Titlebook: Artificial Neural Networks in Pattern Recognition; 9th IAPR TC3 Worksho Frank-Peter Schilling,Thilo Stadelmann Conference proceedings 2020

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Deep Learning Methods for Image Guidance in Radiation Therapyes get outperformed by data driven machine learning methods. Especially worth to mention is that this not only holds true in terms of precision and computational performance but also in terms of complexity reduction and maintainability. In this paper we provide an overview about the different stages

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Long Distance Relationships Without Time Travel: Boosting the Performance of a Sparse Predictive Autnce learning task consisting of high-Markov-order sequences of MNIST digits. We find that this model learns these sequences faster and more completely than an LSTM, and offer several possible explanations why the LSTM architecture might struggle with the partially observable sequence structure in th

ELUDE

craven

Pain Intensity Recognition - An Analysis of Short-Time Sequences in a Real-World Scenariotomated pain intensity recognition approach in combination with different sets of short-time sequences. Our outcomes indicate, that in real-world applications, where the detection of pain intensity is based on more than one single short-time sequence, the accuracy values can be significantly improve