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.



Sander Bohté, professor by special appointment of Cognitive Neurobiology at UvA

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.

Sander Bohté, professor by special appointment of Cognitive Neurobiology at UvA - Read More…

CWI participates in new NWO Perspectief programme

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 participates in new NWO Perspectief programme - Read More…

Current events

ML Seminar: Rémi Bardenet (CNRS & CRIStAL, Univ. Lille)

  • 2019-10-31T11:00:00+01:00
  • 2019-10-31T12:00:00+01:00
October 31 Thursday

Start: 2019-10-31 11:00:00+01:00 End: 2019-10-31 12:00:00+01:00

Room L016 at CWI, Science Park 123 in Amsterdam

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.


Associated Members



Current projects with external funding

  • Efficient Deep Learning Platforms (eDLP)
  • Enabling Personalized Interventions (EPI)
  • Safe Bayesian Inference: A Theory of Misspecification based on Statistical Learning (SAFEBAYES)
  • Spiking Neural Networks research program

Related partners

  • Philips
  • KPMG
  • SURFsara B.V.
  • Technische Universiteit Eindhoven
  • Universiteit Twente
  • Universiteit van Amsterdam
  • Vrije Universiteit Amsterdam