Zobrazeno 1 - 10
of 63
pro vyhledávání: '"Hasan, Sadid"'
Autor:
Mekala, Anmol, Dorna, Vineeth, Dubey, Shreya, Lalwani, Abhishek, Koleczek, David, Rungta, Mukund, Hasan, Sadid, Lobo, Elita
Machine unlearning aims to efficiently eliminate the influence of specific training data, known as the forget set, from the model. However, existing unlearning methods for Large Language Models (LLMs) face a critical challenge: they rely solely on ne
Externí odkaz:
http://arxiv.org/abs/2409.13474
Autor:
Chen, Zheng, Hasan, Sadid A., Liu, Joey, Datla, Vivek, Shamsuzzaman, Md, Khan, Hafiz, Sorower, Mohammad S, Mankovich, Gabe, van Ommering, Rob, Dimitrova, Nevenka
This paper presents an ontology-driven treatment article retrieval system developed and experimented using the data and ground truths provided by the TREC 2017 precision medicine track. The key aspects of our system include: meaningful integration of
Externí odkaz:
http://arxiv.org/abs/2002.05653
Autor:
Oliveira, Lucas Emanuel Silva e, Peters, Ana Carolina, da Silva, Adalniza Moura Pucca, Gebeluca, Caroline P., Gumiel, Yohan Bonescki, Cintho, Lilian Mie Mukai, Carvalho, Deborah Ribeiro, Hasan, Sadid A., Moro, Claudia Maria Cabral
The high volume of research focusing on extracting patient's information from electronic health records (EHR) has led to an increase in the demand for annotated corpora, which are a very valuable resource for both the development and evaluation of na
Externí odkaz:
http://arxiv.org/abs/2001.10071
Autor:
Ghaeini, Reza, Hasan, Sadid A., Datla, Vivek, Liu, Joey, Lee, Kathy, Qadir, Ashequl, Ling, Yuan, Prakash, Aaditya, Fern, Xiaoli Z., Farri, Oladimeji
Publikováno v:
NAACL 2018
We present a novel deep learning architecture to address the natural language inference (NLI) task. Existing approaches mostly rely on simple reading mechanisms for independent encoding of the premise and hypothesis. Instead, we propose a novel depen
Externí odkaz:
http://arxiv.org/abs/1802.05577
Autor:
Prakash, Aaditya, Zhao, Siyuan, Hasan, Sadid A., Datla, Vivek, Lee, Kathy, Qadir, Ashequl, Liu, Joey, Farri, Oladimeji
Diagnosis of a clinical condition is a challenging task, which often requires significant medical investigation. Previous work related to diagnostic inferencing problems mostly consider multivariate observational data (e.g. physiological signals, lab
Externí odkaz:
http://arxiv.org/abs/1612.01848
Autor:
Prakash, Aaditya, Hasan, Sadid A., Lee, Kathy, Datla, Vivek, Qadir, Ashequl, Liu, Joey, Farri, Oladimeji
In this paper, we propose a novel neural approach for paraphrase generation. Conventional para- phrase generation methods either leverage hand-written rules and thesauri-based alignments, or use statistical machine learning principles. To the best of
Externí odkaz:
http://arxiv.org/abs/1610.03098
Publikováno v:
Journal Of Artificial Intelligence Research, Volume 35, pages 1-47, 2009
Complex questions that require inferencing and synthesizing information from multiple documents can be seen as a kind of topic-oriented, informative multi-document summarization where the goal is to produce a single text as a compressed version of a
Externí odkaz:
http://arxiv.org/abs/1401.3479
Autor:
Banerjee, Imon, Ling, Yuan, Chen, Matthew C., Hasan, Sadid A., Langlotz, Curtis P., Moradzadeh, Nathaniel, Chapman, Brian, Amrhein, Timothy, Mong, David, Rubin, Daniel L., Farri, Oladimeji, Lungren, Matthew P.
Publikováno v:
In Artificial Intelligence In Medicine June 2019 97:79-88
Autor:
Ionescu, Bogdan, Müller, Henning, Peteri, Renaud, Dang Nguyen, Duc Tien, Piras, Luca, Riegler, Michael, Tran, Minh Triet, Lux, Mathias, Gurrin, Cathal, Cid, Yashin Dicente, Liauchuk, Vitali, Kovalev, Vassili, Ben Abacha, Asma, Hasan, Sadid A., Datla, Vivek, Liu, Joey, Demner-Fushman, Dina, Pelka, Obioma, Friedrich, C., Chamberlain, Jon, Clark, Adrian, de Herrera, Alba Garcia Seco, Garcia, Narciso, Kavallieratou, Ergina, del Blanco, Carlos Roberto, Rodriguez, Carlos Cuevas, Vasillopoulos, Nikos, Karampidis, Konstantinos
Externí odkaz:
http://hdl.handle.net/1956/23300
Publikováno v:
In Information Processing and Management May 2015 51(3):252-272