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pro vyhledávání: '"Altmann P"'
This paper addresses the computation of ground states of multicomponent Bose-Einstein condensates, defined as the global minimiser of an energy functional on an infinite-dimensional generalised oblique manifold. We establish the existence of the grou
Externí odkaz:
http://arxiv.org/abs/2411.09617
According to the Strong Lottery Ticket Hypothesis, every sufficiently large neural network with randomly initialized weights contains a sub-network which - still with its random weights - already performs as well for a given task as the trained super
Externí odkaz:
http://arxiv.org/abs/2411.04658
We present a Bayesian algorithm to identify generators of open quantum system dynamics, described by a Lindblad master equation, that are compatible with measured experimental data. The algorithm, based on a Markov Chain Monte Carlo approach, assumes
Externí odkaz:
http://arxiv.org/abs/2410.17942
Autor:
Schurz N, Sariaslani L, Altmann P, Leutmezer F, Mitsch C, Pemp B, Rommer P, Zrzavy T, Berger T, Bsteh G
Publikováno v:
Eye and Brain, Vol Volume 13, Pp 59-69 (2021)
Natascha Schurz,1,* Lydia Sariaslani,1,* Patrick Altmann,1 Fritz Leutmezer,1 Christoph Mitsch,2 Berthold Pemp,2 Paulus Rommer,1 Tobias Zrzavy,1 Thomas Berger,1 Gabriel Bsteh1 1Department of Neurology, Medical University of Vienna, Vienna, Austria; 2D
Externí odkaz:
https://doaj.org/article/370c4bef77d34468863765d22ae44ebe
Autor:
Ijishakin, Ayodeji, Aguila, Ana Lawry, Levitis, Elizabeth, Abdulaal, Ahmed, Altmann, Andre, Cole, James
Combining neuroimaging datasets from multiple sites and scanners can help increase statistical power and thus provide greater insight into subtle neuroanatomical effects. However, site-specific effects pose a challenge by potentially obscuring the bi
Externí odkaz:
http://arxiv.org/abs/2408.15890
The rapid evolution of deep learning and its integration with autonomous driving systems have led to substantial advancements in 3D perception using multimodal sensors. Notably, radar sensors show greater robustness compared to cameras and lidar unde
Externí odkaz:
http://arxiv.org/abs/2408.06772
Autor:
Roshani, Navid, Stein, Jonas, Zorn, Maximilian, Kölle, Michael, Altmann, Philipp, Linnhoff-Popien, Claudia
A central challenge in quantum machine learning is the design and training of parameterized quantum circuits (PQCs). Much like in deep learning, vanishing gradients pose significant obstacles to the trainability of PQCs, arising from various sources.
Externí odkaz:
http://arxiv.org/abs/2408.04751
Autor:
Altmann, Philipp, Schönberger, Julian, Illium, Steffen, Zorn, Maximilian, Ritz, Fabian, Haider, Tom, Burton, Simon, Gabor, Thomas
Emergent effects can arise in multi-agent systems (MAS) where execution is decentralized and reliant on local information. These effects may range from minor deviations in behavior to catastrophic system failures. To formally define these effects, we
Externí odkaz:
http://arxiv.org/abs/2408.04514
Autor:
Kölle, Michael, Seidl, Daniel, Zorn, Maximilian, Altmann, Philipp, Stein, Jonas, Gabor, Thomas
Quantum Reinforcement Learning (QRL) offers potential advantages over classical Reinforcement Learning, such as compact state space representation and faster convergence in certain scenarios. However, practical benefits require further validation. QR
Externí odkaz:
http://arxiv.org/abs/2408.01187
Autor:
Kölle, Michael, Schneider, Karola, Egger, Sabrina, Topp, Felix, Phan, Thomy, Altmann, Philipp, Nüßlein, Jonas, Linnhoff-Popien, Claudia
In recent years, Multi-Agent Reinforcement Learning (MARL) has found application in numerous areas of science and industry, such as autonomous driving, telecommunications, and global health. Nevertheless, MARL suffers from, for instance, an exponenti
Externí odkaz:
http://arxiv.org/abs/2407.20739