:
http://arxiv.org/abs/2406.10012
Publikováno v:
Proceedings of the 41st International Conference on Machine Learning, PMLR (2024) 235:49717-49732
The second-order properties of the training loss have a massive impact on the optimization dynamics of deep learning models. Fort & Scherlis (2019) discovered that a large excess of positive curvature and local convexity of the loss Hessian is associ
Externí odkaz:
http://arxiv.org/abs/2402.03579
One of the demanding frontiers in ultracold science is identifying laser cooling schemes for complex atoms and molecules, out of their vast spectra of internal states. Motivated by a need to expand the set of available ultracold molecules for applica
Externí odkaz:
http://arxiv.org/abs/2311.08381
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 decision boundary complexity, which is hard to study in the high-dime
Externí odkaz:
http://arxiv.org/abs/2306.07104
Autor:
Dawid, Anna, LeCun, Yann
Current automated systems have crucial limitations that need to be addressed before artificial intelligence can reach human-like levels and bring new technological revolutions. Among others, our societies still lack Level 5 self-driving cars, domesti
Externí odkaz:
http://arxiv.org/abs/2306.02572
Publikováno v:
Phys. Rev. A 106, 043324 (2022)
We theoretically investigate the properties of two interacting ultracold highly magnetic atoms trapped in a one-dimensional harmonic potential. The atoms interact via an anisotropic long-range dipole-dipole interaction, which in one dimension effecti
Externí odkaz:
http://arxiv.org/abs/2205.08965
Autor:
Dawid, Anna, Arnold, Julian, Requena, Borja, Gresch, Alexander, Płodzień, Marcin, Donatella, Kaelan, Nicoli, Kim A., Stornati, Paolo, Koch, Rouven, Büttner, Miriam, Okuła, Robert, Muñoz-Gil, Gorka, Vargas-Hernández, Rodrigo A., Cervera-Lierta, Alba, Carrasquilla, Juan, Dunjko, Vedran, Gabrié, Marylou, Huembeli, Patrick, van Nieuwenburg, Evert, Vicentini, Filippo, Wang, Lei, Wetzel, Sebastian J., Carleo, Giuseppe, Greplová, Eliška, Krems, Roman, Marquardt, Florian, Tomza, Michał, Lewenstein, Maciej, Dauphin, Alexandre
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 learning and kernel methods in supervised, unsupervised, and reinforcement
Externí odkaz:
http://arxiv.org/abs/2204.04198
Autor:
Sroczyńska, Marta, Dawid, Anna, Tomza, Michał, Calarco, Tommaso, Idziaszek, Zbigniew, Jachymski, Krzysztof
Publikováno v:
New J. Phys. 24, 015001 (2022)
Ultracold molecules trapped in optical tweezers show great promise for the implementation of quantum technologies and precision measurements. We study a prototypical scenario where two interacting polar molecules placed in separate traps are controll
Externí odkaz:
http://arxiv.org/abs/2110.05541
Publikováno v:
Mach. Learn.: Sci. Technol. 3, 015002 (2021)
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 expressive ML algorithms, such as neural networks, are black boxes: The use
Externí odkaz:
http://arxiv.org/abs/2108.02154
Autor:
Käming, Niklas, Dawid, Anna, Kottmann, Korbinian, Lewenstein, Maciej, Sengstock, Klaus, Dauphin, Alexandre, Weitenberg, Christof
Publikováno v:
Mach. Learn.: Sci. Technol. 2 035037 (2021)
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 phase boundaries also from noisy and imperfect data and without the kno
Externí odkaz:
http://arxiv.org/abs/2101.05712