基本信息
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职业迁徙
个人简介
His research is focused on pattern recognition, machine learning, and computational data analytics with application in various fields ranging from signal and speech processing to medical data analysis and other data modeling problems from industrial applications. He is particularly interested in probabilistic graphical models for reasoning under uncertainty, discriminative and hybrid learning paradigms, deep neural networks, and sequence modeling. Graphical models unite probability and graph theory and allow to efficiently formalize both static and dynamic, as well as linear and nonlinear systems and processes. They provide an approach to deal with two inherent problems throughout applied mathematics and engineering, namely, uncertainty and complexity. His recent interest in deep learning is nourished by the remarkable performance boost in many image,signal and speech processing problems. This is particularly true when having big amounts of data and almost unlimited computing resources available. Here, the research is focused on resource-efficient deep learning methods for constraint computing infrastructure of real-world applications.
研究兴趣
论文共 218 篇作者统计合作学者相似作者
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JOURNAL OF MACHINE LEARNING RESEARCH (2024): 1-51
引用34浏览0EI引用
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Benedikt Kantz, Clemens Staudinger,Christoph Feilmayr, Johannes Wachlmayr, Alexander Haberl, Stefan Schuster,Franz Pernkopf
CoRR (2024)
引用0浏览0EI引用
0
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Data in briefpp.110229-110229, (2024)
IEEE Transactions on Applied Superconductivityno. 4 (2024): 1-12
IEEE International Conference on Acoustics, Speech, and Signal Processingpp.6695-6699, (2024)
ICASSPpp.1-5, (2023)
2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP)pp.1-6, (2023)
2024 IEEE Radar Conference (RadarConf24)pp.1-6, (2023)
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作者统计
#Papers: 217
#Citation: 4181
H-Index: 30
G-Index: 54
Sociability: 6
Diversity: 0
Activity: 2
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