ZipNN: Lossless Compression for AI Models

Autor: Hershcovitch, Moshik, Wood, Andrew, Choshen, Leshem, Girmonsky, Guy, Leibovitz, Roy, Ennmouri, Ilias, Malka, Michal, Chin, Peter, Sundararaman, Swaminathan, Harnik, Danny

With the growth of model sizes and the scale of their deployment, their sheer size burdens the infrastructure requiring more network and more storage to accommodate these. While there is a vast model compression literature deleting parts of the model

Externí odkaz: http://arxiv.org/abs/2411.05239

fMRI Neurofeedback Learning Patterns are Predictive of Personal and Clinical Traits

Autor: Leibovitz, Rotem, Osin, Jhonathan, Wolf, Lior, Gurevitch, Guy, Hendler, Talma

Publikováno v: MICCAI 2022

We obtain a personal signature of a person's learning progress in a self-neuromodulation task, guided by functional MRI (fMRI). The signature is based on predicting the activity of the Amygdala in a second neurofeedback session, given a similar fMRI-

Externí odkaz: http://arxiv.org/abs/2112.11014

Early Outcome of Multisystem Inflammatory Syndrome in Neonates Diagnosed following Prenatal Maternal COVID-19 Infection: A Three-Case Series

Autor: Maria Terciu, Ioana Luca, Emilia Panait, Eugene Leibovitz, Maria Mitrica, Bianca Popovici, Anca Ilea, Oana Gabriela Falup-Pecurariu

Publikováno v: Pediatric Reports, Vol 15, Iss 4, Pp 591-598 (2023)

Background: The aim of this case series report is to evaluate the characteristics of multisystem inflammatory syndrome (MIS) in neonates following prenatal maternal COVID-19 infection. Methods: We present a case series of three newborns (≤28 days o

Externí odkaz: https://doaj.org/article/d8eb5b2305884a409dc7fad600e2d2f1

Federated Learning for Breast Density Classification: A Real-World Implementation

Autor: Roth, Holger R., Chang, Ken, Singh, Praveer, Neumark, Nir, Li, Wenqi, Gupta, Vikash, Gupta, Sharut, Qu, Liangqiong, Ihsani, Alvin, Bizzo, Bernardo C., Wen, Yuhong, Buch, Varun, Shah, Meesam, Kitamura, Felipe, Mendonça, Matheus, Lavor, Vitor, Harouni, Ahmed, Compas, Colin, Tetreault, Jesse, Dogra, Prerna, Cheng, Yan, Erdal, Selnur, White, Richard, Hashemian, Behrooz, Schultz, Thomas, Zhang, Miao, McCarthy, Adam, Yun, B. Min, Sharaf, Elshaimaa, Hoebel, Katharina V., Patel, Jay B., Chen, Bryan, Ko, Sean, Leibovitz, Evan, Pisano, Etta D., Coombs, Laura, Xu, Daguang, Dreyer, Keith J., Dayan, Ittai, Naidu, Ram C., Flores, Mona, Rubin, Daniel, Kalpathy-Cramer, Jayashree

Publikováno v: In: Albarqouni S. et al. (eds) Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning. DART 2020, DCL 2020. Lecture Notes in Computer Science, vol 12444. Springer, Cham

Building robust deep learning-based models requires large quantities of diverse training data. In this study, we investigate the use of federated learning (FL) to build medical imaging classification models in a real-world collaborative setting. Seve

Externí odkaz: http://arxiv.org/abs/2009.01871

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