Zobrazeno 1 - 10
of 477
pro vyhledávání: '"Shafipour, A."'
Autor:
Shafipour, Rasoul, Harrison, David, Horton, Maxwell, Marker, Jeffrey, Bedayat, Houman, Mehta, Sachin, Rastegari, Mohammad, Najibi, Mahyar, Naderiparizi, Saman
Large Language Models (LLMs) have transformed natural language processing, but face significant challenges in widespread deployment due to their high runtime cost. In this paper, we introduce SeedLM, a novel post-training compression method that uses
Externí odkaz:
http://arxiv.org/abs/2410.10714
Publikováno v:
BMC Medical Education, Vol 24, Iss 1, Pp 1-8 (2024)
Abstract Background The topic of patient safety and attitudes toward patient safety among health professionals is a main priority of healthcare systems globally. This study aims to investigate the psychometric properties of APSQ-III among Iranian nur
Externí odkaz:
https://doaj.org/article/2c768a945f68499fbd0750aab2692827
Autor:
Rouhani, Bita Darvish, Zhao, Ritchie, More, Ankit, Hall, Mathew, Khodamoradi, Alireza, Deng, Summer, Choudhary, Dhruv, Cornea, Marius, Dellinger, Eric, Denolf, Kristof, Dusan, Stosic, Elango, Venmugil, Golub, Maximilian, Heinecke, Alexander, James-Roxby, Phil, Jani, Dharmesh, Kolhe, Gaurav, Langhammer, Martin, Li, Ada, Melnick, Levi, Mesmakhosroshahi, Maral, Rodriguez, Andres, Schulte, Michael, Shafipour, Rasoul, Shao, Lei, Siu, Michael, Dubey, Pradeep, Micikevicius, Paulius, Naumov, Maxim, Verrilli, Colin, Wittig, Ralph, Burger, Doug, Chung, Eric
Narrow bit-width data formats are key to reducing the computational and storage costs of modern deep learning applications. This paper evaluates Microscaling (MX) data formats that combine a per-block scaling factor with narrow floating-point and int
Externí odkaz:
http://arxiv.org/abs/2310.10537
Autor:
Sara Salar, Fatimah Eftekhari, Maryam Shafipour, Reza Ebrahimnia, Ebrahim Moghadar, Seyed Abedin Moosavi, Hassan Safari, Navisa Sadat Seyedghasemi, Ali Shahryari
Publikováno v:
Journal of Environmental Health and Sustainable Development, Vol 9, Iss 3, Pp 2378-2387 (2024)
Introduction: Treatment of municipal wastewater is essential to remove bacteria. This study is designed to evaluate the efficacy of a wastewater treatment plant (WWTP) for the removal of bacteria and using for irrigation or discharge in the Caspian S
Externí odkaz:
https://doaj.org/article/bacb5abde66b4e73b39bd60a3564cdaf
Autor:
Rouhani, Bita, Zhao, Ritchie, Elango, Venmugil, Shafipour, Rasoul, Hall, Mathew, Mesmakhosroshahi, Maral, More, Ankit, Melnick, Levi, Golub, Maximilian, Varatkar, Girish, Shao, Lei, Kolhe, Gaurav, Melts, Dimitry, Klar, Jasmine, L'Heureux, Renee, Perry, Matt, Burger, Doug, Chung, Eric, Deng, Zhaoxia, Naghshineh, Sam, Park, Jongsoo, Naumov, Maxim
This paper introduces Block Data Representations (BDR), a framework for exploring and evaluating a wide spectrum of narrow-precision formats for deep learning. It enables comparison of popular quantization standards, and through BDR, new formats base
Externí odkaz:
http://arxiv.org/abs/2302.08007
Autor:
Shafipour, Maryam1 (AUTHOR), Mohammadzadeh, Abdolmajid1 (AUTHOR), Mahmoodi, Pezhman1 (AUTHOR), Dehghanpour, Mahdi1,2 (AUTHOR), Ghaemi, Ezzat Allah3 (AUTHOR) eghaemi@yahoo.com
Publikováno v:
PLoS ONE. 10/24/2024, Vol. 19 Issue 10, p1-14. 14p.
Autor:
Amini-Salehi, Ehsan, Letafatkar, Negin, Norouzi, Naeim, Joukar, Farahnaz, Habibi, Arman, Javid, Mona, Sattari, Nazila, Khorasani, Mehrdad, Farahmand, Ali, Tavakoli, Shervin, Masoumzadeh, Behnaz, Abbaspour, Elaheh, Karimzad, Sahand, Ghadiri, Amir, Maddineni, Gautam, Khosousi, Mohammad Javad, Faraji, Niloofar, Keivanlou, Mohammad-Hossein, Mahapatro, Abinash, Gaskarei, Mohamad Amin Khajavi, Okhovat, Paria, Bahrampourian, Ali, Aleali, Maryam Sadat, Mirdamadi, Arian, Eslami, Narges, Javid, Mohamadreza, Javaheri, Naz, Pra, Shrinidhi Vilas, Bakhsi, Arash, Shafipour, Mohammad, Vakilpour, Azin, Ansar, Malek Moein, Kanagala, Sai Guatham, Hashemi, Mohamad, Ghazalgoo, Arezoo, Kheirandish, Masoumeh, Porteghali, Parham, Heidarzad, Forough, Zeinali, Taraneh, Ghanaei, Fariborz Mansour, Hassanipour, Soheil, Ulrich, Michael.T, Melson, Joshua E., Patel, Dhruvan, Nayak, Sandeep Samethadka
Publikováno v:
In Archives of Medical Research September 2024 55(6)
In contrast to image/text data whose order can be used to perform non-local feature aggregation in a straightforward way using the pooling layers, graphs lack the tensor representation and mostly the element-wise max/mean function is utilized to aggr
Externí odkaz:
http://arxiv.org/abs/2108.07028
Autor:
Shafipour, Rasoul, Mateos, Gonzalo
We develop online graph learning algorithms from streaming network data. Our goal is to track the (possibly) time-varying network topology, and effect memory and computational savings by processing the data on-the-fly as they are acquired. The setup
Externí odkaz:
http://arxiv.org/abs/2007.03653
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