Maciej Wielgosz
Using LSTM recurrent neural networks for monitoring the LHC superconducting magnets
M Wielgosz, A Skoczeń, M Mertik
Nuclear Instruments and Methods in Physics Research Section A: Accelerators …, 2017
Mapping neural networks to FPGA-based IoT devices for ultra-low latency processing
M Wielgosz, M Karwatowski
Sensors 19 (13), 2981, 2019
Predictive maintenance of induction motors using ultra-low power wireless sensors and compressed recurrent neural networks
M Markiewicz, M Wielgosz, M Bocheński, W Tabaczyński, T Konieczny, ...
IEEE Access 7, 178891-178902, 2019
FPGA implementation of 64-bit exponential function for HPC
E Jamro, K Wiatr, M Wielgosz
2007 International Conference on Field Programmable Logic and Applications …, 2007
Methodologies of compressing a stable performance convolutional neural networks in image classification
M Al-Hami, M Pietron, R Casas, M Wielgosz
Neural Processing Letters 51 (1), 105-127, 2020
Highly efficient structure of 64-bit exponential function implemented in FPGAs
M Wielgosz, E Jamro, K Wiatr
Reconfigurable Computing: Architectures, Tools and Applications: 4th …, 2008
The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantization
M Wielgosz, M Mertik, A Skoczeń, E De Matteis
Engineering Applications of Artificial Intelligence 74, 166-185, 2018
Comparison of GPU and FPGA implementation of SVM algorithm for fast image segmentation
M Pietron, M Wielgosz, D Zurek, E Jamro, K Wiatr
Architecture of Computing Systems–ARCS 2013: 26th International Conference …, 2013
FPGA implementaton of strongly parallel histogram equalization
E Jamro, M Wielgosz, K Wiatr
2007 IEEE Design and Diagnostics of Electronic Circuits and Systems, 1-6, 2007
FPGA implementation of the dynamic Huffman encoder
E Jamro, M Wielgosz, K Wiatr
IFAC Proceedings Volumes 39 (21), 60-65, 2006
Comparison of Hybrid Sorting Algorithms Implemented on Different Parallel Hardware Platforms
D Żurek, M Pietroń, M Wielgosz, K Wiatr
Computer Science 14 (4), 679--691, 2013
Roadmap on artificial intelligence and big data techniques for superconductivity
M Yazdani-Asrami, W Song, A Morandi, G De Carne, J Murta-Pina, ...
Superconductor Science and Technology 36 (4), 043501, 2023
Retrain or not retrain?-efficient pruning methods of deep cnn networks
M Pietron, M Wielgosz
Computational Science–ICCS 2020: 20th International Conference, Amsterdam …, 2020
Recurrent Neural Networks for anomaly detection in the Post-Mortem time series of LHC superconducting magnets
M Wielgosz, A Skoczeń, M Mertik
arXiv preprint arXiv:1702.00833, 2017
FPGA–ARM heterogeneous system for high speed signal analysis
E Jamro, M Wielgosz, S Bieniasz, W Cioch
Solid State Phenomena 180, 207-213, 2012
Protection of superconducting industrial machinery using RNN-based anomaly detection for implementation in smart sensor
M Wielgosz, A Skoczeń, E De Matteis
Sensors 18 (11), 3933, 2018
Compression of convolutional neural network for natural language processing
K Wróbel, M Karwatowski, M Wielgosz, M Pietroń, K Wiatr
Computer Science 21 (1), 2020
FPGA implementation of the selected parts of the fast image segmentation
M Wielgosz, E Jamro, D Żurek, K Wiatr
Intelligent Tools for Building a Scientific Information Platform, 203-216, 2012
Parallel Implementation of Spatial Pooler in Hierarchical Temporal Memory.
M Pietron, M Wielgosz, K Wiatr
ICAART (2), 346-353, 2016
Using spatial pooler of hierarchical temporal memory to classify noisy videos with predefined complexity
M Wielgosz, M Pietroń
Neurocomputing 240, 84-97, 2017
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