
ResourceEfficient Neural Networks for Embedded Systems
While machine learning is traditionally a resource intensive task, embed...
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Efficient and Robust Machine Learning for RealWorld Systems
While machine learning is traditionally a resource intensive task, embed...
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Complex Signal Denoising and Interference Mitigation for Automotive Radar Using Convolutional Neural Networks
Driver assistance systems as well as autonomous cars have to rely on sen...
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Safe SemiSupervised Learning of SumProduct Networks
In several domains obtaining class annotations is expensive while at the...
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On the Latent Variable Interpretation in SumProduct Networks
One of the central themes in SumProduct networks (SPNs) is the interpre...
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Fixed Points of Belief Propagation  An Analysis via Polynomial Homotopy Continuation
Belief propagation (BP) is an iterative method to perform approximate in...
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Exact Maximum Margin Structure Learning of Bayesian Networks
Recently, there has been much interest in finding globally optimal Bayes...
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SumProduct Networks for Sequence Labeling
We consider higherorder linearchain conditional random fields (HOLCC...
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Automatic Clustering of a Network Protocol with WeaklySupervised Clustering
Abstraction is a fundamental part when learning behavioral models of sys...
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Learning Deep Mixtures of Gaussian Process Experts Using SumProduct Networks
While Gaussian processes (GPs) are the method of choice for regression t...
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SelfGuided Belief Propagation  A Homotopy Continuation Method
We propose selfguided belief propagation (SBP) that modifies belief pro...
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Sound event detection using weaklylabeled semisupervised data with GCRNNS, VAT and SelfAdaptive Label Refinement
In this paper, we present a gated convolutional recurrent neural network...
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Optimisation of Overparametrized SumProduct Networks
It seems to be a pearl of conventional wisdom that parameter learning in...
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Bayesian Learning of SumProduct Networks
Sumproduct networks (SPNs) are flexible density estimators and have rec...
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Parameterized Structured Pruning for Deep Neural Networks
As a result of the growing size of Deep Neural Networks (DNNs), the gap ...
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Learning a Behavior Model of Hybrid Systems Through Combining ModelBased Testing and Machine Learning (Full Version)
Models play an essential role in the design process of cyberphysical sy...
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Deep Structured Mixtures of Gaussian Processes
Gaussian Processes (GPs) are powerful nonparametric Bayesian regression...
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Franz Pernkopf
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