Zobrazeno 1 - 10
of 1 629
pro vyhledávání: '"ALLAM, Ahmed A."'
Autor:
Allam, Ahmed
Large Language Models (LLMs) have become pivotal in advancing natural language processing, yet their potential to perpetuate biases poses significant concerns. This paper introduces a new framework employing Direct Preference Optimization (DPO) to mi
Externí odkaz:
http://arxiv.org/abs/2407.13928
Autor:
Trottet, Cécile, Schürch, Manuel, Allam, Ahmed, Barua, Imon, Petelytska, Liubov, Launay, David, Airò, Paolo, Bečvář, Radim, Denton, Christopher, Radic, Mislav, Distler, Oliver, Hoffmann-Vold, Anna-Maria, Krauthammer, Michael, collaborators, the EUSTAR
We propose a deep generative approach using latent temporal processes for modeling and holistically analyzing complex disease trajectories, with a particular focus on Systemic Sclerosis (SSc). We aim to learn temporal latent representations of the un
Externí odkaz:
http://arxiv.org/abs/2407.11427
Autor:
Allam, Ahmed, Shalan, Mohamed
Large Language Models (LLMs) have demonstrated potential in assisting with Register Transfer Level (RTL) design tasks. Nevertheless, there remains to be a significant gap in benchmarks that accurately reflect the complexity of real-world RTL projects
Externí odkaz:
http://arxiv.org/abs/2405.17378
Autor:
Zheng, Xiaochen, Schürch, Manuel, Chen, Xingyu, Komninou, Maria Angeliki, Schüpbach, Reto, Allam, Ahmed, Bartussek, Jan, Krauthammer, Michael
The identification of phenotypes within complex diseases or syndromes is a fundamental component of precision medicine, which aims to adapt healthcare to individual patient characteristics. Postoperative delirium (POD) is a complex neuropsychiatric c
Externí odkaz:
http://arxiv.org/abs/2405.03327
Autor:
Trottet, Cécile, Schürch, Manuel, Allam, Ahmed, Barua, Imon, Petelytska, Liubov, Distler, Oliver, Hoffmann-Vold, Anna-Maria, Krauthammer, Michael, collaborators, the EUSTAR
In this paper, we propose a deep generative time series approach using latent temporal processes for modeling and holistically analyzing complex disease trajectories. We aim to find meaningful temporal latent representations of an underlying generati
Externí odkaz:
http://arxiv.org/abs/2311.08149
Autor:
Chen, Xingyu, Zheng, Xiaochen, Mollaysa, Amina, Schürch, Manuel, Allam, Ahmed, Krauthammer, Michael
Irregular multivariate time series data is characterized by varying time intervals between consecutive observations of measured variables/signals (i.e., features) and varying sampling rates (i.e., recordings/measurement) across these features. Modeli
Externí odkaz:
http://arxiv.org/abs/2311.07744
Human genetic diseases often arise from point mutations, emphasizing the critical need for precise genome editing techniques. Among these, base editing stands out as it allows targeted alterations at the single nucleotide level. However, its clinical
Externí odkaz:
http://arxiv.org/abs/2311.07636
Autor:
Schürch, Manuel, Li, Xiang, Allam, Ahmed, Rathmes, Giulia, Mollaysa, Amina, Cavelti-Weder, Claudia, Krauthammer, Michael
Publikováno v:
Machine Learning for Health (ML4H) 2023
We propose a novel framework that combines deep generative time series models with decision theory for generating personalized treatment strategies. It leverages historical patient trajectory data to jointly learn the generation of realistic personal
Externí odkaz:
http://arxiv.org/abs/2309.16521
Autor:
Mahgoub Samar M., Alwaili Maha A., Rudayni Hassan A., Almalki Manal A., Allam Ahmed A., Abdel-Reheim Mustafa Ahmed, Mohammed Osama A., Mohamed Mahmoud A.
Publikováno v:
Green Processing and Synthesis, Vol 13, Iss 1, Pp 1374-88 (2024)
Externí odkaz:
https://doaj.org/article/07503b6a1ed04c69998965261c28ff6d
Autor:
Zheng, Xiaochen, Chen, Xingyu, Schürch, Manuel, Mollaysa, Amina, Allam, Ahmed, Krauthammer, Michael
Contrastive learning methods have shown an impressive ability to learn meaningful representations for image or time series classification. However, these methods are less effective for time series forecasting, as optimization of instance discriminati
Externí odkaz:
http://arxiv.org/abs/2303.18205