MW
Maciej Wielgosz
cs.CVcs.LGcs.AIcs.NEcs.ROphysics.acc-phphysics.ins-detcs.CLneuromorphic computingspiking neural networks
On Valency
published · living versionsW_xbcpb9k9·v1 · currentpublished
3Dtrees.earth: community research infrastructure for tree and forest analysis using close-range LiDAR data
with Janusch Vajna-Jehle, Kilian Gerberding, Daniel Lusk, Teja Kattenborn +273
1 version
Preprints & journals
43 papers in the corpus · 2016–2026Toward a foundation model for forest point clouds2609.24787v1 · Yuanwen Yue, Stefano Puliti, Damien Robert et al.2026 · 0 citationsarXiv
Spiking Neural Network Actor-Critic Proximal Policy Optimization Control for Autonomous UAV Navigation Through Constrained Openings in Civil Infrastructure and Buildings2609.23643v1 · Francis Noah Walugembe, Maciej Wielgosz, Tomaz Gorican et al.2026 · 0 citationsarXiv
Unsupervised spiking feature learning for event-based pedestrian crossing detection: approaching supervised accuracy without labelled training data2609.31671v1 · Henok Teklu, Mustafa Sakhai, Matej Mertik et al.2026 · 0 citationsarXiv
Pedestrian Crossing Intent Classification From Event-Based Vision Using Convolutional Spiking Neural Networks With Temporal Augmentation2609.13328v1 · Henok Teklu, Mustafa Sakhai, Maciej Wielgosz et al.2026 · 0 citationsarXiv
A Time-Multiplexed Spiking Neural Network Accelerator with Pipelined Readout for FPGA Inference2608.00595v1 · Reza Ansari, Maciej Wielgosz2026 · 0 citationsarXiv
SegmentAnyTreeV2: Scaling Transformer-Based Tree Instance Segmentation Across Sensors, Platforms, and Forests2606.08206v2 · Maciej Wielgosz, Stefano Puliti, Rasmus Astrup2026 · 0 citationsarXiv
ARCANE-PedSynth: Synthetic Multi-Pedestrian Datasets with Behavioural Crossing Annotations2605.24950v1 · Muhammad Naveed Riaz, Maciej Wielgosz, Antonio M. L'opez Pena2026 · 0 citationsarXiv
DVS-PedX: Synthetic-and-Real Event-Based Pedestrian Dataset.41786772 · Sakhai, Mustafa, Sithu, Kaung, Oke, Min Khant Soe et al.2026 · 2 citationsScientific data. 2026;13(1)
Operational manifolds in spiking neural networks.41788544 · Mazurek, Szymon, Caputa, Jakub, Maj, Piotr et al.2026 · 1 citationFrontiers in neuroscience. 2026;20:1755119
Compression and Inference of Spiking Neural Networks on Resource-Constrained Hardware2511.12136v1 · Karol C. Jurzec, Tomasz Szydlo, Maciej Wielgosz2025 · 0 citationsarXiv
Three-factor learning in spiking neural networks: An overview of methods and trends from a machine learning perspective.41472829 · Mazurek, Szymon, Caputa, Jakub, Argasiński, Jan K et al.2025 · 3 citationsPatterns (New York, N.Y.). 2025;6(12):101414
Analyzing Internal Activity and Robustness of SNNs Across Neuron Parameter Space2507.14757v1 · Szymon Mazurek, Jakub Caputa, Maciej Wielgosz2025 · 1 citationarXiv
ForestFormer3D: A Unified Framework for End-to-End Segmentation of Forest LiDAR 3D Point Clouds2506.16991v2 · Binbin Xiang, Maciej Wielgosz, Stefano Puliti et al.2025 · 13 citationsarXiv
Spatiotemporal Analysis of Forest Machine Operations Using 3D Video Classification2505.24375v1 · Maciej Wielgosz, Simon Berg, Heikki Korpunen et al.2025 · 0 citationsarXiv
Pedestrian intention prediction in Adverse Weather Conditions with Spiking Neural Networks and Dynamic Vision Sensors2406.00473v1 · Mustafa Sakhai, Szymon Mazurek, Jakub Caputa et al.2024 · 1 citationarXiv
Using Super-Resolution for Enhancing Visual Perception and Segmentation Performance in Veterinary Cytology.38541646 · Caputa, Jakub, Wielgosz, Maciej, Łukasik, Daria et al.2024 · 2 citationsLife (Basel, Switzerland). 2024;14(3)
SegmentAnyTree: A sensor and platform agnostic deep learning model for tree segmentation using laser scanning data2401.15739v1 · Maciej Wielgosz, Stefano Puliti, Binbin Xiang et al.2024 · 126 citationsRemote Sensing of Environment, Volume 313, 2024, 114367, ISSN 0034-4257
Synthetic Data Generation Framework, Dataset, and Efficient Deep Model for Pedestrian Intention Prediction2401.06757v2 · Muhammad Naveed Riaz, Maciej Wielgosz, Abel Garcia Romera et al.2024 · 9 citations26th IEEE International Conference on Intelligent Transportation Systems ITSC 2023
Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning2312.15084v2 · Binbin Xiang, Maciej Wielgosz, Theodora Kontogianni et al.2023 · 149 citationsarXiv
FOR-instance: a UAV laser scanning benchmark dataset for semantic and instance segmentation of individual trees2309.01279v1 · Stefano Puliti, Grant Pearse, Peter Surov'y et al.2023 · 31 citationsarXiv
Computer-Aided Cytology Diagnosis in Animals: CNN-Based Image Quality Assessment for Accurate Disease Classification2308.06055v1 · Jan Krupi'nski, Maciej Wielgosz, Szymon Mazurek et al.2023 · 0 citationsarXiv
Using simulation to calibrate real data acquisition in veterinary medicine2307.11695v1 · Krystian Strza\lka, Szymon Mazurek, Maciej Wielgosz et al.2023 · 0 citationsarXiv
Assessing Dataset Quality Through Decision Tree Characteristics in Autoencoder-Processed Spaces2306.15392v1 · Szymon Mazurek, Maciej Wielgosz2023 · 6 citationsarXiv
Segmentation of the veterinary cytological images for fast neoplastic tumors diagnosis2305.04332v1 · Jakub Grzeszczyk, MichałKarwatowski, Daria \Lukasik et al.2023 · 0 citationsarXiv
Point2Tree(P2T) -- framework for parameter tuning of semantic and instance segmentation used with mobile laser scanning data in coniferous forest2305.02651v1 · Maciej Wielgosz, Stefano Puliti, Phil Wilkes et al.2023 · 39 citationsarXiv
CARLA-BSP: a simulated dataset with pedestrians2305.00204v1 · Maciej Wielgosz, Antonio M. L'opez, Muhammad Naveed Riaz2023 · 1 citationarXiv
Modern Cybersecurity Solution using Supervised Machine Learning2109.07593v1 · Mustafa Sakhai, Maciej Wielgosz2021 · 3 citationsarXiv
Retrain or not retrain? -- efficient pruning methods of deep CNN networks2002.07051v1 · Marcin Pietron, Maciej Wielgosz2020 · 22 citationsarXiv
Falls Prediction in eldery people using Gated Recurrent Units1908.01050v1 · Marcin Radzio, Maciej Wielgosz, Matej Mertik2019 · 0 citationsarXiv
Mapping Neural Networks to FPGA-Based IoT Devices for Ultra-Low Latency Processing.31284516 · Wielgosz, Maciej, Karwatowski, Michał2020 · 29 citationsSensors (Basel, Switzerland). 2019;19(13)
Protection of Superconducting Industrial Machinery Using RNN-Based Anomaly Detection for Implementation in Smart Sensor.30441813 · Wielgosz, Maciej, Skoczeń, Andrzej, De Matteis, Ernesto2018 · 13 citationsSensors (Basel, Switzerland). 2018;18(11)
Convolutional neural network compression for natural language processing1805.10796v1 · Krzysztof Wr'obel, Marcin Pietro'n, Maciej Wielgosz et al.2018 · 10 citationsarXiv
The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantization1709.09883v2 · Maciej Wielgosz, Matej Mertik, Andrzej Skocze'n et al.2017 · 23 citationsEngineering Applications of Artificial Intelligence, Volume 74, 2018, Pages 166-185
Improving text classification with vectors of reduced precision1706.06363v1 · Krzysztof Wr'obel, Maciej Wielgosz, Marcin Pietro'n et al.2017 · 5 citationsarXiv
A Conceptual Framework for Supporting a Rapid Design of Web Applications for Data Analysis of Electrical Quality Assurance Data for the LHC1702.01270v1 · Matej Mertik, Maciej Wielgosz2017 · 0 citationsarXiv
Recurrent Neural Networks for anomaly detection in the Post-Mortem time series of LHC superconducting magnets1702.00833v1 · Maciej Wielgosz, Andrzej Skocze'n, Matej Mertik2017 · 7 citationsarXiv
The observer-assisted method for adjusting hyper-parameters in deep learning algorithms1611.10328v1 · Maciej Wielgosz2016 · 1 citationarXiv
Using LSTM recurrent neural networks for monitoring the LHC superconducting magnets1611.06241v2 · Maciej Wielgosz, Andrzej Skocze'n, Matej Mertik2016 · 82 citationsarXiv
A Conceptual Development of Quench Prediction App build on LSTM and ELQA framework1610.09201v1 · Matej Mertik, Maciej Wielgosz, Andrzej Skocze'n2016 · 1 citationarXiv
FPGA implementation of the procedures for video quality assessment1609.06109v3 · Maciej Wielgosz, MichałKarwatowski, Marcin Pietro'n et al.2016 · 0 citationsarXiv
Using Spatial Pooler of Hierarchical Temporal Memory to classify noisy videos with predefined complexity1609.03093v2 · Maciej Wielgosz, Marcin Pietro'n2016 · 12 citationsNeurocomputing 240 (2017), 84-97
OpenCL-accelerated object classification in video streams using Spatial Pooler of Hierarchical Temporal Memory1608.01966v1 · Maciej Wielgosz, Marcin Pietro'n2016 · 1 citationInternational Journal of Advanced Computer Science and Applications (IJACSA), 8 (2), 2017
Formal analysis of HTM Spatial Pooler performance under predefined operation conditions1607.00791v1 · M. Pietron, M. Wielgosz, K. Wiatr2016 · 12 citationsarXiv
Career total: 147 works. 43 are in this corpus.
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