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pro vyhledávání: '"Bendou, A."'
Autor:
Kmainasi, Mohamed Bayan, Khan, Rakif, Shahroor, Ali Ezzat, Bendou, Boushra, Hasanain, Maram, Alam, Firoj
Large language models (LLMs) have shown remarkable abilities in different fields, including standard Natural Language Processing (NLP) tasks. To elicit knowledge from LLMs, prompts play a key role, consisting of natural language instructions. Most op
Externí odkaz:
http://arxiv.org/abs/2409.07054
Autor:
Bendou, Yassir, Lioi, Giulia, Pasdeloup, Bastien, Mauch, Lukas, Hacene, Ghouthi Boukli, Cardinaux, Fabien, Gripon, Vincent
We consider the problem of zero-shot one-class visual classification, extending traditional one-class classification to scenarios where only the label of the target class is available. This method aims to discriminate between positive and negative qu
Externí odkaz:
http://arxiv.org/abs/2404.00675
Autor:
Lafargue, Raphael, Bendou, Yassir, Pasdeloup, Bastien, Diguet, Jean-Philippe, Reid, Ian, Gripon, Vincent, Valmadre, Jack
When training data is scarce, it is common to make use of a feature extractor that has been pre-trained on a large base dataset, either by fine-tuning its parameters on the ``target'' dataset or by directly adopting its representation as features for
Externí odkaz:
http://arxiv.org/abs/2401.15834
Autor:
Bendou, Yassir, Gripon, Vincent, Pasdeloup, Bastien, Lioi, Giulia, Mauch, Lukas, Cardinaux, Fabien, Hacene, Ghouthi Boukli
In the realm of few-shot learning, foundation models like CLIP have proven effective but exhibit limitations in cross-domain robustness especially in few-shot settings. Recent works add text as an extra modality to enhance the performance of these mo
Externí odkaz:
http://arxiv.org/abs/2311.14544
The field of visual few-shot classification aims at transferring the state-of-the-art performance of deep learning visual systems onto tasks where only a very limited number of training samples are available. The main solution consists in training a
Externí odkaz:
http://arxiv.org/abs/2301.06372
Autor:
Bendou, Yassir, Gripon, Vincent, Pasdeloup, Bastien, Mauch, Lukas, Uhlich, Stefan, Cardinaux, Fabien, Hacene, Ghouthi Boukli, Garcia, Javier Alonso
The estimation of the generalization error of classifiers often relies on a validation set. Such a set is hardly available in few-shot learning scenarios, a highly disregarded shortcoming in the field. In these scenarios, it is common to rely on feat
Externí odkaz:
http://arxiv.org/abs/2212.06461
Autor:
Bendou, Yassir, Hu, Yuqing, Lafargue, Raphael, Lioi, Giulia, Pasdeloup, Bastien, Pateux, Stéphane, Gripon, Vincent
Few-shot learning aims at leveraging knowledge learned by one or more deep learning models, in order to obtain good classification performance on new problems, where only a few labeled samples per class are available. Recent years have seen a fair nu
Externí odkaz:
http://arxiv.org/abs/2201.09699
Akademický článek
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Publikováno v:
Journal of Water and Climate Change, Vol 14, Iss 6, Pp 1741-1761 (2023)
The majority of cities in the Saharan territory of south Morocco utilize waste stabilization ponds (WSPs) for municipal wastewater treatment because of their relatively low capital, operational costs, and minimal complexity. New national effluent qua
Externí odkaz:
https://doaj.org/article/15d3f8d41a8a46f9819dc381e2105006
Publikováno v:
Frontiers in Genetics, Vol 14 (2023)
Background: Kidney renal clear cell carcinoma is the most prevalent subtype of renal cell carcinoma encompassing a heterogeneous group of malignancies. Accurate subtype identification and an understanding of the variables influencing prognosis are cr
Externí odkaz:
https://doaj.org/article/402a65f129c34ff985b5ae19394184c1