Zobrazeno 1 - 10
of 682
pro vyhledávání: '"HÜBNER, WOLFGANG"'
Autor:
Zhang, G. P., Liu, Y. Q., Si, M. S., Allbritton, Nicholas, Bai, Y. H., Hübner, Wolfgang, George, Thomas F.
All-optical spin switching (AOS) is a new phenomenon found in a small group of magnetic media, where a single laser pulse can switch spins from one direction to another, without assistance of a magnetic field, on a time scale much shorter than existi
Externí odkaz:
http://arxiv.org/abs/2406.11099
An appropriate data basis grants one of the most important aspects for training and evaluating probabilistic trajectory prediction models based on neural networks. In this regard, a common shortcoming of current benchmark datasets is their limitation
Externí odkaz:
http://arxiv.org/abs/2404.04397
Data pooling offers various advantages, such as increasing the sample size, improving generalization, reducing sampling bias, and addressing data sparsity and quality, but it is not straightforward and may even be counterproductive. Assessing the eff
Externí odkaz:
http://arxiv.org/abs/2311.09768
Probabilistic models for sequential data are the basis for a variety of applications concerned with processing timely ordered information. The predominant approach in this domain is given by recurrent neural networks, implementing either an approxima
Externí odkaz:
http://arxiv.org/abs/2205.01754
Autor:
Xie, Menglin, Ma, Shangjie, Li, Weiqi, Song, Jie, Jiang, Yongyuan, Jing, Yuhang, Li, Chun, Lefkidis, Georgios, Hübner, Wolfgang, Jin, Wei
Publikováno v:
Journal of Chemical Physics; 7/28/2024, Vol. 161 Issue 4, p1-11, 11p
Deep learning-based models, such as recurrent neural networks (RNNs), have been applied to various sequence learning tasks with great success. Following this, these models are increasingly replacing classic approaches in object tracking applications
Externí odkaz:
http://arxiv.org/abs/2107.00422