Neural Networks

Artificial neural networks are computing systems inspired by the biological neural networks that constitute animal brains. Such systems learn to do tasks by considering examples, generally without task-specific programming. We use various models of neural networks in wide spectrum of tasks.

Publications

Cross-Lingual Approaches for Task-Specific Dialogue Act Recognition
Jiří Martínek and Christophe Cerisara and Pavel Král and Ladislav Lenc
17th International Conference on Artificial Intelligence Applications and Innovations (2021)
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Dialogue Act Recognition | Neural Networks
Evaluation Datasets for Cross-lingual Semantic Textual Similarity
Tomáš Hercig and Pavel Král
Recent Advances in Natural Language Processing (2021)
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Semantic Analysis | Neural Networks
Improving Face Recognition Methods based on POEM Features
Ladislav Lenc and Pavel Král
12th International Conference on Agents and Artificial Intelligence (2020)
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Neural Networks | Image Processing
Re-Ranking for Writer Identification and Writer Retrieval
Simon Jordan and Mathias Seuret and Pavel Král and Ladislav Lenc and Jiří Martínek and Barbara Wiermann and Tobias Schwinger and Andreas Maier and Vincent Christlein
14th IAPR International Workshop on Document Analysis Systems (2020)
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Neural Networks | Image Processing
Deep Generalized Max Pooling
Vincent Christlein and Anguelos Nicolaou and Pavel Král
15th International Conference on Document Analysis and Recognition (ICDAR 2019) (2019)
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Neural Networks
Hybrid Training Data for Historical Text OCR
Jiří Martínek and Ladislav Lenc and Pavel Král and Anguelos Nicolaou and Vincent Christlein
15th International Conference on Document Analysis and Recognition (ICDAR 2019) (2019)
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Neural Networks
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