Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Arman Afrasiyabi"'
We introduce Mixture-based Feature Space Learning (MixtFSL) for obtaining a rich and robust feature representation in the context of few-shot image classification. Previous works have proposed to model each base class either with a single point or wi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9b8aa563f1d4cdab9f7081aee95810f8
http://arxiv.org/abs/2011.11872
http://arxiv.org/abs/2011.11872
Autor:
Fatios T. Yarman Vural, Diaa Badawi, Ozan Yildi, A. Enis Cetin, Baris Nasir, Arman Afrasiyabi
Publikováno v:
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing-Proceedings
ICASSP
ICASSP
We present a non-Euclidean vector product for artificial neural networks. The vector product operator does not require any multiplications while providing correlation information between two vectors. Ordinary neurons require inner product of two vect
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::174dbc06d2220333fdba04eea830257c
https://hdl.handle.net/11693/50181
https://hdl.handle.net/11693/50181
Publikováno v:
SIU
Proceedings of the IEEE 25th Signal Processing and Communications Applications Conference, SIU 2017
Proceedings of the IEEE 25th Signal Processing and Communications Applications Conference, SIU 2017
Date of Conference: 15-18 May 2017 Conference Name: IEEE 25th Signal Processing and Communications Applications Conference, SIU 2017 In this paper, we propose a new energy efficient neural network with the universal approximation property over space
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8cf331ecb48018c98a397ddd81249518
https://doi.org/10.1109/siu.2017.7960263
https://doi.org/10.1109/siu.2017.7960263
Publikováno v:
ICCI*CC
One of the major drawbacks of brain decoding from the functional magnetic resonance images (fMRI) is the very high dimension of feature space which consists of thousands of voxels in sequence of brain volumes, recorded during a cognitive stimulus. In
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::73a3c1f47370fb69e93d51aefcdb0330
https://doi.org/10.1109/icci-cc.2016.7862035
https://doi.org/10.1109/icci-cc.2016.7862035
Publikováno v:
SIU
In this study, we combine a voxel selection method with temporal mesh model to decode the discriminative information distributed in functional Magnetic Resonance Imaging (fMRI) data. We first employ one way Analysis of Variance (ANOVA) feature select
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b92f9865aff4027e7b47e5d9b2d46375
https://doi.org/10.1109/siu.2016.7496223
https://doi.org/10.1109/siu.2016.7496223
fMRG Verilerinde Temel Bileşenler Analizi ve Özyinemeli Boyut Eliminasyonu Kullanarak Boyut Küçültme
Publikováno v:
SIU
In this study, dimension reduction analysis is done on the Functional Magnetic Resonance Imagining (fMRI) data. The reduction of voxels which are the dimension in our case is the fundamental step in developing of a generalized model. To reach this go
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::34e6a18451b14be71cd954e2c28c467b
https://doi.org/10.1109/siu.2015.7130375
https://doi.org/10.1109/siu.2015.7130375