Uni Tübingen
A Blog by Machine Learning Cluster

Latest Research

Spotlighting publications from our Cluster
July 25, 2022 Eric Raidl, Sebastian Bordt, Michèle Finck, Ulrike von Luxburg

Artificial Intelligence (AI) – Should it explain itself?

We are no longer baffled by all the tasks algorithms can perform. And apparently, they are now even able to ‘explain’ their output. But is that something we really want?
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July 15, 2022 Matthias Karlbauer, Timothy Praditia, Sebastian Otte, Sergey Oladyshkin, Wolfgang Nowak, Martin V. Butz

Fusing Physical Knowledge with Neural Networks’ Flexibility

Diffusion processes in nature are highly complex, and scientists strive to understand them in detail. With a new physics-aware neural network, we were able to model and predict such processes much more precisely than previously possible.
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February 21, 2022 Robert Geirhos

Do machines see like humans? They are getting closer

Machines may drive you to work one day, but they currently still fail when faced with unusual situations or noisy data. That’s because machines see the world very differently from humans - but this gap is starting to narrow.
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January 21, 2022 Linda Behringer, Maximilian Dax, Elke Müller

Machine Learning Decodes Tremors of the Universe

Researchers train a neural network to estimate – in just a few seconds – the precise characteristics of merging black holes based on their gravitational-wave emissions. The network determines the masses and spins of the black holes, where in the sky, at what angle, and how far away from Earth the merger took place.
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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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