Zobrazeno 1 - 10
of 39 430
pro vyhledávání: '"Yousefi A."'
Operational Modal Analysis (OMA) is vital for identifying modal parameters under real-world conditions, yet existing methods often face challenges with noise sensitivity and stability. This work introduces NExT-LF, a novel method that combines the we
Externí odkaz:
http://arxiv.org/abs/2412.09418
Nowadays, inter-vehicle networks are a viable communication scenario that greatly contributes to daily work, and its issues are gaining more and more attention every day. These days, space networks are growing and developing. There are numerous new u
Externí odkaz:
http://arxiv.org/abs/2411.19002
Autor:
Burlutskiy, Nikolay, Kekic, Marija, de la Torre, Jordi, Plewa, Philipp, Boroumand, Mehdi, Jurkowska, Julia, Venovski, Borjan, Biagi, Maria Chiara, Hagos, Yeman Brhane, Malinowska-Traczyk, Roksana, Wang, Yibo, Zalewski, Jacek, Sawczuk, Paula, Pintarić, Karlo, Yousefi, Fariba, Hultin, Leif
Detecting and quantifying bone changes in micro-CT scans of rodents is a common task in preclinical drug development studies. However, this task is manual, time-consuming and subject to inter- and intra-observer variability. In 2024, Anonymous Compan
Externí odkaz:
http://arxiv.org/abs/2411.17260
Autor:
Yousefi, Midia, Qian, Yao, Chen, Junkun, Wang, Gang, Liu, Yanqing, Wang, Dongmei, Wang, Xiaofei, Xue, Jian
End-to-end speech translation (ST), which translates source language speech directly into target language text, has garnered significant attention in recent years. Many ST applications require strict length control to ensure that the translation dura
Externí odkaz:
http://arxiv.org/abs/2411.07387
In drug discovery, accurate lung tumor segmentation is an important step for assessing tumor size and its progression using \textit{in-vivo} imaging such as MRI. While deep learning models have been developed to automate this process, the focus has p
Externí odkaz:
http://arxiv.org/abs/2411.00922
Autor:
Moayeri, Mazda, Balachandran, Vidhisha, Chandrasekaran, Varun, Yousefi, Safoora, Fel, Thomas, Feizi, Soheil, Nushi, Besmira, Joshi, Neel, Vineet, Vibhav
With models getting stronger, evaluations have grown more complex, testing multiple skills in one benchmark and even in the same instance at once. However, skill-wise performance is obscured when inspecting aggregate accuracy, under-utilizing the ric
Externí odkaz:
http://arxiv.org/abs/2410.13826
Autor:
Liu, Che, Wan, Zhongwei, Wang, Haozhe, Chen, Yinda, Qaiser, Talha, Jin, Chen, Yousefi, Fariba, Burlutskiy, Nikolay, Arcucci, Rossella
Medical Vision-Language Pre-training (MedVLP) has made significant progress in enabling zero-shot tasks for medical image understanding. However, training MedVLP models typically requires large-scale datasets with paired, high-quality image-text data
Externí odkaz:
http://arxiv.org/abs/2410.13523
The ability to follow instructions is crucial for numerous real-world applications of language models. In pursuit of deeper insights and more powerful capabilities, we derive instruction-specific vector representations from language models and use th
Externí odkaz:
http://arxiv.org/abs/2410.12877
Autor:
Kowsher, Md, Sobuj, Md. Shohanur Islam, Prottasha, Nusrat Jahan, Alanis, E. Alejandro, Garibay, Ozlem Ozmen, Yousefi, Niloofar
Time series forecasting remains a challenging task, particularly in the context of complex multiscale temporal patterns. This study presents LLM-Mixer, a framework that improves forecasting accuracy through the combination of multiscale time-series d
Externí odkaz:
http://arxiv.org/abs/2410.11674
We propose RoCoFT, a parameter-efficient fine-tuning method for large-scale language models (LMs) based on updating only a few rows and columns of the weight matrices in transformers. Through extensive experiments with medium-size LMs like BERT and R
Externí odkaz:
http://arxiv.org/abs/2410.10075