no code implementations • 9 Dec 2023 • Kálmán Klapcsik, Bálint Gyires-Tóth, Juan Manuel Rosselló, Ferenc Hegedűs
The agent must choose the optimal pressure amplitude values to manipulate the bubble position in the range of $x/\lambda_0\in[0. 05, 0. 25]$.
1 code implementation • 16 Nov 2022 • Dániel Unyi, Bálint Gyires-Tóth
A major challenge in medical image analysis is the automated detection of biomarkers from neuroimaging data.
1 code implementation • 1 Sep 2022 • András Kalapos, Bálint Gyires-Tóth
In this paper, we elaborate and analyse the effectiveness of supervised and self-supervised pretraining approaches on downstream medical image segmentation, focusing on convergence and data efficiency.
1 code implementation • 7 Jun 2022 • Dániel Unyi, Ferdinando Insalata, Petar Veličković, Bálint Gyires-Tóth
Our evaluation shows that GNNs outperform EGNNs on aligned meshes, due to their ability to leverage the presence of a global coordinate system.
1 code implementation • 23 Apr 2022 • Bálint Gyires-Tóth, Csaba Zainkó
Telecommunications (for voice and video), and speech synthesis systems (for generated speech) are a few of the many applications of the method.
1 code implementation • 28 Apr 2021 • Gergely Hajgató, Bálint Gyires-Tóth, György Paál
The reconstruction method is based on K-localized spectral graph filters, wherewith graph convolution on water networks is possible.
1 code implementation • 13 Oct 2020 • Gergely Hajgató, György Paál, Bálint Gyires-Tóth
The main contribution of the presented approach is that the agent can run the pumps in real-time because it depends only on measurement data.
no code implementations • 1 Oct 2020 • Gergely Hajgató, Bálint Gyires-Tóth, György Paál
This database was used to train an encoder-decoder style deep convolutional neural network to predict the velocity distribution from the geometry.
no code implementations • 23 Sep 2020 • Péter Almási, Róbert Moni, Bálint Gyires-Tóth
In our approach, the agent is trained in a simulated environment and it is able to navigate both in a simulated and real-world environment.
no code implementations • RANLP 2019 • Sevinj Yolchuyeva, Géza Németh, Bálint Gyires-Tóth
Self-attention networks (SAN) have shown promising performance in various Natural Language Processing (NLP) scenarios, especially in machine translation.
1 code implementation • arXiv preprint 2019 • Sevinj Yolchuyeva, Géza Németh, Bálint Gyires-Tóth
The transformer network architecture is completely based on attention mechanisms, and it outperforms sequence-to-sequence models in neural machine translation without recurrent and convolutional layers.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
1 code implementation • 26 Sep 2019 • Patrik Reizinger, Bálint Gyires-Tóth
The aim of this paper is to introduce two widely applicable regularization methods based on the direct modification of weight matrices.
no code implementations • 16 Sep 2019 • Marco Henrique de Almeida Inácio, Rafael Izbicki, Bálint Gyires-Tóth
Given two distinct datasets, an important question is if they have arisen from the the same data generating function or alternatively how their data generating functions diverge from one another.