Zobrazeno 1 - 10
of 2 243
pro vyhledávání: '"Alzubaidi AN"'
Autor:
Firdoussi, Aymane El, Seddik, Mohamed El Amine, Hayou, Soufiane, Alami, Reda, Alzubaidi, Ahmed, Hacid, Hakim
Synthetic data has gained attention for training large language models, but poor-quality data can harm performance (see, e.g., Shumailov et al. (2023); Seddik et al. (2024)). A potential solution is data pruning, which retains only high-quality data
Externí odkaz:
http://arxiv.org/abs/2410.08942
Autor:
Alami, Reda, Almansoori, Ali Khalifa, Alzubaidi, Ahmed, Seddik, Mohamed El Amine, Farooq, Mugariya, Hacid, Hakim
We demonstrate that preference optimization methods can effectively enhance LLM safety. Applying various alignment techniques to the Falcon 11B model using safety datasets, we achieve a significant boost in global safety score (from $57.64\%$ to $99.
Externí odkaz:
http://arxiv.org/abs/2409.07772
Autor:
Jebur, Sabah Abdulazeez, Hussein, Khalid A., Hoomod, Haider Kadhim, Alzubaidi, Laith, Saihood, Ahmed Ali, Gu, YuanTong
Anomaly detection in videos is challenging due to the complexity, noise, and diverse nature of activities such as violence, shoplifting, and vandalism. While deep learning (DL) has shown excellent performance in this area, existing approaches have st
Externí odkaz:
http://arxiv.org/abs/2408.00792
Adversarial attacks are a potential threat to machine learning models by causing incorrect predictions through imperceptible perturbations to the input data. While these attacks have been extensively studied in unstructured data like images, applying
Externí odkaz:
http://arxiv.org/abs/2407.11463
Autor:
Alnashri, Yahya, Alzubaidi, Hasan
The stochastic reaction-diffusion model driven by a multiplicative noise is examined. We construct the gradient discretisation method (GDM), an abstract framework combining several numerical method families. The paper provides the discretisation and
Externí odkaz:
http://arxiv.org/abs/2407.07834
Deep learning has significantly advanced automatic medical diagnostics and released the occupation of human resources to reduce clinical pressure, yet the persistent challenge of data scarcity in this area hampers its further improvements and applica
Externí odkaz:
http://arxiv.org/abs/2407.06566
Recently, transfer learning and self-supervised learning have gained significant attention within the medical field due to their ability to mitigate the challenges posed by limited data availability, improve model generalisation, and reduce computati
Externí odkaz:
http://arxiv.org/abs/2407.05592
Autor:
Alzubaidi, Husniyah, Maciocia, Antony
We modify the axioms of triangulated categories to include both higher triangles and distinguished maps of higher triangles. The distinguished maps are specializations of Neeman's ``good'' maps of $2$-triangles. The axioms both simplify Neeman's axio
Externí odkaz:
http://arxiv.org/abs/2312.06843
Exact Travelling Wave Solutions using Tanh Method for two-dimensional Stochastic Allen-Cahn equation
Autor:
Alzubaidi, Hasan
Exact travelling wave solutions to the two-dimensional stochastic Allen-Cahn equation with multiplicative noise are obtained through the hyperbolic tangent (tanh) method. This technique limits the solutions to travelling wave profiles by representing
Externí odkaz:
http://arxiv.org/abs/2312.03685
The recent advancement of Blockchain technology consolidates its status as a viable alternative for various domains. However, evaluating the performance of blockchain applications can be challenging due to the underlying infrastructure's complexity a
Externí odkaz:
http://arxiv.org/abs/2309.11205