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.
Machinelearning 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 deeplearning 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 lowenergy 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 braincontrolled 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: Jaron Sanders (Delft University)
 20180920T10:00:00+02:00
 20180920T11:00:00+02:00
ML Seminar: Jaron Sanders (Delft University)
Start: 20180920 10:00:00+02:00 End: 20180920 11:00:00+02:00
We have the pleasure to announce our CWI Machine Learning seminar with Jaron Sanders with the title: Optimal Clustering Algorithms in Block Markov Chains
This paper considers cluster detection in Block Markov Chains (BMCs). These Markov chains are characterized by a block structure in their transition matrix. More precisely, the n possible states are divided into a finite number of K groups or clusters, such that states in the same cluster exhibit the same transition rates to other states. One observes a trajectory of the Markov chain, and the objective is to recover, from this observation only, the (initially unknown) clusters. In this paper we devise a clustering procedure that accurately, efficiently, and provably detects the clusters. We first derive a fundamental informationtheoretical lower bound on the detection error rate satisfied under any clustering algorithm. This bound identifies the parameters of the BMC, and trajectory lengths, for which it is possible to accurately detect the clusters. We next develop two clustering algorithms that can together accurately recover the cluster structure from the shortest possible trajectories, whenever the parameters allow detection. These algorithms thus reach the fundamental detectability limit, and are optimal in that sense.
Members
Associated Members
Publications

Sander Bohté  Bionieuws  020618. (2018). Sander Bohté  Bionieuws  020618.

Grünwald, P.D. (2018). Safe probability. Journal of Statistical Planning and Inference, 195, 47–63. doi:10.1016/j.jspi.2017.09.014

Eén alarmerend bericht, en iedereen heeft het idee: het gaat héél slecht  NEMO Kennislink  26022018. (2018). Eén alarmerend bericht, en iedereen heeft het idee: het gaat héél slecht  NEMO Kennislink  26022018.

Sterkenburg, T.F. (2018, January 18). Universal prediction : a philosophical investigation.

Leitao Rodriguez, A, Oosterlee, C.W, Ortiz Gracia, L, & Bohte, S.M. (2018). On the datadriven COS method. Applied Mathematics and Computation, 317, 68–84. doi:10.1016/j.amc.2017.09.002

van Gerven, M, & Bohte, S.M. (2017). Editorial: Artificial neural networks as models of neural information processing. Frontiers in Computational Neuroscience (Vol. 11). doi:10.3389/fncom.2017.00114

Kaufmann, E, & KoolenWijkstra, W.M. (2017). MonteCarlo tree search by best arm identification. In Advances in Neural Information Processing Systems (pp. 4898–4907).

Science Café over kunstmatige neurale netwerken  Gelderlander ed. Nijmegen  021117. (2017). Science Café over kunstmatige neurale netwerken  Gelderlander ed. Nijmegen  021117.

Sterkenburg, T.F. (2017). A generalized characterization of algorithmic probability. Theory of Computing Systems, 61(4), 1337–1352. doi:10.1007/s0022401797749

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 proofofconcept experiments in learning theory, particularly in Prediction with Expert Advice problems, a core problem in learning theory.
Current projects with external funding

Deep Spiking Vision: Better, Faster, Cheaper (DEVIS)

Efficient Deep Learning Platforms (eDLP)

Enabling Personalized Interventions (EPI)

Safe Bayesian Inference: A Theory of Misspecification based on Statistical Learning (SAFEBAYES)
Related partners

Philips

KPMG

SURFsara B.V.

Universiteit van Amsterdam

Vrije Universiteit Amsterdam