Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
119 tokens/sec
GPT-4o
56 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
6 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Hyperdimensional computing: a fast, robust and interpretable paradigm for biological data (2402.17572v1)

Published 27 Feb 2024 in cs.LG and q-bio.QM

Abstract: Advances in bioinformatics are primarily due to new algorithms for processing diverse biological data sources. While sophisticated alignment algorithms have been pivotal in analyzing biological sequences, deep learning has substantially transformed bioinformatics, addressing sequence, structure, and functional analyses. However, these methods are incredibly data-hungry, compute-intensive and hard to interpret. Hyperdimensional computing (HDC) has recently emerged as an intriguing alternative. The key idea is that random vectors of high dimensionality can represent concepts such as sequence identity or phylogeny. These vectors can then be combined using simple operators for learning, reasoning or querying by exploiting the peculiar properties of high-dimensional spaces. Our work reviews and explores the potential of HDC for bioinformatics, emphasizing its efficiency, interpretability, and adeptness in handling multimodal and structured data. HDC holds a lot of potential for various omics data searching, biosignal analysis and health applications.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (128)
  1. A brief history of bioinformatics. Briefings in Bioinformatics. 2019;20(6):1981–1996. doi:10.1093/bib/bby063.
  2. Deep Learning. Nature. 2015;521(7553):436–444. doi:10.1038/nature14539.
  3. Kernel Methods in Computational Biology. MIT Press; 2004.
  4. A guide to machine learning for biologists. Nature Reviews Molecular Cell Biology. 2022;23(1):40–55. doi:10.1038/s41580-021-00407-0.
  5. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596(7873):583–589. doi:10.1038/s41586-021-03819-2.
  6. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science. 2023;379(6637):1123–1130. doi:10.1126/science.ade2574.
  7. Accurate prediction of protein structures and interactions using a three-track neural network. Science. 2021;373(6557):871–876. doi:10.1126/science.abj8754.
  8. De novo design of luciferases using deep learning. Nature. 2023;614(7949):774–780. doi:10.1038/s41586-023-05696-3.
  9. An overview of deep learning in medical imaging. Informatics in Medicine Unlocked. 2021;26:100723.
  10. Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii. Nature Chemical Biology. 2023;19(11):1342–1350.
  11. Current Progress and Open Challenges for Applying Deep Learning across the Biosciences. Nature Communications. 2022;13(1):1728. doi:10.1038/s41467-022-29268-7.
  12. Han H, Liu X. The Challenges of Explainable AI in Biomedical Data Science. BMC Bioinformatics. 2022;22(12):443. doi:10.1186/s12859-021-04368-1.
  13. Methods for Interpreting and Understanding Deep Neural Networks. Digital Signal Processing. 2018;73:1–15. doi:10.1016/j.dsp.2017.10.011.
  14. Seeing is Believing: Brain-Inspired Modular Training for Mechanistic Interpretability. arXiv. 2023;.
  15. Geometric Deep Learning of RNA Structure. Science. 2021;373(6558):1047–1051. doi:10.1126/SCIENCE.ABE5650.
  16. A review of deep transfer learning and recent advancements. Technologies. 2023;11(2):40.
  17. Learning Both Weights and Connections for Efficient Neural Networks. In: Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1. NIPS’15. Cambridge, MA, USA: MIT Press; 2015. p. 1135–1143.
  18. Processing-in-Memory for Energy-Efficient Neural Network Training: A Heterogeneous Approach. In: 2018 51st Annual IEEE/ACM International Symposium on Microarchitecture (MICRO). Fukuoka City, Japan; 2018. p. 655–668.
  19. Kanerva P. Hyperdimensional Computing: An Introduction to Computing in Distributed Representation with High-Dimensional Random Vectors. Cognitive Computation. 2009;1(2):139–159. doi:10.1007/s12559-009-9009-8.
  20. Toward a formal theory for computing machines made out of whatever physics offers. Nature Communications. 2023;14(1):4911.
  21. Kanerva P. Hyperdimensional Computing: An Algebra for Computing with Vectors. In: Advances in Semiconductor Technologies. John Wiley & Sons, Ltd; 2022. p. 25–42.
  22. A Survey on Hyperdimensional Computing Aka Vector Symbolic Architectures, Part I: Models and Data Transformations. ACM Computing Surveys. 2022;55(6):130:1–130:40. doi:10.1145/3538531.
  23. Smolensky P. Tensor product variable binding and the representation of symbolic structures in connectionist systems. Artificial Intelligence. 1990;46(1-2):159–216.
  24. Plate TA. Holographic reduced representations. IEEE Transactions on Neural networks. 1995;6(3):623–641.
  25. Kanerva P. Binary spatter-coding of ordered k-tuples. In: International Conference on Artificial Neural Networks. Bochum, Germany: Springer; 1996. p. 869–873.
  26. Gayler RW. Multiplicative binding, representation operators & analogy. Advances in Analogy Research: Integration of Theory and Data from the Cognitive, Computational, and Neural Sciences. 1998; p. 1–4.
  27. A Comparison of Vector Symbolic Architectures. Artificial Intelligence Review. 2022;55(6):4523–4555. doi:10.1007/s10462-021-10110-3.
  28. Thomas A, Rosing T. A Theoretical Perspective on Hyperdimensional Computing. Journal of Artificial Intelligence Research. 2021;72:215–249.
  29. Gorban AN, Tyukin IY. Blessing of Dimensionality: Mathematical Foundations of the Statistical Physics of Data. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 2018;376(2118):20170237. doi:10.1098/rsta.2017.0237.
  30. Rachkovskij DA, Kussul EM. Binding and Normalization of Binary Sparse Distributed Representations by Context-Dependent Thinning. Neural Computation. 2001;13(2):411–452. doi:10.1162/089976601300014592.
  31. Why Is Tanimoto Index an Appropriate Choice for Fingerprint-Based Similarity Calculations? Journal of Cheminformatics. 2015;7(20):1–13. doi:10.1186/s13321-015-0069-3.
  32. Sparse Binary Distributed Encoding of Numeric Vectors. Journal of Automation and Information Sciences. 2005;37:47–61. doi:10.1615/JAutomatInfScien.v37.i11.60.
  33. A Computational Theory for Life-Long Learning of Semantics. In: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 10999 LNAI. Springer Verlag; 2018. p. 217–226.
  34. Learning Sensorimotor Control with Neuromorphic Sensors: Toward Hyperdimensional Active Perception. Science Robotics. 2019;4(30). doi:10.1126/scirobotics.aaw6736.
  35. RegHD: Robust and efficient regression in hyper-dimensional learning system. In: 2021 58th ACM/IEEE Design Automation Conference (DAC). IEEE; 2021. p. 7–12.
  36. The Hyperdimensional Transform for Distributional Modelling, Regression and Classification. arXiv preprint. 2023;.
  37. BRIC: Locality-based Encoding for Energy-Efficient Brain-Inspired Hyperdimensional Computing. In: Proceedings of the 56th Annual Design Automation Conference 2019. Las Vegas NV USA: ACM; 2019. p. 1–6.
  38. Johnson WB, Lindenstrauss J. Extensions of Lipschitz Mappings into a Hilbert Space. In: Beals R, Beck A, Bellow A, Hajian A, editors. Contemporary Mathematics. vol. 26. Providence, Rhode Island: American Mathematical Society; 1984. p. 189–206.
  39. Mahoney MMW. Randomized Algorithms for Matrices and Data. Foundations and Trends in Machine Learning. 2011;3(2):123–224. doi:10.1561/2200000035.
  40. Drineas P, Mahoney MW. RandNLA: Randomized Numerical Linear Algebra. Communications of the ACM. 2016;59(6):80–90. doi:10.1145/2842602.
  41. The Hyperdimensional Transform: a Holographic Representation of Functions. arXiv preprint arXiv:231016065. 2023;.
  42. Autoscaling Bloom Filter: Controlling Trade-off between True and False Positives. Neural Computing and Applications. 2020;32(8):3675–3684. doi:10.1007/s00521-019-04397-1.
  43. GrapHD: Graph-Based Hyperdimensional Memorization for Brain-Like Cognitive Learning. Frontiers in Neuroscience. 2022;16:757125.
  44. Rachkovskij DA. Representation of spatial objects by shift-equivariant similarity-preserving hypervectors. Neural Computing and Applications. 2022;34(24):22387–22403.
  45. Yilmaz O. Analogy Making and Logical Inference on Images Using Cellular Automata Based Hyperdimensional Computing. In: Proceedings of the 2015th International Conference on Cognitive Computation: Integrating Neural and Symbolic Approaches - Volume 1583. COCO’15. Aachen, Germany: CEUR-WS.org; 2015. p. 19–27.
  46. Ge L, Parhi KK. Classification Using Hyperdimensional Computing: A Review. IEEE Circuits and Systems Magazine. 2020;20(2):30–47. doi:10.1109/MCAS.2020.2988388.
  47. Computing on functions using randomized vector representations (in brief). In: Proceedings of the 2022 Annual Neuro-Inspired Computational Elements Conference; 2022. p. 115–122.
  48. OnlineHD: Robust, Efficient, and Single-Pass Online Learning Using Hyperdimensional System. In: 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE). Grenoble, France: IEEE; 2021. p. 56–61.
  49. Adapthd: Adaptive efficient training for brain-inspired hyperdimensional computing. In: 2019 IEEE Biomedical Circuits and Systems Conference (BioCAS). Nara, Japan: IEEE; 2019. p. 1–4.
  50. Efficient biosignal processing using hyperdimensional computing: Network templates for combined learning and classification of ExG signals. Proceedings of the IEEE. 2018;107(1):123–143.
  51. Big data in biology: The hope and present-day challenges in it. Gene Reports. 2020;21:100869.
  52. Rapid ex vivo molecular fingerprinting of biofluids using laser-assisted rapid evaporative ionization mass spectrometry. Nature Protocols. 2021;16(9):4327–4354. doi:10.1038/s41596-021-00580-8.
  53. Image-seq: spatially resolved single-cell sequencing guided by in situ and in vivo imaging. Nature Methods. 2022;19(12):1622–1633.
  54. High-throughput imaging flow cytometry by optofluidic time-stretch microscopy. Nature Protocols. 2018;13(7):1603–1631.
  55. Al-Hashimi HM. Turing, von Neumann, and the Computational Architecture of Biological Machines. Proceedings of the National Academy of Sciences. 2023;120(25):e2220022120. doi:10.1073/pnas.2220022120.
  56. SemiHD: Semi-Supervised Learning Using Hyperdimensional Computing. In: 2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD); 2019. p. 1–8.
  57. Accelerating Hyperdimensional Computing on FPGAs by Exploiting Computational Reuse. IEEE Transactions on Computers. 2020;69(8):1159–1171. doi:10.1109/TC.2020.2992662.
  58. Demeter: A Fast and Energy-Efficient Food Profiler Using Hyperdimensional Computing in Memory. IEEE Access. 2022;10:82493–82510. doi:10.1109/ACCESS.2022.3195878.
  59. Improved metagenomic analysis with Kraken 2. Genome Biology. 2019;20:1–13.
  60. MetaCache: context-aware classification of metagenomic reads using minhashing. Bioinformatics. 2017;33(23):3740–3748.
  61. Molnar C. Interpretable Machine Learning: A Guide for Making Black Box Models Explainable. Leanpub; 2019.
  62. Štrumbelj E, Kononenko I. Explaining Prediction Models and Individual Predictions with Feature Contributions. Knowledge and Information Systems. 2014;41(3):647–665. doi:10.1007/s10115-013-0679-x.
  63. Wilstrup C, Kasak J. Symbolic regression outperforms other models for small data sets. arXiv preprint arXiv:210315147. 2021;.
  64. Cranmer M. Interpretable machine learning for science with PySR and SymbolicRegression.jl. arXiv preprint arXiv:230501582. 2023;.
  65. Methods for the Integration of Multi-Omics Data: Mathematical Aspects. BMC Bioinformatics. 2016;17(2):S15. doi:10.1186/s12859-015-0857-9.
  66. Multimodal Deep Learning for Biomedical Data Fusion: A Review. Briefings in Bioinformatics. 2022;23(2):bbab569. doi:10.1093/bib/bbab569.
  67. Hyperdimensional Computing-Based Multimodality Emotion Recognition with Physiological Signals. In: 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS). Hsinchu, Taiwan: IEEE; 2019. p. 137–141.
  68. Attentive Multimodal Learning on Sensor Data Using Hyperdimensional Computing. In: Proceedings of the 22nd International Conference on Information Processing in Sensor Networks. IPSN ’23. New York, NY, USA: Association for Computing Machinery; 2023. p. 312–313.
  69. Neubert P, Schubert S. Hyperdimensional Computing as a Framework for Systematic Aggregation of Image Descriptors; 2021.
  70. A Neuro-Vector-Symbolic Architecture for Solving Raven’s Progressive Matrices. Nature Machine Intelligence. 2023;5(4):363–375. doi:10.1038/s42256-023-00630-8.
  71. A Comprehensive Review on Multiple Instance Learning. Electronics. 2023;12(20):4323. doi:10.3390/electronics12204323.
  72. HDNA: Energy-efficient DNA Sequencing Using Hyperdimensional Computing. In: 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI); 2018. p. 271–274.
  73. GenieHD: Efficient DNA Pattern Matching Accelerator Using Hyperdimensional Computing. In: 2020 Design, Automation & Test in Europe Conference & Exhibition (DATE). Grenoble, France: IEEE; 2020. p. 115–120.
  74. BioHD: An Efficient Genome Sequence Search Platform Using HyperDimensional Memorization. In: Proceedings of the 49th Annual International Symposium on Computer Architecture. New York New York: ACM; 2022. p. 656–669.
  75. HDGIM: Hyperdimensional Genome Sequence Matching on Unreliable highly scaled FeFET. In: 2023 Design, Automation & Test in Europe Conference & Exhibition (DATE); 2023. p. 1–6.
  76. A Brain-Inspired Hyperdimensional Computing Approach for Classifying Massive DNA Methylation Data of Cancer. Algorithms. 2020;13(9):233. doi:10.3390/A13090233.
  77. Rachkovskij DA. Shift-equivariant similarity-preserving hypervector representations of sequences. arXiv preprint arXiv:211215475. 2021;.
  78. HyperSpec: Ultrafast Mass Spectra Clustering in Hyperdimensional Space. Journal of Proteome Research. 2023;22(6):1639–1648. doi:10.1021/acs.jproteome.2c00612.
  79. Intelligent Processing of Proteomics Data to Predict Glioma Sensitivity to Chemotherapy. Cybernetics and Computing (In Russian). 2010;161:90–105.
  80. Massively Parallel Open Modification Spectral Library Searching with Hyperdimensional Computing. In: Proceedings of the International Conference on Parallel Architectures and Compilation Techniques. PACT ’22. New York, NY, USA: Association for Computing Machinery; 2023. p. 536–537.
  81. A Robust and Energy-Efficient Classifier Using Brain-Inspired Hyperdimensional Computing. In: Proceedings of the 2016 International Symposium on Low Power Electronics and Design. San Francisco Airport CA USA: ACM; 2016. p. 64–69.
  82. A highly energy-efficient hyperdimensional computing processor for biosignal classification. IEEE Transactions on Biomedical Circuits and Systems. 2022;16(4):524–534.
  83. A Hyperdimensional Computing Framework for Analysis of Cardiorespiratory Synchronization During Paced Deep Breathing. IEEE Access. 2019;7:34403–34415. doi:10.1109/ACCESS.2019.2904311.
  84. Schindler KA, Rahimi A. A primer on hyperdimensional computing for iEEG seizure detection. Frontiers in Neurology. 2021;12:701791.
  85. A wearable biosensing system with in-sensor adaptive machine learning for hand gesture recognition. Nature Electronics. 2021;4(1):54–63.
  86. An EMG gesture recognition system with flexible high-density sensors and brain-inspired high-dimensional classifier. In: 2018 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE; 2018. p. 1–5.
  87. An ensemble of hyperdimensional classifiers: Hardware-friendly short-latency seizure detection with automatic iEEG electrode selection. IEEE journal of biomedical and health informatics. 2020;25(4):935–946.
  88. Analysis of robust implementation of an EMG pattern recognition based control. In: International Conference on Bio-inspired Systems and Signal Processing. vol. 2. ScitePress; 2014. p. 45–54.
  89. Ge L, Parhi KK. Applicability of hyperdimensional computing to seizure detection. IEEE Open Journal of Circuits and Systems. 2022;3:59–71.
  90. Class-modeling of septic shock with hyperdimensional computing. In: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE; 2021. p. 1653–1659.
  91. EEG-based brain–computer interfaces for communication and rehabilitation of people with motor impairment: a novel approach of the 21st Century. Frontiers in human neuroscience. 2018;12:14.
  92. Eventhd: Robust and efficient hyperdimensional learning with neuromorphic sensor. Frontiers in Neuroscience. 2022;16:1147.
  93. Hyperdimensional biosignal processing: A case study for EMG-based hand gesture recognition. In: 2016 IEEE International Conference on Rebooting Computing (ICRC). IEEE; 2016. p. 1–8.
  94. Hyperdimensional computing encoding for feature selection on the use case of epileptic seizure detection. arXiv preprint arXiv:220507654. 2022;.
  95. Hyperdimensional computing for blind and one-shot classification of EEG error-related potentials. Mobile Networks and Applications. 2020;25:1958–1969.
  96. Hyperdimensional computing with local binary patterns: One-shot learning of seizure onset and identification of ictogenic brain regions using short-time iEEG recordings. IEEE Transactions on Biomedical Engineering. 2019;67(2):601–613.
  97. Hypervector design for efficient hyperdimensional computing on edge devices. arXiv preprint arXiv:210306709. 2021;.
  98. Incremental learning in multiple limb positions for electromyography-based gesture recognition using hyperdimensional computing. 2021;.
  99. Laelaps: An energy-efficient seizure detection algorithm from long-term human iEEG recordings without false alarms. In: 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE; 2019. p. 752–757.
  100. Zhang Z, Parhi KK. Low-complexity seizure prediction from iEEG/sEEG using spectral power and ratios of spectral power. IEEE transactions on biomedical circuits and systems. 2015;10(3):693–706.
  101. Neurally-Inspired Hyperdimensional Classification for Efficient and Robust Biosignal Processing. In: Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design; 2022. p. 1–9.
  102. One-shot learning for iEEG seizure detection using end-to-end binary operations: Local binary patterns with hyperdimensional computing. In: 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS). IEEE; 2018. p. 1–4.
  103. Online learning and classification of EMG-based gestures on a parallel ultra-low power platform using hyperdimensional computing. IEEE transactions on biomedical circuits and systems. 2019;13(3):516–528.
  104. Systematic assessment of hyperdimensional computing for epileptic seizure detection. In: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE; 2021. p. 6361–6367.
  105. Vector-based analysis of the similarity between breathing and heart rate during paced deep breathing. In: 2018 Computing in Cardiology Conference (CinC). vol. 45. IEEE; 2018. p. 1–4.
  106. Hyperrec: Efficient recommender systems with hyperdimensional computing. In: Proceedings of the 26th Asia and South Pacific Design Automation Conference. Tokyo Odaiba Waterfront, Japan; 2021. p. 384–389.
  107. Predicting adverse drug-drug interactions with neural embedding of semantic predications. In: AMIA Annual Symposium Proceedings. American Medical Informatics Association; 2019. p. 992.
  108. Slipchenko SV. Distributed Representations for the Processing of Hierarchically Structured Numerical and Symbolic Information. System Technologies (In Russian). 2005;6:134–141.
  109. MoleHD: Efficient Drug Discovery Using Brain Inspired Hyperdimensional Computing. In: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Las Vegas, NV, USA: IEEE; 2022. p. 390–393.
  110. Detecting COVID-19 Related Pneumonia on CT Scans Using Hyperdimensional Computing. In: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC); 2021. p. 3970–3973.
  111. Many paths lead to discovery: analogical retrieval of cancer therapies. In: Quantum Interaction, QI 2012. Springer; 2012. p. 90–101.
  112. Cohen T, Widdows D. Empirical distributional semantics: methods and biomedical applications. Journal of biomedical informatics. 2009;42(2):390–405.
  113. Discovering discovery patterns with predication-based semantic indexing. Journal of biomedical informatics. 2012;45(6):1049–1065.
  114. Cohen T, Widdows D. Embedding of semantic predications. Journal of biomedical informatics. 2017;68:150–166.
  115. Predicting High-Throughput Screening Results With Scalable Literature-Based Discovery Methods. CPT: pharmacometrics & systems pharmacology. 2014;3(10):1–9.
  116. BioBERT: A Pre-Trained Biomedical Language Representation Model for Biomedical Text Mining. Bioinformatics. 2020;36(4):1234–1240. doi:10.1093/bioinformatics/btz682.
  117. Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing. ACM Transactions on Computing for Healthcare. 2021;3(1):2:1–2:23. doi:10.1145/3458754.
  118. Modality classification of medical images with distributed representations based on cellular automata reservoir computing. In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017). IEEE; 2017. p. 1053–1056.
  119. Billmeyer R, Parhi KK. Biological Gender Classification from fMRI via Hyperdimensional Computing. In: 2021 55th Asilomar Conference on Signals, Systems, and Computers. Pacific Grove, CA, USA: IEEE; 2021. p. 578–582.
  120. Detecting COVID-19 related pneumonia on CT scans using hyperdimensional computing. In: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE; 2021. p. 3970–3973.
  121. Phage Diversity, Genomics and Phylogeny. Nature Reviews Microbiology. 2020;18(3):125–138. doi:10.1038/s41579-019-0311-5.
  122. Purnick PE, Weiss R. The second wave of synthetic biology: from modules to systems. Nature reviews Molecular cell biology. 2009;10(6):410–422.
  123. Vector Symbolic Architectures as a Computing Framework for Emerging Hardware. Proceedings of the IEEE. 2022;110(10):1538–1571. doi:10.1109/JPROC.2022.3209104.
  124. Prospects and Applications of Photonic Neural Networks. Advances in Physics: X. 2022;7(1):1981155. doi:10.1080/23746149.2021.1981155.
  125. Adamatzky A. Reaction-Diffusion Computing. In: Meyers RA, editor. Computational Complexity: Theory, Techniques, and Applications. New York, NY: Springer; 2012. p. 2594–2610.
  126. Leveraging Plant Physiological Dynamics Using Physical Reservoir Computing. Scientific Reports. 2022;12(1):12594. doi:10.1038/s41598-022-16874-0.
  127. Neurocompositional Computing: From the Central Paradox of Cognition to a New Generation of AI Systems. AI Magazine. 2022;43(3):308–322. doi:10.1002/aaai.12065.
  128. Symbolic Representation and Learning with Hyperdimensional Computing. Frontiers in Robotics and AI. 2020;7:63. doi:10.3389/frobt.2020.00063.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (7)
  1. Michiel Stock (10 papers)
  2. Dimitri Boeckaerts (1 paper)
  3. Pieter Dewulf (3 papers)
  4. Steff Taelman (1 paper)
  5. Maxime Van Haeverbeke (1 paper)
  6. Wim Van Criekinge (1 paper)
  7. Bernard De Baets (33 papers)
Citations (1)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets