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 builds new supercomputer

CWI has started the construction of a new supercomputer cluster in the beginning of October 2003. The cluster, consisting of 48 dual and quad AMD Opteron systems, is the first quad Opteron cluster in the Benelux. The new supercomputer, funded by the Netherlands Organization for Scientific Research NWO, is expected to be operational in two months.

CWI builds new supercomputer - Read More…

Sander Bohte receives NWO grants

The Netherlands Organization for Scientific Research NWO has granted a VENI subsidy to CWI researcher Sander Bohte. Bohte will use the grant, approved in March 2003, to further his research on spiking neural networks. These types of networks incorporate the latest insights in functional biological neurons. In theory they are much more powerful than traditional artificial neural networks. Bothe's work is aimed at using spiking neurons in large-scale networks that can learn to deal with symbolic structures like grammar in language or compact descriptions of objects in vision.

Sander Bohte receives NWO grants - Read More…

Members

Associated Members

Publications

Software

Current projects with external funding

  • Machine Learning at the Intrinsic Task Pace
  • Deep Spiking Vision: Better, Faster, Cheaper (DEVIS)
  • Safe Bayesian Inference: A Theory of Misspecification based on Statistical Learning (SAFEBAYES)