Zobrazeno 1 - 10
of 4 372
pro vyhledávání: '"Luzi, P."'
We propose autophagy penalized likelihood estimation (PLE), an unbiased alternative to maximum likelihood estimation (MLE) which is more fair and less susceptible to model autophagy disorder (madness). Model autophagy refers to models trained on thei
Externí odkaz:
http://arxiv.org/abs/2405.13977
The rapid advancement of Generative Adversarial Networks (GANs) necessitates the need to robustly evaluate these models. Among the established evaluation criteria, the Fr\'{e}chetInception Distance (FID) has been widely adopted due to its conceptual
Externí odkaz:
http://arxiv.org/abs/2310.20636
Autor:
Alemohammad, Sina, Casco-Rodriguez, Josue, Luzi, Lorenzo, Humayun, Ahmed Imtiaz, Babaei, Hossein, LeJeune, Daniel, Siahkoohi, Ali, Baraniuk, Richard G.
Seismic advances in generative AI algorithms for imagery, text, and other data types has led to the temptation to use synthetic data to train next-generation models. Repeating this process creates an autophagous (self-consuming) loop whose properties
Externí odkaz:
http://arxiv.org/abs/2307.01850
Publikováno v:
REC: Interventional Cardiology (English Ed.), Vol 6, Iss 3, Pp 224-234 (2024)
ABSTRACT Assessment of the functional significance of coronary artery stenoses to guide percutaneous coronary intervention is widely performed using pressure wire fractional flow reserve during adenosine- or adenosine triphosphate-induced hyperemia.
Externí odkaz:
https://doaj.org/article/32af3ff12bae493d9a15d1086f572a82
Publikováno v:
REC: Interventional Cardiology, Vol 6, Iss 3, Pp 224-234 (2024)
RESUMEN La evaluación funcional de las estenosis coronarias para guiar los procedimientos de intervencionismo coronario percutáneo se realiza frecuentemente midiendo la reserva fraccional de flujo durante la hiperemia inducida por adenosina o trifo
Externí odkaz:
https://doaj.org/article/933368236d6c46a4bde5dadcfb089de0
Autor:
Ren, Simiao, Luzi, Francesco, Lahrichi, Saad, Kassaw, Kaleb, Collins, Leslie M., Bradbury, Kyle, Malof, Jordan M.
Recently, the first foundation model developed specifically for image segmentation tasks was developed, termed the "Segment Anything Model" (SAM). SAM can segment objects in input imagery based on cheap input prompts, such as one (or more) points, a
Externí odkaz:
http://arxiv.org/abs/2304.13000
Recent object detection models for infrared (IR) imagery are based upon deep neural networks (DNNs) and require large amounts of labeled training imagery. However, publicly-available datasets that can be used for such training are limited in their si
Externí odkaz:
http://arxiv.org/abs/2212.12824
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLVIII-1-2024, Pp 1-6 (2024)
This work aims to compare and assess the performance of certain methodologies for shoreline mapping based on the use of medium (10 m) and high resolution (3 m) multi-spectral imagery, provided by Sentinel-2 (S2) and PlanetScope (PS), respectively. Be
Externí odkaz:
https://doaj.org/article/1a5e9aada4714f8eb4fdf76cd48a7c57
We study the generalization behavior of transfer learning of deep neural networks (DNNs). We adopt the overparameterization perspective -- featuring interpolation of the training data (i.e., approximately zero train error) and the double descent phen
Externí odkaz:
http://arxiv.org/abs/2211.11074
Autor:
Luzi, Lorenzo, LeJeune, Daniel, Siahkoohi, Ali, Alemohammad, Sina, Saragadam, Vishwanath, Babaei, Hossein, Liu, Naiming, Wang, Zichao, Baraniuk, Richard G.
We study the interpolation capabilities of implicit neural representations (INRs) of images. In principle, INRs promise a number of advantages, such as continuous derivatives and arbitrary sampling, being freed from the restrictions of a raster grid.
Externí odkaz:
http://arxiv.org/abs/2211.00219