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
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.
Current events
ML Seminar: Tristan Cossio (Netflix)
- 2018-07-19T11:00:00+02:00
- 2018-07-19T12:00:00+02:00
ML Seminar: Tristan Cossio (Netflix)
Start: 2018-07-19 11:00:00+02:00 End: 2018-07-19 12:00:00+02:00
With pleasure we announce this seminar: Data Science at Netflix
Data science at Netflix is much more than just personalized recommendations (e.g. the 2006-2009 Netflix Prize) - it is also a
critical aspect of streaming, quality control, and content development and buying decisions. This talk surveys the breadth of data science work at Netflix, with a specific focus on how the Content Science & Algorithms team (based in Hollywood CA) uses machine learning for valuation and programming of Licensed & Original content.
- Web
- Email: Wouter Koolen
- Phone: 020-592 4009
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iCal
Members
Associated Members
Publications
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De wiskunde van het insectenarmageddon - NEMO Kennislink - 20-02-18. (2018). De wiskunde van het insectenarmageddon - NEMO Kennislink - 20-02-18.
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Hoogleraar statistiek: "Thuiswedstrijden Feyenoord geen reden tot argwaan' - ElfVoetbal.nl - 06-02-18. (2018). Hoogleraar statistiek: "Thuiswedstrijden Feyenoord geen reden tot argwaan' - ElfVoetbal.nl - 06-02-18.
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Kirichenko, A, & van Zanten, J.H. (2018). Minimax lower bounds for function estimation on graphs. Electronic Journal of Statistics, 12(1), 651–666. doi:10.1214/18-EJS1407
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van der Pas, S.L, & Grünwald, P.D. (2018). Almost the best of three worlds: Risk, consistency and optional stopping for the switch criterion in nested model selection. Statistica Sinica, 28(1), 229–253. doi:10.5705/ss.202016.0011
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Kotłowski, W, Koolen-Wijkstra, W.M, & Malek, A. (2017). Random permutation online isotonic regression. In Advances in Neural Information Processing Systems 30 (NIPS 2017) (pp. 4181–4190).
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Grünwald, P.D, & Mehta, N.A. (2017). A tight excess risk bound via a unified PAC-Bayesian-Rademacher-Shtarkov-MDL complexity.
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Zambrano, D, Nusselder, R.B.P, Scholte, H.S, & Bohte, S.M. (2017). Efficient computation in adaptive artificial spiking neural networks. arXiv.org e-Print archive.
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Lewis, N, & Grünwald, P.D. (2017). Objectively combining AR5 instrumental period and paleoclimate climate sensitivity evidence. Climate Dynamics. doi:10.1007/s00382-017-3744-4
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Kaufmann, E, & Koolen-Wijkstra, W.M. (2017). Monte-Carlo Tree Search by Best Arm Identification.
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Razendsnelle ontwikkelingen in machinelearning - PCM - 30-04-2017. (2017). Razendsnelle ontwikkelingen in machinelearning - PCM - 30-04-2017.
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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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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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