Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Maucher, Johannes"'
Autor:
Eisemann, Leon, Maucher, Johannes
Publikováno v:
2024 IEEE Intelligent Vehicles Symposium (IV)
High-definition road maps play a crucial role in the functionality and verification of highly automated driving functions. These contain precise information about the road network, geometry, condition, as well as traffic signs. Despite their importan
Externí odkaz:
http://arxiv.org/abs/2407.18703
Despite tremendous progress, machine learning and deep learning still suffer from incomprehensible predictions. Incomprehensibility, however, is not an option for the use of (deep) reinforcement learning in the real world, as unpredictable actions ca
Externí odkaz:
http://arxiv.org/abs/2407.14714
Knowing the printer model used to print a given document may provide a crucial lead towards identifying counterfeits or conversely verifying the validity of a real document. Inkjet printers produce probabilistic droplet patterns that appear to be dis
Externí odkaz:
http://arxiv.org/abs/2407.09539
We propose a novel architecture design for video prediction in order to utilize procedural domain knowledge directly as part of the computational graph of data-driven models. On the basis of new challenging scenarios we show that state-of-the-art vid
Externí odkaz:
http://arxiv.org/abs/2407.09537
Publikováno v:
2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), Paris, France, 2023, pp. 1076-1084
We propose a general way to integrate procedural knowledge of a domain into deep learning models. We apply it to the case of video prediction, building on top of object-centric deep models and show that this leads to a better performance than using d
Externí odkaz:
http://arxiv.org/abs/2406.18220
Autor:
Eisemann, Leon, Maucher, Johannes
Publikováno v:
2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)
High-resolution road representations are a key factor for the success of (highly) automated driving functions. These representations, for example, high-definition (HD) maps, contain accurate information on a multitude of factors, among others: road g
Externí odkaz:
http://arxiv.org/abs/2405.07544
Understanding the interactions of agents trained with deep reinforcement learning is crucial for deploying agents in games or the real world. In the former, unreasonable actions confuse players. In the latter, that effect is even more significant, as
Externí odkaz:
http://arxiv.org/abs/2309.03651
Recently, leveraging pre-trained Transformer based language models in down stream, task specific models has advanced state of the art results in natural language understanding tasks. However, only a little research has explored the suitability of thi
Externí odkaz:
http://arxiv.org/abs/2012.02462
One obstacle that so far prevents the introduction of machine learning models primarily in critical areas is the lack of explainability. In this work, a practicable approach of gaining explainability of deep artificial neural networks (NN) using an i
Externí odkaz:
http://arxiv.org/abs/1904.05394
Publikováno v:
Computer Speech & Language Volume 62, July 2020, 101056 Computer Speech & Language Volume 62, July 2020, 101056
Deep learning techniques have recently shown to be successful in many natural language processing tasks forming state-of-the-art systems. They require, however, a large amount of annotated data which is often missing. This paper explores the use of d
Externí odkaz:
http://arxiv.org/abs/1807.05195