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ORCIDTamás Varga
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2020 – today
- 2024
[c7]Tamás Varga
, Yaiza Aragonés-Soria, Manuel Oriol:
Quantum types: going beyond qubits and quantum gates. Q-SE@ICSE 2024: 49-52- 2021
- [j8]Jan Strube, Malachi Schram, Sabiha Rustam, Zachary Kennedy, Tamás Varga:
Identifying build orientation of 3D-printed materials using convolutional neural networks. Stat. Anal. Data Min. 14(6): 575-582 (2021) - 2020
- [j7]Alex Kummer, Tamás Varga, Lajos Nagy:
Semi-batch reactor control with NMPC avoiding thermal runaway. Comput. Chem. Eng. 134: 106694 (2020) - [j6]Alex Kummer, Lajos Nagy, Tamás Varga:
NMPC-based control scheme for a semi-batch reactor under parameter uncertainty. Comput. Chem. Eng. 141: 106998 (2020)
2010 – 2019
- 2019
- [j5]Alex Kummer, Tamás Varga, János Abonyi:
Genetic programming-based development of thermal runaway criteria. Comput. Chem. Eng. 131 (2019) - 2017
- [j4]Alex Kummer, Tamás Varga:
Feeding trajectory optimization in fed-batch reactor with highly exothermic reactions. Comput. Chem. Eng. 98: 1-11 (2017) - 2015
- [j3]Ioana A. Popescu, Tamás Varga, Attila Egedy, Szabolcs Fogarasi, Tibor Chován, Árpád Imre-Lucaci, Petru Ilea:
Kinetic models based on analysis of the dissolution of copper, zinc and brass from WEEE in a sodium persulfate environment. Comput. Chem. Eng. 83: 214-220 (2015) - 2014
- [c6]Tamás Varga, László Bacsárdi:
Efficient simulation of quantum-based searching. SoftCOM 2014: 403-407 - [i1]Tamás Varga, András Király, János Abonyi:
Improvement of PSO algorithm by memory based gradient search - application in inventory management. CoRR abs/1410.5652 (2014)
2000 – 2009
- 2009
- [j2]Tamás Varga, Ferenc Szeifert, János Abonyi:
Decision tree and first-principles model-based approach for reactor runaway analysis and forecasting. Eng. Appl. Artif. Intell. 22(4-5): 569-578 (2009) - 2008
- [p1]Tamás Varga, Horst Bunke:
Perturbation Models for Generating Synthetic Training Data in Handwriting Recognition. Machine Learning in Document Analysis and Recognition 2008: 333-360 - 2007
- [c5]F. Lüthy, Tamás Varga, Horst Bunke:
Using Hidden Markov Models as a Tool for Handwritten Text Line Segmentation. ICDAR 2007: 8- - 2006
- [b1]Tamás Varga:
Off-line cursive handwriting recognition using synthetic training data. University of Bern, 2006, ISBN 978-3-89838-300-4, pp. 1-109 - 2005
- [c4]Tamás Varga, Horst Bunke:
Tree Structure forWord Extraction from Handwritten Text Lines. ICDAR 2005: 352-356 - 2004
- [j1]Tamás Varga, Horst Bunke:
Offline Handwriting Recognition Using Synthetic Training Data Produced By Means Of A Geometrical Distortion Model. Int. J. Pattern Recognit. Artif. Intell. 18(7): 1285-1302 (2004) - [c3]Tamás Varga, Horst Bunke:
Comparing natural and synthetic training data for off-line cursive handwriting recognition. IWFHR 2004: 221-225 - [c2]Tamás Varga, Horst Bunke:
Off-line Handwritten Textline Recognition Using a Mixture of Natural and Synthetic Training Data. ICPR (2) 2004: 545-549 - 2003
- [c1]Tamás Varga, Horst Bunke:
Generation of Synthetic Training Data for an HMM-based Handwriting Recognition System. ICDAR 2003: 618-622
Coauthor Index
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