Text normalization with convolutional neural networks. - Sevinj Yolchuyeva; Géza Németh; Bálint Gyires-Tóth (Int. J. Speech Technol., 2018)

Utilizing Deep Learning for Mobile Telecommunications Network Management. - Bálint Gyires-Tóth; Pál Varga; Tamas Tothfalusi (IM, 2019)

Transformer Based Grapheme-to-Phoneme Conversion. - Sevinj Yolchuyeva; Géza Németh; Bálint Gyires-Tóth (INTERSPEECH, 2019)

Stochastic Weight Matrix-Based Regularization Methods for Deep Neural Networks. - Patrik Reizinger; Bálint Gyires-Tóth (LOD, 2019)

Self-Attention Networks for Intent Detection. - Sevinj Yolchuyeva; Géza Németh; Bálint Gyires-Tóth (RANLP, 2019)

Distance Assessment and Hypothesis Testing of High-Dimensional Samples using Variational Autoencoders. - Marco Henrique de Almeida Inácio; Rafael Izbicki; Bálint Gyires-Tóth (CoRR, 2019)

Robust Reinforcement Learning-based Autonomous Driving Agent for Simulation and Real World. - Péter Almási; Róbert Moni; Bálint Gyires-Tóth (IJCNN, 2020)

GPU Accelerated Data Preparation for Limit Order Book Modeling. - Viktor Burján; Bálint Gyires-Tóth (LOD (1), 2020)

Predicting the flow field in a U-bend with deep neural networks. - Gergely Hajgató; Bálint Gyires-Tóth; György Paál (CoRR, 2020)

Deep Reinforcement Learning for Real-Time Optimization of Pumps in Water Distribution Systems. - Gergely Hajgató; György Paál; Bálint Gyires-Tóth (CoRR, 2020)

Scalable Graph Convolutional Variational Autoencoders. - Dániel Unyi; Bálint Gyires-Tóth (SACI, 2021)

Reconstructing nodal pressures in water distribution systems with graph neural networks. - Gergely Hajgató; Bálint Gyires-Tóth; György Paál (CoRR, 2021)

Distance assessment and analysis of high-dimensional samples using variational autoencoders. - Marco Henrique de Almeida Inácio; Rafael Izbicki; Bálint Gyires-Tóth (Inf. Sci., 2021)

Self-supervised Pretraining for 2D Medical Image Segmentation. - András Kalapos; Bálint Gyires-Tóth (ECCV Workshops (7), 2022)

Utility of Equivariant Message Passing in Cortical Mesh Segmentation. - Dániel Unyi; Ferdinando Insalata; Petar Velickovic; Bálint Gyires-Tóth (MIUA, 2022)

Neurodevelopmental Phenotype Prediction: A State-of-the-Art Deep Learning Model. - Dániel Unyi; Bálint Gyires-Tóth (ML4H@NeurIPS, 2022)

PredMaX: Predictive maintenance with explainable deep convolutional autoencoders. - Gergely Hajgató; Richárd Wéber; Botond Szilágyi; Balázs Tóthpál; Bálint Gyires-Tóth; Csaba Hos (Adv. Eng. Informatics, 2022)

Improving Self-Supervised Learning-based MOS Prediction Networks. - Bálint Gyires-Tóth; Csaba Zainkó (CoRR, 2022)

Language of the Market: NLP-Driven Sentiment Analysis of Hungarian Economy. - Frigyes Viktor Arthur; Bálint Gyires-Tóth; Máté Imre Debreczeni; Lívia Réka Ónozó (CogInfoCom, 2023)

Experiences With Deep Learning Enhanced Steering Mechanisms for Debugging of Fundamental Cloud Services. - Róbert Lovas; Erno Rigó; Dániel Unyi; Bálint Gyires-Tóth (IEEE Access, 2023)

Position control of an acoustic cavitation bubble by reinforcement learning. - Kálmán Klapcsik; Bálint Gyires-Tóth; Juan Manuel Rosselló; Ferenc Hegedüs (CoRR, 2023)

Whitening Consistently Improves Self-Supervised Learning. - András Kalapos; Bálint Gyires-Tóth (ICMLA, 2024)

CNN-JEPA: Self-Supervised Pretraining Convolutional Neural Networks Using Joint Embedding Predictive Architecture. - András Kalapos; Bálint Gyires-Tóth (ICMLA, 2024)

Self-Supervised Pretraining for Cortical Surface Analysis. - Dániel Unyi; Bálint Gyires-Tóth (MIUA (1), 2024)

Leveraging LLMs for Financial News Analysis and Macroeconomic Indicator Nowcasting. - Lívia Réka Ónozó; Frigyes Viktor Arthur; Bálint Gyires-Tóth (IEEE Access, 2024)

Advancing Deep-Learning in NLP: From Network Logs to Text Classification. - Lívia Réka Ónozó; Bálint Gyires-Tóth (NOMS, 2025)

Self-supervised domain transfer for reinforcement learning-based autonomous driving agent. - Róbert Moni; Bálint Gyires-Tóth (Expert Syst. Appl., 2025)