91 citations · 182 across the 11 of their papers we have counts for
11 papers
Whitening Consistently Improves Self-Supervised Learning
András Kalapos, Bálint Gyires-Tóth
Self-supervised learning (SSL) has been shown to be a powerful approach for learning visual representations. In this study, we propose incorporating ZCA whitening as the final laye…
CNN-JEPA: Self-Supervised Pretraining Convolutional Neural Networks Using Joint Embedding Predictive Architecture
András Kalapos, Bálint Gyires-Tóth
Self-supervised learning (SSL) has become an important approach in pretraining large neural networks, enabling unprecedented scaling of model and dataset sizes. While recent advanc…
Position control of an acoustic cavitation bubble by reinforcement learning
Kálmán Klapcsik, Bálint Gyires-Tóth, Juan Manuel Rosselló +1
A control technique is developed via Reinforcement Learning that allows arbitrary controlling of the position of an acoustic cavitation bubble in a dual-frequency standing acoustic…
Utility of Equivariant Message Passing in Cortical Mesh Segmentation
Dániel Unyi, Ferdinando Insalata, Petar Veličković +1
The automated segmentation of cortical areas has been a long-standing challenge in medical image analysis. The complex geometry of the cortex is commonly represented as a polygon m…
Deep Reinforcement Learning for Real-Time Optimization of Pumps in Water Distribution Systems
Gergely Hajgató, György Paál, Bálint Gyires-Tóth
Real-time control of pumps can be an infeasible task in water distribution systems (WDSs) because the calculation to find the optimal pump speeds is resource-intensive. The computa…
Predicting the flow field in a U-bend with deep neural networks
Gergely Hajgató, Bálint Gyires-Tóth, György Paál
This paper describes a study based on computational fluid dynamics (CFD) and deep neural networks that focusing on predicting the flow field in differently distorted U-shaped pipes…
Robust Reinforcement Learning-based Autonomous Driving Agent for Simulation and Real World
Péter Almási, Róbert Moni, Bálint Gyires-Tóth
Deep Reinforcement Learning (DRL) has been successfully used to solve different challenges, e.g. complex board and computer games, recently. However, solving real-world robotics ta…
Self-Attention Networks for Intent Detection
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. One of the main points of…
Transformer based Grapheme-to-Phoneme Conversion
Sevinj Yolchuyeva, Géza Németh, Bálint Gyires-Tóth
Attention mechanism is one of the most successful techniques in deep learning based Natural Language Processing (NLP). The transformer network architecture is completely based on a…
Stochastic Weight Matrix-based Regularization Methods for Deep Neural Networks
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. The first method, Weight Reinitialization, u…
Distance Assessment and Hypothesis Testing of High-Dimensional Samples using Variational Autoencoders
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 fr…