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publications
Hyphenation using deep neural networks
Published in XIV. Magyar Számítógépes Nyelvészeti Konferencia, 2018
Hyphenation algorithms are the computer based ways of syllabification and mostly used in typesetting, formatting documents as well as text-to-speech and speech recognition systems. We present a deep learning approach to automatic hyphenation of Hungarian text. Our experiments compare feed forward, recurrent and convolutional neural network approaches.
Recommended citation: Németh, G. D., Ács, J. (2018). "Hyphenation using deep neural networks" XIV. Magyar Számítógépes Nyelvészeti Konferencia http://negedng.github.io/files/2018-Hyphenation.pdf
A Snapshot of the Frontiers of Client Selection in Federated Learning
Published in Transactions on Machine Learning Research, 2022
Federated learning (FL) has been proposed as a privacy-preserving approach in distributed machine learning. A federated learning architecture consists of a central server and a number of clients that have access to private, potentially sensitive data. Clients are able to keep their data in their local machines and only share their locally trained model’s parameters with a central server that manages the collaborative learning process. FL has delivered promising results in real-life scenarios, such as healthcare, energy, and finance. However, when the number of participating clients is large, the overhead of managing the clients slows down the learning. Thus, client selection has been introduced as a strategy to limit the number of communicating parties at every step of the process. Since the early naive random selection of clients, several client selection methods have been proposed in the literature. Unfortunately, given that this is an emergent field, there is a lack of a taxonomy of client selection methods, making it hard to compare approaches. In this paper, we propose a taxonomy of client selection in Federated Learning that enables us to shed light on current progress in the field and identify potential areas of future research in this promising area of machine learning.
Recommended citation: Németh, G. D., Lozano, M. A., Quadrianto, N., & Oliver, N. (2022). " A Snapshot of the Frontiers of Client Selection in Federated Learning" Transactions on Machine Learning Research http://negedng.github.io/files/2022-Snapshot.pdf
talks
teaching
Introduction to the Theory of Computing 2, 2016+
Undegraduate course, Budapest University of Technology and Economics, Department of Computer Science and Information Theory, 2016
This is an undergraduate teaching assistant experience. The goal of the subject is to acquire the fundamental mathematical knowledge (in the area of linear algebra and number theory) necessary for software engineering studies.
Basics of programming
High school course, ELTE Radnóti Miklós School, 2016
This is a one year experience as a teacher for 11-12th grade students. The course is a specialisation to learn the basics of programming with the help of ColoBot, Processing and Java.
Introduction to the Theory of Computing 2, 2017
Undegraduate course, Budapest University of Technology and Economics, Department of Computer Science and Information Theory, 2017
This is an undergraduate teaching assistant experience. The goal of the subject is to acquire the fundamental mathematical knowledge (in the area of linear algebra and number theory) necessary for software engineering studies.
Advanced level programming
High school course, Fazekas Mihály School, 2017
This is an advanced level programming course for high school students. Students of this course competed in various levels of porgramming championships.
Introduction to the Theory of Computing 1, 2017
Undegraduate course, Budapest University of Technology and Economics, Department of Computer Science and Information Theory, 2017
This is an undergraduate teaching assistant experience. The goal of the subject is to acquire the fundamental mathematical knowledge (in the area of linear algebra and number theory) necessary for software engineering studies.
Basics of programming - online
Online course, Udemy, 2018
This is an online course teaching programming in Hungarian. Originally, the course was free but I realized that it generates a lack of motivation in the students. Therefore, I changed it to the minimal amount possible in Udemy.