Zobrazeno 1 - 10
of 32 644
pro vyhledávání: '"A, Horsch"'
Autor:
Olaf, Schultz (AUTHOR), Schultz, Olaf (AUTHOR)
Publikováno v:
Agrarzeitung. 8/23/2024, Issue 34, p16-16. 1p.
Autor:
Agarwal, Rohit, Naidu, Karaka Prasanth, Horsch, Alexander, Agarwal, Krishna, Prasad, Dilip K.
We study the online learning problem characterized by the varying input feature space of streaming data. Although LSTMs have been employed to effectively capture the temporal nature of streaming data, they cannot handle the dimension-varying streams
Externí odkaz:
http://arxiv.org/abs/2410.17394
In multi-task reinforcement learning, agents train on a fixed set of tasks and have to generalise to new ones. Recent work has shown that increased exploration improves this generalisation, but it remains unclear why exactly that is. In this paper, w
Externí odkaz:
http://arxiv.org/abs/2410.03565
The domain of online learning has experienced multifaceted expansion owing to its prevalence in real-life applications. Nonetheless, this progression operates under the assumption that the input feature space of the streaming data remains constant. I
Externí odkaz:
http://arxiv.org/abs/2404.04903
Autor:
BENEŠ, Ladislav
This bacalary work is about comparison seed drills Horsch Pronto 6 DC and John Deere 740 A in conventional and precision agriculture. Both seed drills are in aggregation with tractor from John Deere of the same power. The main difference is tractor g
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______2186::8496aadbddbaae578eb9fd09972678d8
http://www.nusl.cz/ntk/nusl-517887
http://www.nusl.cz/ntk/nusl-517887
Publikováno v:
Agrarzeitung. 3/15/2024, Issue 11, p11-11. 1p.
Autor:
HORSCH, VOLKER
Publikováno v:
Mennonite Quarterly Review; Apr2018, Vol. 92 Issue 2, p299-305, 7p
Untrimmed videos have interrelated events, dependencies, context, overlapping events, object-object interactions, domain specificity, and other semantics that are worth highlighting while describing a video in natural language. Owing to such a vast d
Externí odkaz:
http://arxiv.org/abs/2311.02538
Autor:
Agarwal, Rohit, Sinha, Aman, Vishwakarma, Ayan, Coubez, Xavier, Clausel, Marianne, Constant, Mathieu, Horsch, Alexander, Prasad, Dilip K.
Modeling irregularly-sampled time series (ISTS) is challenging because of missing values. Most existing methods focus on handling ISTS by converting irregularly sampled data into regularly sampled data via imputation. These models assume an underlyin
Externí odkaz:
http://arxiv.org/abs/2309.08698