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
Tenure-track position Bio-inspired information processing with spiking neurons
Centrum Wiskunde & Informatica (CWI) in Amsterdam invites talented researchers to apply for a Tenure-track position in the area of Bio-inspired information processing with spiking neurons.
News
CWI and INRIA use AI to better predict harmful solar storms
Extreme solar storms can have destructive effects on communications and electrical power grids. To improve space weather forecasting systems Mandar Chandorkar combined AI and data from space missions. He defends his thesis on 14 November.
The brain as a computer
On Wednesday 6 November 2019 Sander Bohte (CWI and UvA) will hold his inaugural lecture “the Brain as a computer” as a professor by special appointment of Cognitive Neurobiology.
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.
Members
Associated Members
Publications
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Mücke, N.T, Bohte, S.M, & Oosterlee, C.W. (2021). Reduced order modeling for parameterized time-dependent PDEs using spatially and memory aware deep learning. Journal of Computational Science, 53. doi:10.1016/j.jocs.2021.101408
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Zambrano, D, Roelfsema, P.R, & Bohte, S.M. (2021). Learning continuous-time working memory tasks with on-policy neural reinforcement learning. Neurocomputing. doi:10.1016/j.neucom.2020.11.072
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de Heide, R. (2021, January 26). Bayesian Learning: Challenges, Limitations and Pragmatics.
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Proença, H.M, Grünwald, P.D, Bäck, T, & van Leeuwen, M. (2021). Discovering outstanding subgroup lists for numeric targets using MDL. In ECML PKDD: Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 19–35). doi:10.1007/978-3-030-67658-2_2
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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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Turner, R.J, & Grünwald, P.D. (2020). Safe tests for 2 x 2 contingency tables and the Cochran-Mantel-Haenszel test.
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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
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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Efficient Models of Decision-Making for Asseing Cognitive Processing States (None)
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Perceptive acting under uncertainty: safety solutions for autonomous systems (None)
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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