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 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.
Early genetic code very resistant to mutation
Researchers of Centrum Wiskunde & Informatica (CWI) in Amsterdam show that the genetic code is remarkably resistant to DNA replication errors. This might explain the success of the common ancestor of all life, who 3,5 billion years ago developed the genetic code that resides in every organism.
Members
Associated Members
Publications
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Mhammedi, Z, Grünwald, P.D, & Guedj, B. (2019). PAC-Bayes Unexpected Bernstein Inequality. In Proceedings NeurIPS (Annual Conference on Neural Information Processing Systems).
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Grünwald, P.D, & Roos, T. (2019). Minimum Description Length Revisited. Mathematics for industry.
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van Ommen, M, Koolen-Wijkstra, W.M, & Grünwald, P.D. (2019). Efficient algorithms for minimax decisions under tree-structured incompleteness. In Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence (pp. 336–347). doi:10.1007/978-3-030-29765-7_28
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ter Schure, J.A, & Grünwald, P.D. (2019). Accumulation Bias in meta-analysis: the need to consider time in error control [version 1; peer review: two approved]. F1000Research, 8. doi:10.12688/f1000research.19375.1
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Grünwald, P.D, & Mehta, N.A. (2019). A Tight Excess Risk Bound via a Unified PAC-Bayesian-Rademacher-Shtarkov-MDL Complexity. In Proceedings ALT (Algorithmic Learning Theory).
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Liu, S, Oosterlee, C.W, & Bohte, S.M. (2019). Pricing options and computing implied volatilities using neural networks. Risks, 7(1). doi:10.3390/risks7010016
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Wagenmakers, E.-J, Love, J, Marsman, M, Jamil, T, Ly, A, Verhagen, J, … Morey, R.D. (2019). Bayesian inference for psychology. Part II: Example applications with JASP. Psychonomic Bulletin & Review, 25(1), 58–76. doi:10.3758/s13423-017-1323-7
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Borovykh, A.I, Bohte, S.M, & Oosterlee, C.W. (2019). Dilated convolutional neural networks for time series forecasting. Journal of Computational Finance, 73–101. doi:10.21314/JCF.2019.358
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ter Schure, J.A. (2019). Efficient accumulation of scientific knowledge, research waste and accumulation bias. ERCIM News, 116, 8–9.
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Degenne, R.R.B.P, & Koolen-Wijkstra, W.M. (2019). Pure Exploration with Multiple Correct Answers. In Advances in meural information processing systems.
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