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
Veni grant for Alexander Ly
The Dutch Research Council (NWO) awarded a Veni grant to Alexander Ly, who will start working at CWI in the run of 2022. Ly received the grant for his proposal “Increasing Scientific Efficiency with Sequential Methods”.
Learning better from data: Rianne de Heide wins prestigious award for her dissertation
How can humans and computers learn from data? In her dissertation Rianne de Heide describes some of these problems and introduces a new way of hypothesis testing. For this she received the Willem R. van Zwet Award.
New statistical method brings studies together at an early stage
To be able to combine global research, PhD student Judith ter Schure of CWI's Machine Learning group developed a new kind of statistics: ALL-IN meta-analysis.
Energy-efficient AI detects heart defects
Mathematical breakthrough by CWI allows neuromorphic chip to recognise heart defects, speech and hand gestures up to a thousand times more energy efficient. Such low-energy AI chips are ideal for wearable and mobile applications.
Current events
Paradiso lecture Sander Bohte
- 2022-06-19T11:00:00+02:00
- 2022-06-19T12:00:00+02:00
Paradiso lecture Sander Bohte
Start: 2022-06-19 11:00:00+02:00 End: 2022-06-19 12:00:00+02:00
Kijken in de ziel van AI - Over het brein als computer
Moderne Artificial Intelligence (AI) is sterk geïnspireerd op het functioneren van het brein. Maar hoe werkt het brein eigenlijk? Of, preciezer, hoe is ons brein in staat om intelligent gedrag te genereren? En wat bedoelen we eigenlijk met ‘intelligent gedrag’? In zijn Paradisolezing gaat Sander in op de vraag hoe de honderden miljarden cellen in ons brein in staat zijn om intelligent gedrag te leren en uit te voeren. Hij legt de overeenkomsten met een computer uit, maar gaat ook in op de verschillen en hoe deze verschillen nieuwe inzichten bieden voor slimmere AI en energiezuinigere computers.
Please note that the Paradiso lectures are in Dutch.
Modern Artificial Intelligence (AI) is heavily inspired by the functioning of the brain. But how does the brain actually work? Or, more precisely, how is our brain able to generate intelligent behavior? And what do we actually mean by "intelligent behavior"? In his Paradiso Lecture, Sander addresses the question of how the hundreds of billions of cells in our brain are able to learn and execute intelligent behavior. He explains the similarities to a computer, but also discusses the differences and how these differences offer new insights for smarter AI and more energy-efficient computers.
Members
Associated Members
Publications
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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
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Hendriksen, A.A, de Heide, R, & Grünwald, P.D. (2021). Optional stopping with Bayes factors: A categorization and extension of folklore results, with an application to invariant situations. Bayesian Analysis, 16(3), 961–989. doi:10.1214/20-BA1234
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Dora, S, Bohte, S.M, & Pennartz, C. (2021). Deep gated Hebbian predictive coding accounts for emergence of complex neural response properties along the visual cortical hierarchy. Frontiers in Computational Neuroscience, 15. doi:10.3389/fncom.2021.666131
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de Graaf, H, ter Schure, J.A, & van Liere, G.A.F.S. (2021). How old are young people when they start having sex? Unravelling the applicability of Cox proportional hazards regression. Journal of Public Health. doi:10.1007/s10389-021-01619-6
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