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pro vyhledávání: '"Belaid, Mohamed Karim"'
Assessing the importance of individual training samples is a key challenge in machine learning. Traditional approaches retrain models with and without specific samples, which is computationally expensive and ignores dependencies between data points.
Externí odkaz:
http://arxiv.org/abs/2412.04158
Pairwise difference learning (PDL) has recently been introduced as a new meta-learning technique for regression. Instead of learning a mapping from instances to outcomes in the standard way, the key idea is to learn a function that takes two instance
Externí odkaz:
http://arxiv.org/abs/2406.20031
With the rapid growth of data availability and usage, quantifying the added value of each training data point has become a crucial process in the field of artificial intelligence. The Shapley values have been recognized as an effective method for dat
Externí odkaz:
http://arxiv.org/abs/2304.01224
In recent years, Explainable AI (xAI) attracted a lot of attention as various countries turned explanations into a legal right. xAI allows for improving models beyond the accuracy metric by, e.g., debugging the learned pattern and demystifying the AI
Externí odkaz:
http://arxiv.org/abs/2207.14160
Autor:
Belaid, Mohamed Karim
From object segmentation to word vector representations, Scene Graph Generation (SGG) became a complex task built upon numerous research results. In this paper, we focus on the last module of this model: the fusion function. The role of this latter i
Externí odkaz:
http://arxiv.org/abs/2011.04779
Autor:
Belaid, Mohamed Karim
Publikováno v:
Data Science Seminar, 2019, Uni Passau
Recent models for image processing are using the Convolutional neural network (CNN) which requires a pixel per pixel analysis of the input image. This method works well. However, it is time-consuming if we have large images. To increase the performan
Externí odkaz:
http://arxiv.org/abs/1912.03467
Publikováno v:
Automotive & Engine Technology; Dec2022, Vol. 7 Issue 3/4, p229-244, 16p
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