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.
News
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.
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.
Current events
ML Seminar: Rémi Bardenet (CNRS & CRIStAL, Univ. Lille)
- 2019-10-31T11:00:00+01:00
- 2019-10-31T12:00:00+01:00
ML Seminar: Rémi Bardenet (CNRS & CRIStAL, Univ. Lille)
Start: 2019-10-31 11:00:00+01:00 End: 2019-10-31 12:00:00+01:00
Everyone is welcome to attend the ML seminar of Rémi Bardenet with the title 'DPPs everywhere: repulsive point processes for Monte Carlo integration, signal processing and machine learning'.
Abstract: Determinantal point processes (DPPs) are specific repulsive point processes, which were introduced in the 1970s by Macchi to model fermion beams in quantum optics. More recently, they have been studied as models and sampling tools by statisticians and machine learners. Important statistical quantities associated to DPPs have geometric and algebraic interpretations, which makes them a fun object to study and a powerful algorithmic building block.
After a quick introduction to determinantal point processes, I will discuss some of our recent statistical applications of DPPs. First, we
used DPPs to sample nodes in numerical integration, resulting in Monte Carlo integration with fast convergence with respect to the number of integrand evaluations. Second, we turned DPPs into low-error variable selection procedures in linear regression. If time allows it, I'll describe a third application where we used DPP machinery to characterize the distribution of the zeros of time-frequency transforms of white noise, a recent challenge in signal processing.
Members
Associated Members
Publications
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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, 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. In Bioinformatics (Vol. 35, pp. i538–i547). doi:10.1093/bioinformatics/btz369
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Een computer kan niet alles leren - NRC- 1 juni 2019. (2019). Een computer kan niet alles leren - NRC- 1 juni 2019.
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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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Kaufmann, E, Koolen-Wijkstra, W.M, & Garivier, A. (2018). Sequential test for the lowest mean: From Thompson to Murphy sampling. In Advances in Neural Information Processing Systems (pp. 6332–6342).
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Karamanis, M, Zambrano, D, & Bohte, S.M. (2018). Continuous-time spike-based reinforcement learning for working memory tasks. In Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence (pp. 250–262). doi:10.1007/978-3-030-01421-6_25
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Dora, S, Pennartz, C, & Bohte, S.M. (2018). A deep predictive coding network for inferring hierarchical causes underlying sensory inputs. In Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence (pp. 457–467). doi:10.1007/978-3-030-01424-7_45
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Pozzi, I, Nusselder, R.B.P, Zambrano, D, Bohte, S.M, & Iliadis, L. (2018). Gating sensory noise in a spiking subtractive LSTM. In V Kůrková, Y Manolopoulos, B Hammer, & I Maglogiannis (Eds.), Proceedings of Artificial Neural Networks and Machine Learning - ICANN 2018 (pp. 284–293). Springer, Cham. doi:10.1007/978-3-030-01418-6_28
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Grünwald, P.D, & de Heide, R. (2018). Invited discussion to the paper Using Stacking to Average Bayesian Predictive Distributions by Yao, Vehtari, Simpson and Gelman. Bayesian Analysis, 13(3), 917–1003.
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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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