Zobrazeno 1 - 10
of 7 839
pro vyhledávání: '"ALTMANN, P."'
In sequential decision-making environments, the primary approaches for training agents are Reinforcement Learning (RL) and Imitation Learning (IL). Unlike RL, which relies on modeling a reward function, IL leverages expert demonstrations, where an ex
Externí odkaz:
http://arxiv.org/abs/2412.07617
Autor:
Vlaskin, Alexey, Altmann, Eduardo G.
Predicting missing links in complex networks requires algorithms that are able to explore statistical regularities in the existing data. Here we investigate the interplay between algorithm efficiency and network structures through the introduction of
Externí odkaz:
http://arxiv.org/abs/2412.03757
Over a smooth projective toric variety we study toric sheaves, that is, reflexive sheaves equivariant with respect to the acting torus, from a polyhedral point of view. One application is the explicit construction of the torus invariant universal ext
Externí odkaz:
http://arxiv.org/abs/2412.03476
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:
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