Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
CALABI-YAU VARIETIES AND MACHINE LEARNING
Download
master-tezi-FBE (6).pdf
Date
2022-7
Author
Bayramoglu, Tutku Doruk
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
292
views
89
downloads
Cite This
Techniques from Machine Learning (ML) have been applied to various mathematical problems in recent years. One such problem is the determination of Hodge numbers of Calabi-Yau varieties. In this thesis, a neural network model to estimate the Hodge numbers of complete intersection Calabi-Yau varieties is built and evaluated.
Subject Keywords
Calabi-Yau variety, Hodge number, Machine Learning, neural network
URI
https://hdl.handle.net/11511/98113
Collections
Graduate School of Natural and Applied Sciences, Thesis
Suggestions
OpenMETU
Core
Deep learning approach for laboratory mice grimace scaling
Eral, Mustafa; Halıcı, Uğur; Department of Electrical and Electronics Engineering (2016)
Deep learning is extremely attractive research topic in pattern recognition and machine learning areas. Applications in speech recognition, natural language processing, and machine vision fields gained huge acceleration in performance by employing deep learning. In this thesis, deep learning is used for medical purposes in order to scale pain degree of drug stimulated mice by examining facial grimace. For this purpose each frame in the videos in the training set were scaled manually by experts according to ...
Deep Learning-Enabled Technologies for Bioimage Analysis
Rabbi, Fazle; Dabbagh, Sajjad Rahmani; Angın, Pelin; Yetisen, Ali Kemal; Tasoglu, Savas (2022-02-01)
Deep learning (DL) is a subfield of machine learning (ML), which has recently demon-strated its potency to significantly improve the quantification and classification workflows in bio-medical and clinical applications. Among the end applications profoundly benefitting from DL, cellular morphology quantification is one of the pioneers. Here, we first briefly explain fundamental concepts in DL and then we review some of the emerging DL-enabled applications in cell morphology quantification in the fields of em...
EXTRACTION OF INTERPRETABLE DECISION RULES FROM BLACK-BOX MODELS FOR CLASSIFICATION TASKS
GALATALI, EGEMEN BERK; ALEMDAR, HANDE; Department of Computer Engineering (2022-8-31)
In this work, we have proposed a new method and ready to use workflow to extract simplified rule sets for a given Machine Learning (ML) model trained on a classifi- cation task. Those rules are both human readable and in the form of software code pieces thanks to the syntax of Python programming language. We have inspired from the power of Shapley Values as our source of truth to select most prominent features for our rule sets. The aim of this work to select the key interval points in given data in order t...
Wasserstein generative adversarial active learning for anomaly detection with gradient penalty
Duran, Hasan Ali; Ertekin Bolelli, Şeyda; Department of Computer Engineering (2021-9)
Anomaly detection has become a very important topic with the advancing machine learning techniques and is used in many different application areas. In this study, we approach differently than the anomaly detection methods performed on standard generative models and describe anomaly detection as a binary classification problem. However, in order to train a highly accurate classifier model, the number of anomaly data in data-sets is very limited, and with synthetic data produced using generative models, it ca...
MODELLING OF KERNEL MACHINES BY INFINITE AND SEMI-INFINITE PROGRAMMING
Ozogur-Akyuz, S.; Weber, Gerhard Wilhelm (2009-06-03)
In Machine Learning (ML) algorithms, one of the crucial issues is the representation of the data. As the data become heterogeneous and large-scale, single kernel methods become insufficient to classify nonlinear data. The finite combinations of kernels are limited up to a finite choice. In order to overcome this discrepancy, we propose a novel method of "infinite" kernel combinations for learning problems with the help of infinite and semi-infinite programming regarding all elements in kernel space. Looking...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
T. D. Bayramoglu, “CALABI-YAU VARIETIES AND MACHINE LEARNING,” M.S. - Master of Science, Middle East Technical University, 2022.