Zobrazeno 1 - 10
of 56
pro vyhledávání: '"Lazaar, Nadjib"'
The prediction of human trajectories is important for planning in autonomous systems that act in the real world, e.g. automated driving or mobile robots. Human trajectory prediction is a noisy process, and no prediction does precisely match any futur
Externí odkaz:
http://arxiv.org/abs/2407.18756
Understanding driving scenes and communicating automated vehicle decisions are key requirements for trustworthy automated driving. In this article, we introduce the Qualitative Explainable Graph (QXG), which is a unified symbolic and qualitative repr
Externí odkaz:
http://arxiv.org/abs/2403.16908
We present the Qualitative Explainable Graph (QXG): a unified symbolic and qualitative representation for scene understanding in urban mobility. QXG enables the interpretation of an automated vehicle's environment using sensor data and machine learni
Externí odkaz:
http://arxiv.org/abs/2403.09668
The future of automated driving (AD) is rooted in the development of robust, fair and explainable artificial intelligence methods. Upon request, automated vehicles must be able to explain their decisions to the driver and the car passengers, to the p
Externí odkaz:
http://arxiv.org/abs/2308.12755
Discovering relevant patterns for a particular user remains a challenging tasks in data mining. Several approaches have been proposed to learn user-specific pattern ranking functions. These approaches generalize well, but at the expense of the runnin
Externí odkaz:
http://arxiv.org/abs/2203.02696
In most optimization problems, users have a clear understanding of the function to optimize (e.g., minimize the makespan for scheduling problems). However, the constraints may be difficult to state and their modelling often requires expertise in Cons
Externí odkaz:
http://arxiv.org/abs/2111.11871
Autor:
Belaid, Mohamed-Bachir, Lazaar, Nadjib
The problem of discovering frequent itemsets including rare ones has received a great deal of attention. The mining process needs to be flexible enough to extract frequent and rare regularities at once. On the other hand, it has recently been shown t
Externí odkaz:
http://arxiv.org/abs/2109.07844
Itemset mining is one of the most studied tasks in knowledge discovery. In this paper we analyze the computational complexity of three central itemset mining problems. We prove that mining confident rules with a given item in the head is NP-hard. We
Externí odkaz:
http://arxiv.org/abs/2012.02619
Autor:
Bessiere, Christian, Carbonnel, Clement, Dries, Anton, Hebrard, Emmanuel, Katsirelos, George, Lazaar, Nadjib, Narodytska, Nina, Quimper, Claude-Guy, Stergiou, Kostas, Tsouros, Dimosthenis C., Walsh, Toby
Learning constraint networks is known to require a number of membership queries exponential in the number of variables. In this paper, we learn constraint networks by asking the user partial queries. That is, we ask the user to classify assignments t
Externí odkaz:
http://arxiv.org/abs/2003.06649
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