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
Post-doc position in the area of Spiking Neural Networks
The Machine Learning group at the Centrum Wiskunde & Informatica (CWI) in Amsterdam invites applicants for a Post-doc position (2 or 3 years) in the area of Spiking Neural Networks. We are looking for a motivated postdoctoral researcher with expertise in the area of spiking neural networks and an interest in probabilistic computing.
News
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.
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).
Members
Associated Members
Publications
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Yin, B, Scholte, H.S, & Bohte, S.M. (2021). LocalNorm: Robust image classification through dynamically regularized normalization. In Proceedings of the International Conference of Artificial Neural Networks (pp. 240–252).
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Sterkenburg, T.F, & Grünwald, P.D. (2021). The no-free-lunch theorems of supervised learning. Synthese. doi:10.1007/s11229-021-03233-1
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Sterkenburg, T.F, & de Heide, R. (2021). On the truth-convergence of open-minded Bayesianism. The Review of Symbolic Logic, 1–37. doi:http://dx.doi:10.1017/S1755020321000022
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Kruijne, W, Bohte, S.M, Roelfsema, P.R, & Olivers, C.N.L. (2021). Flexible working memory through selective gating and attentional tagging. Neural Computation, 33(1), 1–40. doi:10.1162/neco_a_01339
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van Gendt, M.J, Siebrecht, M, Briaire, J.J, Bohte, S.M, & Frijns, J.H.M. (2020). Short and long-term adaptation in the auditory nerve stimulated with high-rate electrical pulse trains are better described by a power law. Hearing Research, 398. doi:10.1016/j.heares.2020.108090
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Degenne, R.R.B.P, Shao, H, & Koolen-Wijkstra, W.M. (2020). Structure adaptive algorithms for stochastic bandits. In Proceedings of the International Conference on Machine Learning (pp. 2443–2452).
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Mhammedi, Z, & Koolen-Wijkstra, W.M. (2020). Lipschitz and comparator-norm adaptivity in online learning. In Proceedings of Machine Learning Research (pp. 2858–2887).
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Turner, R.J. (2020). Safe Statistics for Means and Proportions.
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Grünwald, P.D, & Mehta, N.A. (2020). Fast rates for general unbounded loss functions: From ERM to generalized bayes. Journal of Machine Learning Research, 21, 1–80.
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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).
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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Bayesian Best-Arm Identification: optimal algortihms for sequential decision making (None)
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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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INRIA
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Katholieke Universiteit Nijmegen
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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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Technische Universiteit Delft
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Universiteit Twente
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Universiteit van Amsterdam
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Vrije Universiteit Amsterdam