Zobrazeno 1 - 10
of 40 763
pro vyhledávání: '"A. Nourbakhsh"'
Autor:
Zmigrod, Ran, Shetty, Pranav, Sibue, Mathieu, Ma, Zhiqiang, Nourbakhsh, Armineh, Liu, Xiaomo, Veloso, Manuela
The rise of large language models (LLMs) for visually rich document understanding (VRDU) has kindled a need for prompt-response, document-based datasets. As annotating new datasets from scratch is labor-intensive, the existing literature has generate
Externí odkaz:
http://arxiv.org/abs/2410.15484
Autor:
Chen, Zhiyu Zoey, Ma, Jing, Zhang, Xinlu, Hao, Nan, Yan, An, Nourbakhsh, Armineh, Yang, Xianjun, McAuley, Julian, Petzold, Linda, Wang, William Yang
In the fast-evolving domain of artificial intelligence, large language models (LLMs) such as GPT-3 and GPT-4 are revolutionizing the landscapes of finance, healthcare, and law: domains characterized by their reliance on professional expertise, challe
Externí odkaz:
http://arxiv.org/abs/2405.01769
Autor:
Zmigrod, Ran, Wang, Dongsheng, Sibue, Mathieu, Pei, Yulong, Babkin, Petr, Brugere, Ivan, Liu, Xiaomo, Navarro, Nacho, Papadimitriou, Antony, Watson, William, Ma, Zhiqiang, Nourbakhsh, Armineh, Shah, Sameena
The field of visually rich document understanding (VRDU) aims to solve a multitude of well-researched NLP tasks in a multi-modal domain. Several datasets exist for research on specific tasks of VRDU such as document classification (DC), key entity ex
Externí odkaz:
http://arxiv.org/abs/2404.04003
Akademický článek
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Research into COVID-19 has been rapidly evolving since the onset of the pandemic. This occasionally results in contradictory recommendations by credible sources of scientific opinion, public health authorities, and medical professionals. In this stud
Externí odkaz:
http://arxiv.org/abs/2403.09260
Visually Rich Form Understanding (VRFU) poses a complex research problem due to the documents' highly structured nature and yet highly variable style and content. Current annotation schemes decompose form understanding and omit key hierarchical struc
Externí odkaz:
http://arxiv.org/abs/2402.05282
Advances in Visually Rich Document Understanding (VrDU) have enabled information extraction and question answering over documents with complex layouts. Two tropes of architectures have emerged -- transformer-based models inspired by LLMs, and Graph N
Externí odkaz:
http://arxiv.org/abs/2401.02823
Autor:
Khanmohammadi, Reza, Kaur, Simerjot, Smiley, Charese H., Alhanai, Tuka, Brugere, Ivan, Nourbakhsh, Armineh, Ghassemi, Mohammad M.
This paper investigates the relationship between scientific innovation in biomedical sciences and its impact on industrial activities, focusing on how the historical impact and content of scientific papers influenced future funding and innovation gra
Externí odkaz:
http://arxiv.org/abs/2401.00942
Autor:
Wang, Dongsheng, Raman, Natraj, Sibue, Mathieu, Ma, Zhiqiang, Babkin, Petr, Kaur, Simerjot, Pei, Yulong, Nourbakhsh, Armineh, Liu, Xiaomo
Enterprise documents such as forms, invoices, receipts, reports, contracts, and other similar records, often carry rich semantics at the intersection of textual and spatial modalities. The visual cues offered by their complex layouts play a crucial r
Externí odkaz:
http://arxiv.org/abs/2401.00908
Autor:
Amirali Azimi, Fatemeh Sadat Tabatabaei, Kasra Kolahdouzan, Hamideh Rashidian, Forouzan Nourbakhsh, Maryam Abedini Parizi, Nima Mousavi Darzikolaee, Reyhaneh Bayani, Samaneh Salarvand, Azadeh Sharifian, Farzaneh Bagheri, Saeed Rezaei, Naeim Nabian, Reza Nazari, Negin Mohammadi, Mohammad Babaei, Marzieh Lashkari, Farshid Farhan, Mahdi Aghili, Felipe Couñago, Maria Antonietta Gambacorta, Reza Ghalehtaki
Publikováno v:
Radiation Oncology, Vol 19, Iss 1, Pp 1-11 (2024)
Abstract Background/Aim Current approaches for locally advanced rectal cancer (LARC) typically recommend neoadjuvant chemoradiotherapy (nCRT) with 5-fluorouracil (5FU) or its oral analogs followed by surgery as the standard of care. However, the ques
Externí odkaz:
https://doaj.org/article/6bf7f5612e68425fbc22233e70cfe959
