Zobrazeno 1 - 10
of 3 142
pro vyhledávání: '"P. Obeïd"'
Autor:
Jaghouar, Sami, Ong, Jack Min, Basra, Manveer, Obeid, Fares, Straube, Jannik, Keiblinger, Michael, Bakouch, Elie, Atkins, Lucas, Panahi, Maziyar, Goddard, Charles, Ryabinin, Max, Hagemann, Johannes
In this report, we introduce INTELLECT-1, the first 10 billion parameter language model collaboratively trained across the globe, demonstrating that large-scale model training is no longer confined to large corporations but can be achieved through a
Externí odkaz:
http://arxiv.org/abs/2412.01152
Autor:
Obeid, Ahmad, Boumaraf, Said, Sohail, Anabia, Hassan, Taimur, Javed, Sajid, Dias, Jorge, Bennamoun, Mohammed, Werghi, Naoufel
Recent years witnessed remarkable progress in computational histopathology, largely fueled by deep learning. This brought the clinical adoption of deep learning-based tools within reach, promising significant benefits to healthcare, offering a valuab
Externí odkaz:
http://arxiv.org/abs/2410.19820
Convolutional Neural Networks (CNNs) excel in many visual tasks, but they tend to be sensitive to slight input perturbations that are imperceptible to the human eye, often resulting in task failures. Recent studies indicate that training CNNs with re
Externí odkaz:
http://arxiv.org/abs/2410.03952
We introduce GoldFinch, a hybrid Linear Attention/Transformer sequence model that uses a new technique to efficiently generate a highly compressed and reusable KV-Cache in linear time and space with respect to sequence length. GoldFinch stacks our ne
Externí odkaz:
http://arxiv.org/abs/2407.12077
The widespread absence of diacritical marks in Arabic text poses a significant challenge for Arabic natural language processing (NLP). This paper explores instances of naturally occurring diacritics, referred to as "diacritics in the wild," to unveil
Externí odkaz:
http://arxiv.org/abs/2406.05760
Autor:
Peng, Bo, Goldstein, Daniel, Anthony, Quentin, Albalak, Alon, Alcaide, Eric, Biderman, Stella, Cheah, Eugene, Du, Xingjian, Ferdinan, Teddy, Hou, Haowen, Kazienko, Przemysław, GV, Kranthi Kiran, Kocoń, Jan, Koptyra, Bartłomiej, Krishna, Satyapriya, McClelland Jr., Ronald, Lin, Jiaju, Muennighoff, Niklas, Obeid, Fares, Saito, Atsushi, Song, Guangyu, Tu, Haoqin, Wirawan, Cahya, Woźniak, Stanisław, Zhang, Ruichong, Zhao, Bingchen, Zhao, Qihang, Zhou, Peng, Zhu, Jian, Zhu, Rui-Jie
We present Eagle (RWKV-5) and Finch (RWKV-6), sequence models improving upon the RWKV (RWKV-4) architecture. Our architectural design advancements include multi-headed matrix-valued states and a dynamic recurrence mechanism that improve expressivity
Externí odkaz:
http://arxiv.org/abs/2404.05892
Autor:
Farah Kobaisi, Eric Sulpice, Ali Nasrallah, Patricia Obeïd, Hussein Fayyad-Kazan, Walid Rachidi, Xavier Gidrol
Publikováno v:
Cell Death and Disease, Vol 15, Iss 11, Pp 1-9 (2024)
Abstract Xeroderma Pigmentosum C is a dermal hereditary disease caused by a mutation in the DNA damage recognition protein XPC that belongs to the Nucleotide excision repair pathway. XPC patients display heightened sensitivity to light and an inabili
Externí odkaz:
https://doaj.org/article/4d5c93327c1242b5ab9435c58376def8
We present Camelira, a web-based Arabic multi-dialect morphological disambiguation tool that covers four major variants of Arabic: Modern Standard Arabic, Egyptian, Gulf, and Levantine. Camelira offers a user-friendly web interface that allows resear
Externí odkaz:
http://arxiv.org/abs/2211.16807
Autor:
Alhafni, Bashar, Habash, Nizar, Bouamor, Houda, Obeid, Ossama, Alrowili, Sultan, Alzeer, Daliyah, Alshanqiti, Khawlah M., ElBakry, Ahmed, ElNokrashy, Muhammad, Gabr, Mohamed, Issam, Abderrahmane, Qaddoumi, Abdelrahim, Vijay-Shanker, K., Zyate, Mahmoud
In this paper, we present the results and findings of the Shared Task on Gender Rewriting, which was organized as part of the Seventh Arabic Natural Language Processing Workshop. The task of gender rewriting refers to generating alternatives of a giv
Externí odkaz:
http://arxiv.org/abs/2210.12410
We introduce the User-Aware Arabic Gender Rewriter, a user-centric web-based system for Arabic gender rewriting in contexts involving two users. The system takes either Arabic or English sentences as input, and provides users with the ability to spec
Externí odkaz:
http://arxiv.org/abs/2210.07538