Zobrazeno 1 - 10
of 377
pro vyhledávání: '"ABRAHAMYAN, LUSINE"'
Autor:
Abrahamyan, Lusine, Deligiannis, Nikos
This paper introduces an efficient patch-based computational module, coined Entropy-based Patch Encoder (EPE) module, for resource-constrained semantic segmentation. The EPE module consists of three lightweight fully-convolutional encoders, each extr
Externí odkaz:
http://arxiv.org/abs/2207.03233
Recent success in the field of single image super-resolution (SISR) is achieved by optimizing deep convolutional neural networks (CNNs) in the image space with the L1 or L2 loss. However, when trained with these loss functions, models usually fail to
Externí odkaz:
http://arxiv.org/abs/2202.00997
Autor:
Roberts, Surain B., Choi, Woo Jin, Worobetz, Lawrence, Vincent, Catherine, Flemming, Jennifer A., Cheung, Angela, Qumosani, Karim, Swain, Mark, Grbic, Dusanka, Ko, Hin Hin, Peltekian, Kevork M., Abrahamyan, Lusine, Saini, Monika, Tirona, Kattleya, Aziz, Bishoi, Lytvyak, Ellina, Invernizzi, Pietro, Ponsioen, Cyriel Y., Bruns, Tony, Cazzagon, Nora, Lindor, Keith, Dalekos, George N., Gatselis, Nikolaos K., Verhelst, Xavier, Floreani, Annarosa, Corpechot, Christophe, Mayo, Marlyn J., Levy, Cynthia, Londoño, Maria-Carlota, Battezzati, Pier M., Pares, Albert, Nevens, Frederik, van der Meer, Adriaan, Kowdley, Kris V., Trivedi, Palak J., Lleo, Ana, Thorburn, Douglas, Carbone, Marco, Selzner, Nazia, Gulamhusein, Aliya F., Janssen, Harry LA., Montano-Loza, Aldo J., Mason, Andrew L., Hirschfield, Gideon M., Hansen, Bettina E.
Publikováno v:
In JHEP Reports October 2024 6(10)
Compact convolutional neural networks (CNNs) have witnessed exceptional improvements in performance in recent years. However, they still fail to provide the same predictive power as CNNs with a large number of parameters. The diverse and even abundan
Externí odkaz:
http://arxiv.org/abs/2107.11170
Training deep neural networks on large datasets containing high-dimensional data requires a large amount of computation. A solution to this problem is data-parallel distributed training, where a model is replicated into several computational nodes th
Externí odkaz:
http://arxiv.org/abs/2103.08870
Autor:
Flores-Umanzor, Eduardo, Keshvara, Rajesh, Reza, Seleman, Asghar, Areeba, Rashidul Anwar, Mohammed, Cepas-Guillen, Pedro L., Osten, Mark, Halankar, Jaydeep, Abrahamyan, Lusine, Horlick, Eric
Publikováno v:
In Journal of Cardiovascular Computed Tomography November-December 2023 17(6):373-383
Autor:
Sahakyan, Yeva, Abrahamyan, Lusine, Ratjen, Felix, Bear, Christine, Strug, Lisa, Eckford, Paul D.W., Peel, John K., Krahn, Murray, Sander, Beate
Publikováno v:
In Journal of Cystic Fibrosis September 2023 22(5):933-940
Autor:
Shah, Ashish H., Oechslin, Erwin, Benson, Lee, Crean, Andrew M., Silversides, Candice, Bach, Yvonne, Wald, Rachel M., Roche, S. Lucy, Osten, Mark, Bruaene, Alexander Van De, Colman, Jack, Goraya, Burhan, Abrahamyan, Lusine, Hanneman, Kate, Nguyen, Elsie, Horlick, Eric
Publikováno v:
In The American Journal of Cardiology 15 August 2023 201:232-238
Publikováno v:
In Journal of Psychiatric Research August 2023 164:125-132
Publikováno v:
In Journal of Affective Disorders 15 July 2023 333:72-78