Description
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.
Vacancies
No vacancies currently.
News
CWI launches first podcast ‘Superscience’
Together with IT professional magazine AG Connect, CWI presents a podcast series in honour of its 75th anniversary: Superscience. In the first episode: Sander Bohté.
Jan Hemelrijk Award for CWI's Rosanne Turner
The Netherlands Society for Statistics and Operations Research (VVSOR) awarded the Jan Hemelrijk Award to PhD student Machine Learning Rosanne Turner.
Bayesian learning from data: challenges, limitations and pragmatics
How do humans and computers learn from data? PhD student Rianne de Heide of CWI’s Machine Learning group explored Bayesian learning in specific.
NWA grant for Sander Bohté
New AI research project focuses on brain-like systems for safer smart vehicles.
Members
Associated Members
Publications
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Yin, B, Guo, Q, Corporaal, H, Corradi, F, & Bohte, S.M. (2022). Attentive Decision-making and Dynamic Resetting of Continual Running SRNNs for End-to-End Streaming Keyword Spotting. In Proceedings of the International Conference on Neuromorphic Systems.
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Turner, R.J, Grünwald, P.D, & Härmä, A. (2022). Safe Sequential Conditional Independence Tests for Discrete Variables. In Proceedings of the Online Conference to Unite Philips AI 2022.
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Turner, R.J, Coenen, F, Roelofs, F, Hagoort, K, Härmä, A, Grünwald, P.D, … Scheepers, F.E. (2022). Information extraction from free text for aiding transdiagnostic psychiatry: constructing NLP pipelines tailored to clinicians’ needs. BMC Psychiatry, 22(407). doi:10.1186/s12888-022-04058-z
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ter Schure, J.A. (2022, April 7). ALL-IN meta-analysis.
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Sörensen, L.K.A, Bohte, S.M, Slagter, H.A, & Scholte, H.S. (2022). Arousal state affects perceptual decision-making by modulating hierarchical sensory processing in a large-scale visual system model. PLoS Computational Biology, 18(4), e1009976.1–e1009976.25. doi:10.1371/journal.pcbi.1009976
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Agrawal, S, Juneja, S, & Koolen-Wijkstra, W.M. (2021). Regret-minimization in risk-averse bandits. In Proceedings of the Indian Control Conference, ICC (pp. 195–200). doi:10.1109/ICC54714.2021.9703134
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Pearson, M.J, Dora, S, Struckmeier, O, Knowles, T.C, Mitchinson, B, Tiwari, K, … Pennartz, C. (2021). Multimodal representation learning for place recognition using deep Hebbian predictive coding. Frontiers in Robotics and AI. doi:10.3389/frobt.2021.732023
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de Heide, R, Cheshire, J, Ménard, P, & Carpentier, A. (2021). Bandits with many optimal arms. In Proceedings NeurIPS (Annual Conference on Neural Information Processing Systems).
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Kaufmann, E, & Koolen-Wijkstra, W.M. (2021). Mixture martingales revisited with applications to sequential tests and confidence intervals. Journal of Machine Learning Research, 22, 1–44.
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Yin, B, Corradi, F, & Bohte, S.M. (2021). Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks. Nature Machine Intelligence, 3(10), 905–913. doi:10.1038/s42256-021-00397-w
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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Efficient Deep Learning Platforms (eDLP)
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Enabling Personalized Interventions (EPI)
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Human Brain Project - SGA3 (HBP-SGA3)
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Efficient Models of Decision-Making for Asseing Cognitive Processing States (None)
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Perceptive acting under uncertainty: safety solutions for autonomous systems (None)
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Safe Bayesian Inference: A Theory of Misspecification based on Statistical Learning (SAFEBAYES)
Related partners
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Katholieke Universiteit Nijmegen
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Philips
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KPMG
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SURFsara B.V.
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Technische Universiteit Eindhoven
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Technische Universiteit Delft
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Universiteit Twente
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Universiteit van Amsterdam
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Vrije Universiteit Amsterdam