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
Researchers develop neural model for working memory
Neuroscientists of Centrum Wiskunde & Informatica (CWI) and the Netherlands Institute for Neuroscience (NIN) have developed a biologically plausible neural network model that can learn to remember past events in order to use them in the future. The researchers developed their model by combining theoretical principles from machine learning with insights from neuroscience.
CWI starts research on spiking neural networks
Sander Bohte, researcher in the life science group of the Centrum Wiskunde & Informatica (CWI) in Amsterdam, starts in collaboration with researchers from the University of Amsterdam (UvA) a new project on spiking neural networks.
Brain mechanisms better understood with new model
Building a neural network with the same properties and capacity as the human brain is the holy grail in neuroinformatics. Such a network would not only explain the inner workings of the brain, but would also pave the road for brain-controlled machines such as computers operated by thought and robot limbs for the handicapped.
CWI simulates brain activity on video cards
Neuroinformaticists of Centrum Wiskunde & Informatica (CWI) in Amsterdam managed to simulate complex brain activity on simple video cards. The simulated brain contains 50,000 neurons communicating with 35 million signals per second. This is comparable to the brain capacity of insects such as ants or flies.
Members
Associated Members
Publications
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de Heide, R, Kirichenko, A.A, Mehta, N.A, & Grünwald, P.D. (2020). Safe-Bayesian Generalized Linear Regression. In Proceedings of the International Conference on Artificial Intelligence and Statistics (pp. 2623–2633).
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Pozzi, I, Bohte, S.M, & Roelfsema, P.R. (2020). Attention-Gated Brain Propagation: How the brain can implement reward-based error backpropagation. Advances in Neural Information Processing Systems.
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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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van Erven, T.A.L, van der Hoeven, D, Kotlowski, W.T, & Koolen-Wijkstra, W.M. (2020). Open problem: Fast and optimal online portfolio selection. In Proceedings of Machine Learning Research (pp. 3864–3869).
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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
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