Uni Tübingen
A Blog by Machine Learning Cluster

Latest Research

Spotlighting publications from our Cluster
October 6, 2021 Philipp Berens, Dmitry Kobak

First comprehensive atlas of neuron types in the brain

With hundreds of scientists, we have explored the properties of different neuron types in mice, monkeys and humans using novel experimental techniques and machine learning methods for data analysis. The result is a unique overview of the motor cortex in the brain and its evolution.
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September 7, 2021 Artur Speiser

Machine Learning Improves
Super-resolution Microscopy

Single-molecule localization microscopy is a powerful method to image cellular structures with nanometer resolution. We developed DECODE, a deep learning based analysis algorithm that makes this technique faster and more precise.
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July 19, 2021 Michael Deistler, Jonathan Oesterle

Identifying Models in
Neuroscience with Machine Learning

Computer models are a great tool to analyze neuronal mechanisms in the brain, but tuning these models to match brain activity has long been a daunting task for scientists. We developed a new machine learning tool that automates this process and used it to develop a simulation environment for a retinal implant.
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July 18, 2021 Tobias Rentschler , Ulrike Werban , Sandra Teuber, Karsten Schmidt , Thomas Scholten

Using Machine Learning for 3D Soil Mapping

Spatial soil variability makes a farmer's daily business challenging as it leads to varying growth conditions for field crops. Machine learning can help to map soil properties so that farmers can adapt fertilizing and irrigation management in a time- and cost-efficient way.
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July 18, 2021 Agustinus Kristiadi , Philipp Hennig

Painless Uncertainty for Deep Learning

The Bayesian formalism can add uncertainty to deep neural networks. But Bayesian deep learning has a reputation as cumbersome and expensive. No longer. Recent results show how to achieve calibrated uncertainty in deep networks efficiently, without affecting their predictive performance.
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