Search Results for author: Amir Ahooye Atashin

Found 6 papers, 2 papers with code

Variational Leakage: The Role of Information Complexity in Privacy Leakage

1 code implementation5 Jun 2021 Amir Ahooye Atashin, Behrooz Razeghi, Deniz Gündüz, Slava Voloshynovskiy

We study the role of information complexity in privacy leakage about an attribute of an adversary's interest, which is not known a priori to the system designer.

Attribute Face Recognition +3

An efficient projection neural network for $\ell_1$-regularized logistic regression

1 code implementation12 May 2021 Majid Mohammadi, Amir Ahooye Atashin, Damian A. Tamburri

$\ell_1$ regularization has been used for logistic regression to circumvent the overfitting and use the estimated sparse coefficient for feature selection.

feature selection regression

SANOM Results for OAEI 2019

no code implementations9 Jun 2020 Majid Mohammadi, Amir Ahooye Atashin, Wout Hofman, Yao-Hua Tan

Simulated annealing-based ontology matching (SANOM) participates for the second time at the ontology alignment evaluation initiative (OAEI) 2019.

Anatomy Ontology Matching

Comparison of ontology alignment systems across single matching task via the McNemar's test

no code implementations29 Mar 2017 Majid Mohammadi, Amir Ahooye Atashin, Wout Hofman, Yao-Hua Tan

Ontology alignment is widely-used to find the correspondences between different ontologies in diverse fields. After discovering the alignments, several performance scores are available to evaluate them. The scores typically require the identified alignment and a reference containing the underlying actual correspondences of the given ontologies. The current trend in the alignment evaluation is to put forward a new score(e. g., precision, weighted precision, etc.

Anatomy

Training LDCRF model on unsegmented sequences using Connectionist Temporal Classification

no code implementations26 Jun 2016 Amir Ahooye Atashin, Kamaledin Ghiasi-Shirazi, Ahad Harati

Experimental results on two gesture recognition tasks show that the proposed method outperforms LDCRFs, hidden Markov models, and conditional random fields.

Classification General Classification +4

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