2021

Vorträge der Abschlussarbeiten & Seminaren, Februar

Audiovisual Data-Driven Android App for Emotion Recognition (Masterarbeit)

Redner: Qiang Chang

Termin: 9. Februar 2021, 10:00

Ort: Online

 

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Implementierung einer Android Applikation für die Klassifikation von Schnarchdaten mittels neuronaler Netze (Bachelorarbeit)

Redner: Igor Tkatschenko

Termin: 9. Februar 2021, 10:00

Ort: Online

 

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Seminararbeiten, 10.02.2021, online

Digital Health

09:30 Marius Pleyer
09:45 Stefan Crummenauer
10:00 Fabian Brain
10:15 Qiang Chang
10:30 Benjamin Jin
10:45 Bernhard Scherer
11:00 Frederic Schulz

 

Computational Intelligence

11:30 Michael Ihrler
11:45 Francois Lux
12:00 Daniel Schubert
12:15 Reinhard Seidl
12:30 Lena Holland
12:45 Sarah Sporck

 

 

 

​​​​​​​Learning with known operators reduces maximum error bounds

Gastredner: Prof. Andreas Maier  

Termin: 12. Januar 2021, 14:00

Ort: Online

 

Zusammenfassung

We describe an approach for incorporating prior knowledge into machine learning algorithms. We aim at applications in physics and signal processing in which we know that certain operations must be embedded into the algorithm. Any operation that allows computation of a gradient or sub-gradient towards its inputs is suited for our framework. We derive a maximal error bound for deep nets that demonstrates that inclusion of prior knowledge results in its reduction. Furthermore, we show experimentally that known operators reduce the number of free parameters. We apply this approach to various tasks ranging from computed tomography image reconstruction over vessel segmentation to the derivation of previously unknown imaging algorithms. As such, the concept is widely applicable for many researchers in physics, imaging and signal processing. We assume that our analysis will support further investigation of known operators in other fields of physics, imaging and signal processing.

 

Artikel: https://www.nature.com/articles/s42256-019-0077-5

 

Biografie

Prof. Dr. Andreas Maier was born on 26th of November 1980 in Erlangen. He studied Computer Science, graduated in 2005, and received his PhD in 2009. From 2005 to 2009 he was working at the Pattern Recognition Lab at the Computer Science Department of the University of Erlangen-Nuremberg. His major research subject was medical signal processing in speech data. In this period, he developed the first online speech intelligibility assessment tool - PEAKS - that has been used to analyze over 4.000 patient and control subjects so far.
From 2009 to 2010, he started working on flat-panel C-arm CT as post-doctoral fellow at the Radiological Sciences Laboratory in the Department of Radiology at the Stanford University. From 2011 to 2012 he joined Siemens Healthcare as innovation project manager and was responsible for reconstruction topics in the Angiography and X-ray business unit. 


In 2012, he returned the University of Erlangen-Nuremberg as head of the Medical Reconstruction Group at the Pattern Recognition lab. In 2015 he became professor and head of the Pattern Recognition Lab. Since 2016, he is member of the steering committee of the European Time Machine Consortium. In 2018, he was awarded an ERC Synergy Grant "4D nanoscope".  Current research interests focuses on medical imaging, image and audio processing, digital humanities, and interpretable machine learning and the use of known operators.

 

 

Vorträge der Abschlussarbeiten, Januar

Deep Learning Annotation Optimisation for Emotion Recognition (Masterarbeit)

Rednerin: Lea Schumann

Termin: 15. Januar 2021, 11:00

Ort: Online

 

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Author-centric Machine Reviewing of Papers for Deep Learning Utilising Natural Language Feature (Masterarbeit)

Redner: Philip Müller

Termin: 15. Januar 2021, 11:00

Ort: Online

 

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Automated Detection and Classification of Airborne Pollen Grains Using Deep Learning (Masterarbeit)

Redner: Jakob Schäfer

Termin: 15. Januar 2021, 11:00

Ort: Online

 

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Towards End-to-End Intrusion Detection Utilising Convolutional Recurrent Neural Networks (Bachelorarbeit)

Redner: Tobias Hallmen

Termin: 15. Januar 2021, 11:00

Ort: Online

 

 

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