Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Yaniv Romano"'
Publikováno v:
Frontiers in Neuroscience, Vol 17 (2023)
IntroductionEpilepsy is a neurological disease characterized by sudden, unprovoked seizures. The unexpected nature of epileptic seizures is a major component of the disease burden. Predicting seizure onset and alarming patients may allow timely inter
Externí odkaz:
https://doaj.org/article/4ec0fd83e14c4603b02e9476d9153788
Publikováno v:
Biometrika. 109:611-629
Summary In this article we develop a method based on model-X knockoffs to find conditional associations that are consistent across environments, while controlling the false discovery rate. The motivation for this problem is that large datasets may co
Autor:
Yaniv Romano, Harel Primack, Talya Vaknin, Idan Meirzada, Ilan Karpas, Dov Furman, Chene Tradonsky, Ruti Ben Shlomi
The ultimate goal of any sparse coding method is to accurately recover from a few noisy linear measurements, an unknown sparse vector. Unfortunately, this estimation problem is NP-hard in general, and it is therefore always approached with an approxi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5c3aa632882dcb1b92119e07d5036401
Publikováno v:
Journal of the American Statistical Association. 115:1861-1872
This paper introduces a machine for sampling approximate model-X knockoffs for arbitrary and unspecified data distributions using deep generative models. The main idea is to iteratively refine a knockoff sampling mechanism until a criterion measuring
Publikováno v:
Journal of Mathematical Imaging and Vision. 62:313-327
Despite their impressive performance, deep convolutional neural networks (CNN) have been shown to be sensitive to small adversarial perturbations. These nuisances, which one can barely notice, are powerful enough to fool sophisticated and well perfor
Publikováno v:
Harvard Data Science Review.
Publikováno v:
IEEE Signal Processing Magazine. 35:72-89
Modeling data is the way we-scientists-believe that information should be explained and handled. Indeed, models play a central role in practically every task in signal and image processing and machine learning. Sparse representation theory (we shall
Publikováno v:
IEEE Transactions on Computational Imaging. 3:110-125
Given an image, we wish to produce an image of larger size with significantly more pixels and higher image quality. This is generally known as the Single Image Super-Resolution (SISR) problem. The idea is that with sufficient training data (correspon
Publikováno v:
Signal Processing. 130:403-411
The problem of system classification consists of identifying the source system corresponding to a certain output signal. In the context of dynamical systems, the outputs are usually given in the form of time series, and this identification process in
Publikováno v:
SIAM Journal on Imaging Sciences. 10:1804-1844
Removal of noise from an image is an extensively studied problem in image processing. Indeed, the recent advent of sophisticated and highly effective denoising algorithms lead some to believe that existing methods are touching the ceiling in terms of