Zobrazeno 1 - 10
of 49 331
pro vyhledávání: '"Sani, A. A."'
Autor:
Sani, Samin Mahdizadeh, Sadeghi, Pouya, Vu, Thuy-Trang, Yaghoobzadeh, Yadollah, Haffari, Gholamreza
Large language models (LLMs) have made great progress in classification and text generation tasks. However, they are mainly trained on English data and often struggle with low-resource languages. In this study, we explore adding a new language, i.e.,
Externí odkaz:
http://arxiv.org/abs/2412.13375
In this work, we present a general Milstein-type scheme for McKean-Vlasov stochastic differential equations (SDEs) driven by Brownian motion and Poisson random measure and the associated system of interacting particles where drift, diffusion and jump
Externí odkaz:
http://arxiv.org/abs/2411.11759
Autor:
Sani, Depanshu, Anand, Saket
The growing demand for robust scene understanding in mobile robotics and autonomous driving has highlighted the importance of integrating multiple sensing modalities. By combining data from diverse sensors like cameras and LIDARs, fusion techniques c
Externí odkaz:
http://arxiv.org/abs/2411.03702
Autor:
Sani, Lorenzo, Iacob, Alex, Cao, Zeyu, Lee, Royson, Marino, Bill, Gao, Yan, Cai, Dongqi, Li, Zexi, Zhao, Wanru, Qiu, Xinchi, Lane, Nicholas D.
Scaling large language models (LLMs) demands extensive data and computing resources, which are traditionally constrained to data centers by the high-bandwidth requirements of distributed training. Low-bandwidth methods like federated learning (FL) co
Externí odkaz:
http://arxiv.org/abs/2411.02908
In this paper, we address the challenge of certifying the performance of a machine learning model on an unseen target network, using measurements from an available source network. We focus on a scenario where heterogeneous datasets are distributed ac
Externí odkaz:
http://arxiv.org/abs/2410.20250
Autor:
Khoshtab, Paria, Namazifard, Danial, Masoudi, Mostafa, Akhgary, Ali, Sani, Samin Mahdizadeh, Yaghoobzadeh, Yadollah
This study addresses the gap in the literature concerning the comparative performance of LLMs in interpreting different types of figurative language across multiple languages. By evaluating LLMs using two multilingual datasets on simile and idiom int
Externí odkaz:
http://arxiv.org/abs/2410.16461
Chest X-rays (X-ray images) have been proven to be effective for the diagnosis of chest diseases, including Pneumonia, Lung Opacity, and COVID-19. However, relying on traditional medical methods for diagnosis from X-ray images is prone to delays and
Externí odkaz:
http://arxiv.org/abs/2410.15437
Large language models (LLMs) hold promise for generating plans for complex tasks, but their effectiveness is limited by sequential execution, lack of control flow models, and difficulties in skill retrieval. Addressing these issues is crucial for imp
Externí odkaz:
http://arxiv.org/abs/2410.12870
Autor:
Iacob, Alex, Sani, Lorenzo, Kurmanji, Meghdad, Shen, William F., Qiu, Xinchi, Cai, Dongqi, Gao, Yan, Lane, Nicholas D.
Language model pre-training benefits from diverse data to enhance performance across domains and languages. However, training on such heterogeneous corpora requires extensive and costly efforts. Since these data sources vary lexically, syntactically,
Externí odkaz:
http://arxiv.org/abs/2410.05021
The current cybersecurity landscape is increasingly complex, with traditional Static Application Security Testing (SAST) tools struggling to capture complex and emerging vulnerabilities due to their reliance on rule-based matching. Meanwhile, Large L
Externí odkaz:
http://arxiv.org/abs/2409.15735