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Bálint Gyires-Tóth
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2020 – today
- 2024
- [c12]Dániel Unyi
, Bálint Gyires-Tóth
Self-Supervised Pretraining for Cortical Surface Analysis. MIUA (1) 2024: 96-108 - [i15]András Kalapos, Bálint Gyires-Tóth:
CNN-JEPA: Self-Supervised Pretraining Convolutional Neural Networks Using Joint Embedding Predictive Architecture. CoRR abs/2408.07514 (2024) - [i14]András Kalapos, Bálint Gyires-Tóth:
Whitening Consistently Improves Self-Supervised Learning. CoRR abs/2408.07519 (2024) - 2023
- [j4]Róbert Lovas
Experiences With Deep Learning Enhanced Steering Mechanisms for Debugging of Fundamental Cloud Services. IEEE Access 11: 26403-26418 (2023) - [c11]Frigyes Viktor Arthur, Bálint Gyires-Tóth, Máté Imre Debreczeni, Lívia Réka Ónozó:
Language of the Market: NLP-Driven Sentiment Analysis of Hungarian Economy. CogInfoCom 2023: 93-98 - [i13]Kálmán Klapcsik, Bálint Gyires-Tóth, Juan Manuel Rosselló, Ferenc Hegedüs:
Position control of an acoustic cavitation bubble by reinforcement learning. CoRR abs/2312.05674 (2023) - 2022
- [j3]Gergely Hajgató
PredMaX: Predictive maintenance with explainable deep convolutional autoencoders. Adv. Eng. Informatics 54: 101778 (2022) - [c10]András Kalapos
Self-supervised Pretraining for 2D Medical Image Segmentation. ECCV Workshops (7) 2022: 472-484 - [c9]Dániel Unyi, Ferdinando Insalata
Utility of Equivariant Message Passing in Cortical Mesh Segmentation. MIUA 2022: 412-424 - [c8]Dániel Unyi, Bálint Gyires-Tóth:
Neurodevelopmental Phenotype Prediction: A State-of-the-Art Deep Learning Model. ML4H@NeurIPS 2022: 279-289 - [i12]Bálint Gyires-Tóth, Csaba Zainkó:
Improving Self-Supervised Learning-based MOS Prediction Networks. CoRR abs/2204.11030 (2022) - [i11]Dániel Unyi, Ferdinando Insalata
Utility of Equivariant Message Passing in Cortical Mesh Segmentation. CoRR abs/2206.03164 (2022) - [i10]András Kalapos, Bálint Gyires-Tóth:
Self-Supervised Pretraining for 2D Medical Image Segmentation. CoRR abs/2209.00314 (2022) - [i9]Dániel Unyi, Bálint Gyires-Tóth:
Neurodevelopmental Phenotype Prediction: A State-of-the-Art Deep Learning Model. CoRR abs/2211.08831 (2022) - 2021
- [j2]Marco Henrique de Almeida Inácio, Rafael Izbicki
Distance assessment and analysis of high-dimensional samples using variational autoencoders. Inf. Sci. 557: 407-420 (2021) - [c7]Dániel Unyi, Bálint Gyires-Tóth:
Scalable Graph Convolutional Variational Autoencoders. SACI 2021: 467-472 - [i8]Gergely Hajgató, Bálint Gyires-Tóth, György Paál:
Reconstructing nodal pressures in water distribution systems with graph neural networks. CoRR abs/2104.13619 (2021) - 2020
- [c6]Péter Almási, Róbert Moni, Bálint Gyires-Tóth:
Robust Reinforcement Learning-based Autonomous Driving Agent for Simulation and Real World. IJCNN 2020: 1-8 - [c5]Viktor Burján, Bálint Gyires-Tóth:
GPU Accelerated Data Preparation for Limit Order Book Modeling. LOD (1) 2020: 385-397 - [i7]Sevinj Yolchuyeva, Géza Németh, Bálint Gyires-Tóth:
Transformer based Grapheme-to-Phoneme Conversion. CoRR abs/2004.06338 (2020) - [i6]Sevinj Yolchuyeva, Géza Németh, Bálint Gyires-Tóth:
Self-Attention Networks for Intent Detection. CoRR abs/2006.15585 (2020) - [i5]Péter Almási, Róbert Moni, Bálint Gyires-Tóth:
Robust Reinforcement Learning-based Autonomous Driving Agent for Simulation and Real World. CoRR abs/2009.11212 (2020) - [i4]Gergely Hajgató
Predicting the flow field in a U-bend with deep neural networks. CoRR abs/2010.00258 (2020) - [i3]Gergely Hajgató
Deep Reinforcement Learning for Real-Time Optimization of Pumps in Water Distribution Systems. CoRR abs/2010.06460 (2020)
2010 – 2019
- 2019
- [c4]Bálint Gyires-Tóth, Pál Varga, Tamas Tothfalusi:
Utilizing Deep Learning for Mobile Telecommunications Network Management. IM 2019: 575-580 - [c3]Sevinj Yolchuyeva, Géza Németh
Transformer Based Grapheme-to-Phoneme Conversion. INTERSPEECH 2019: 2095-2099 - [c2]Patrik Reizinger
Stochastic Weight Matrix-Based Regularization Methods for Deep Neural Networks. LOD 2019: 45-57 - [c1]Sevinj Yolchuyeva, Géza Németh
Self-Attention Networks for Intent Detection. RANLP 2019: 1373-1379 - [i2]Marco Henrique de Almeida Inácio, Rafael Izbicki, Bálint Gyires-Tóth:
Distance Assessment and Hypothesis Testing of High-Dimensional Samples using Variational Autoencoders. CoRR abs/1909.07182 (2019) - [i1]Patrik Reizinger, Bálint Gyires-Tóth:
Stochastic Weight Matrix-based Regularization Methods for Deep Neural Networks. CoRR abs/1909.11977 (2019) - 2018
- [j1]Sevinj Yolchuyeva, Géza Németh
Text normalization with convolutional neural networks. Int. J. Speech Technol. 21(3): 589-600 (2018)
Coauthor Index
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last updated on 2024-09-25 00:38 CEST by the dblp team
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