Description
Leader of the group Machine Learning: Peter Grünwald.
Our research group focuses on how computer programs can learn from and understand data, and then make useful predictions based on it. These algorithms integrate insights from various fields, including statistics, artificial intelligence and neuroscience.
Machine-learning applications are increasingly part of every aspect of life, from speech recognition on cell phones to illness prediction in healthcare. One common problem is extremely polluted data, for which no single model can provide adequate explanations. At CWI we address this issue with statistical machine learning based on combining predictions from different models and experts in order to achieve reliable conclusions.
We also study how networks of neurons in the brain process information, and how modern deep-learning methods can benefit from neuroscience. We develop novel neural networks, like Deep Adaptive Spiking Neural Networks, and also theoretical models of neural learning and information processing in biology. Applications of our work range from low-energy consumption neural machine learning to neuroprosthetics, to increased insight into the question of how the brain works.
Vacancies
No vacancies currently.
News
Sander Bohté and Kees Oosterlee awarded with NWO Indo-Dutch funding
Sander Bohté (Machine Learning) and Kees Oosterlee (Scientific Computing) have been awarded with funding from NWO’s Indo-Dutch joint research programme for ICT.
Sander Bohté, professor by special appointment of Cognitive Neurobiology at UvA
Sander Bohté (1974) has been named professor by special appointment of Cognitive Neurobiology, specialising in Computational Neuroscience, at the University of Amsterdam (UvA)'s Faculty of Science. The chair was designated on behalf of the Science Plus Foundation (Stichting Bèta Plus). He will be combining his professorship with his position as senior researcher at CWI's Machine Learning group.
Sterkenburg argues universal method of prediction is impossible
Is it possible to formulate a universal method of prediction? PhD candidate Tom Sterkenburg of CWI’s Machine Learning group argues in his thesis that it is not. He will defend his thesis on 18 January 2018.
CWI participates in new NWO Perspectief programme
In the coming years almost a hundred researchers are going to develop innovative technologies together with industry and social organisations. That will happen in six new Perspectief programmes, which have been given the green light by NWO, Netherlands Organisation for Scientific Research, on 21 November 2017. CWI's Machine Learning group participates in the programme Efficient Deep Learning Systems.
Members
Associated Members
Publications
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Yin, B, Corradi, F, & Bohte, S.M. (2020). Effective and Efficient Computation with Multiple-timescale Spiking Recurrent Neural Networks. In ICONS 2020L International Conference on Neuromorphic Systems 2020 (pp. 1–8). doi:10.1145/3407197.3407225
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Hagenaars, J.J, Paredes-Vallés, F, Bohte, S.M, & De Croon, G.C.H.E. (Guido C. H. E.). (2020). Evolved Neuromorphic Control for High Speed Divergence-Based Landings of MAVs. IEEE Robotics and Automation Letters, 5(4), 6239–6246. doi:10.1109/LRA.2020.3012129
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Seijdel, N, Tsakmakidis, N, de Haan, E.H.F, Bohte, S.M, & Scholte, H.S. (2020). Depth in convolutional neural networks solves scene segmentation. PLoS Computational Biology, 16(7). doi:10.1371/journal.pcbi.1008022
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Calonne, N, Richter, B, Löwe, H, Cetti, C, ter Schure, J.A, Van Herwijnen, A, … Schneebeli, M. (2020). The RHOSSA campaign: Multi-resolution monitoring of the seasonal evolution of the structure and mechanical stability of an alpine snowpack. The Cryosphere, 14(6), 1829–1848. doi:10.5194/tc-14-1829-2020
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Chandorkar, M.H. (2019, November 14). Machine learning in space weather : forecasting, identification and uncertainty quantification.
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Borovykh, A.I, Oosterlee, C.W, & Bohte, S.M. (2019). Generalization in fully-connected neural networks for time series forecasting. Journal of Computational Science, 36(101020), 1–15. doi:10.1016/j.jocs.2019.07.007
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Yin, B, Balvert, M, van der Spek, R.A.A, Dutilh, B.E, Bohte, S.M, Veldink, J, & Schönhuth, A. (2019). Using the structure of genome data in the design of deep neural networks for predicting amyotrophic lateral sclerosis from genotype. Bioinformatics, 35(14), i538–i547. doi:10.1093/bioinformatics/btz369
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Mhammedi, Z, Koolen-Wijkstra, W.M, & van Erven, T.A.L. (2019). Lipschitz Adaptivity with Multiple Learning Rates in Online Learning. In Proceedings of Machine Learning Research (pp. 1–22).
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van Doorn, J, Ly, A, Marsman, M, & Wagenmakers, E.-J. (2019). Bayesian estimation of Kendall's τ using a latent normal approach. Statistics & Probability Letters, 145, 268–272. doi:10.1016/j.spl.2018.10.004
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Zambrano, D, Nusselder, R.B.P, Scholte, H.S, & Bohte, S.M. (2019). Sparse computation in adaptive spiking neural networks. Frontiers in Neuroscience, 12(JAN). doi:10.3389/fnins.2018.00987
Software
Squint: Experimenting in Prediction with Expert Advice problems
Squint provides a codebase to perform numerical proof-of-concept experiments in learning theory, particularly in Prediction with Expert Advice problems, a core problem in learning theory.
Current projects with external funding
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Efficient Deep Learning Platforms (eDLP)
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Enabling Personalized Interventions (EPI)
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Human Brain Project - SGA3 (HBP-SGA3)
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Safe Bayesian Inference: A Theory of Misspecification based on Statistical Learning (SAFEBAYES)
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Spiking Neural Networks research program
Related partners
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Philips
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KPMG
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SURFsara B.V.
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Technische Universiteit Eindhoven
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Universiteit Twente
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Universiteit van Amsterdam
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Vrije Universiteit Amsterdam