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.

 

 

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ML Seminar: Jaron Sanders (Delft University)

  • 2018-09-20T10:00:00+02:00
  • 2018-09-20T11:00:00+02:00
September 20 Thursday

Start: 2018-09-20 10:00:00+02:00 End: 2018-09-20 11:00:00+02:00

CWI, Lecture room L016

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 information-theoretical 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.

 

 

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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