Description
Leader of the group Machine Learning: Peter Grunwald.
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, 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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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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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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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.), 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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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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Grünwald, P.D. (2018). Safe probability. Journal of Statistical Planning and Inference, 195, 47–63. doi:10.1016/j.jspi.2017.09.014
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Sterkenburg, T.F. (2018, January 18). Universal prediction : a philosophical investigation.
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Leitao Rodriguez, A, Oosterlee, C.W, Ortiz Gracia, L, & Bohte, S.M. (2018). On the data-driven COS method. Applied Mathematics and Computation, 317, 68–84. doi:10.1016/j.amc.2017.09.002
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Kaufmann, E, & Koolen-Wijkstra, W.M. (2017). Monte-Carlo tree search by best arm identification. In Advances in Neural Information Processing Systems (pp. 4898–4907).
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Sterkenburg, T.F. (2017). A generalized characterization of algorithmic probability. Theory of Computing Systems, 61(4), 1337–1352. doi:10.1007/s00224-017-9774-9
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Borovykh, A, Bohte, S.M, & Oosterlee, C.W. (2017). Conditional time series forecasting with convolutional neural networks. In Lecture Notes in Computer Science/Lecture Notes in Artificial Intelligence (pp. 729–730).
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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Spiking Neural Networks research program
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Deep Spiking Vision: Better, Faster, Cheaper (DEVIS)
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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)
Related partners
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Huawei
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Philips
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KPMG
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SURFsara B.V.
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Universiteit van Amsterdam
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Vrije Universiteit Amsterdam