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
CWI researchers selected as ACM Future of Computing Academy members
CWI researchers Tim Baarslag and Wouter Koolen have been selected as members of the of the ACM Future of Computing Academy (FCA).
NWO TOP grant for Peter Grünwald
The Netherlands Organisation for Scientific Research (NWO) has awarded a Physical Sciences TOP grant 1 for curiosity driven research to Peter Grünwald of CWI.
ERCIM News 107 on Machine Learning co-coordinated by Sander Bohte - extra Open Access section
In October 2016, ERCIM News No. 107 was published: http://ercim-news.ercim.eu/en107. It features a special theme on current trends and new paradigms in Machine Learning, coordinated by the guest editors Sander Bohte (CWI ) and Hung Son Nguyen (University of Warsaw).
Bayesian statistics not as robust as commonly thought
The widely used method of Bayesian statistics is not as robust as commonly thought. Researcher Thijs van Ommen of Centrum Wiskunde & Informatica (CWI) discovered that for certain types of problems, Bayesian statistics finds non-existing patterns in the data. Van Ommen defends his thesis on this topic on Wednesday 10 June at Leiden University.
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