Zobrazeno 1 - 10
of 4 061
pro vyhledávání: '"Yazidi, A."'
Electroencephalography (EEG) data provides a non-invasive method for researchers and clinicians to observe brain activity in real time. The integration of deep learning techniques with EEG data has significantly improved the ability to identify meani
Externí odkaz:
http://arxiv.org/abs/2409.17815
The expanding research on manifold-based self-supervised learning (SSL) builds on the manifold hypothesis, which suggests that the inherent complexity of high-dimensional data can be unraveled through lower-dimensional manifold embeddings. Capitalizi
Externí odkaz:
http://arxiv.org/abs/2405.13848
Vision Transformers implement multi-head self-attention via stacking multiple attention blocks. The query, key, and value are often intertwined and generated within those blocks via a single, shared linear transformation. This paper explores the conc
Externí odkaz:
http://arxiv.org/abs/2402.00534
Autor:
Belaid, Mohamed-Bachir, Sharma, Jivitesh, Jiao, Lei, Granmo, Ole-Christoffer, Andersen, Per-Arne, Yazidi, Anis
Tsetlin Machines (TMs) have garnered increasing interest for their ability to learn concepts via propositional formulas and their proven efficiency across various application domains. Despite this, the convergence proof for the TMs, particularly for
Externí odkaz:
http://arxiv.org/abs/2310.02005
Autor:
Barghady Najoua, Assou Soumia Ait, Er-Rajy Mohammed, Boujdi Khalid, Arzine Aziz, Rhazi Yassine, Tüzün Burak, Nakkabi Asmae, Chalkha Mohammed, El Hassouni Mohammed, Kabra Atul, Alanazi Mohammed M., Baouid Abdesselam, El Yazidi Mohamed
Publikováno v:
Open Chemistry, Vol 22, Iss 1, Pp 279-83 (2024)
Externí odkaz:
https://doaj.org/article/fb004eb9056e4467b720dd48bf8fffec
Autor:
Mats Tveter, Thomas Tveitstøl, Christoffer Hatlestad-Hall, Ana S. Pérez T., Erik Taubøll, Anis Yazidi, Hugo L. Hammer, Ira R. J. Hebold Haraldsen
Publikováno v:
Brain Informatics, Vol 11, Iss 1, Pp 1-12 (2024)
Abstract Deep Learning (DL) has the potential to enhance patient outcomes in healthcare by implementing proficient systems for disease detection and diagnosis. However, the complexity and lack of interpretability impede their widespread adoption in c
Externí odkaz:
https://doaj.org/article/ac8e752216de448587ab16a344df19ac
Autor:
Julien Van Gils, Slim Karkar, Aurélien Barre, Seyta Ley-Ngardigal, Sophie Nothof, Stéphane Claverol, Caroline Tokarski, Jean-Philippe Trani, Raphael Chevalier, Natacha Broucqsault, Claire El Yazidi, Didier Lacombe, Patricia Fergelot, Frédérique Magdinier
Publikováno v:
Communications Biology, Vol 7, Iss 1, Pp 1-15 (2024)
Abstract Rubinstein-Taybi syndrome (RTS) is a rare and severe genetic developmental disorder characterized by multiple congenital anomalies and intellectual disability. CREBBP and EP300, the two genes known to cause RTS encode transcriptional coactiv
Externí odkaz:
https://doaj.org/article/5ca15b01bb7840f3b1cb9bd06d1fa239
Publikováno v:
ICLR 2024
The manifold hypothesis posits that high-dimensional data often lies on a lower-dimensional manifold and that utilizing this manifold as the target space yields more efficient representations. While numerous traditional manifold-based techniques exis
Externí odkaz:
http://arxiv.org/abs/2305.10267
Autor:
Jha, Debesh, Rauniyar, Ashish, Srivastava, Abhiskek, Hagos, Desta Haileselassie, Tomar, Nikhil Kumar, Sharma, Vanshali, Keles, Elif, Zhang, Zheyuan, Demir, Ugur, Topcu, Ahmet, Yazidi, Anis, Håakegård, Jan Erik, Bagci, Ulas
Artificial intelligence (AI) methods hold immense potential to revolutionize numerous medical care by enhancing the experience of medical experts and patients. AI-based computer-assisted diagnosis and treatment tools can democratize healthcare by mat
Externí odkaz:
http://arxiv.org/abs/2304.11530
State representation learning aims to capture latent factors of an environment. Contrastive methods have performed better than generative models in previous state representation learning research. Although some researchers realize the connections bet
Externí odkaz:
http://arxiv.org/abs/2303.07437