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
Breakthrough in energy efficient artificial intelligence
Thanks to a mathematical breakthrough, AI applications can become a hundred to a thousand times more energy efficient. This will make it possible to put much more elaborate AI in chips, enabling applications to run on a smartphone or smartwatch where before this was done in the cloud.
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.
Members
Associated Members
Publications
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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
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Mhammedi, Z, Koolen-Wijkstra, W.M, & van Erven, T.A.L. (2019). Lipschitz Adaptivity with Multiple Learning Rates in Online Learning. In Proceedings of Machine Learning Research (pp. 1–22).
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van Doorn, J, Ly, A, Marsman, M, & Wagenmakers, E.-J. (2019). Bayesian estimation of Kendall's τ using a latent normal approach. Statistics & Probability Letters, 145, 268–272. doi:10.1016/j.spl.2018.10.004
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Zambrano, D, Nusselder, R.B.P, Scholte, H.S, & Bohte, S.M. (2019). Sparse computation in adaptive spiking neural networks. Frontiers in Neuroscience, 12(JAN). doi:10.3389/fnins.2018.00987
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Degenne, R.R.B.P, Koolen-Wijkstra, W.M, & Ménard, P. (2019). Non-Asymptotic Pure Exploration by Solving Games. In Advances in meural information processing systems.
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Mac Giolla, E, & Ly, A. (2019). What to do with all these Bayes factors: How to make Bayesian reports in deception research more informative. Legal and Criminological Psychology. doi:10.1111/lcrp.12162
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Love, J, Selker, R, Marsman, M, Jamil, T, Dropmann, D, Verhagen, J, … Wagenmakers, E.-J. (2019). JASP: Graphical statistical software for common statistical designs. Journal of Statistical Software, 88(1). doi:10.18637/jss.v088.i02
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