6 citations · 16 across the 17 of their papers we have counts for
5 papers · 1 filter
Where to Measure: Epistemic Uncertainty-Based Sensor Placement with ConvCNPs
Feyza Eksen, Stefan Oehmcke, Stefan Lüdtke
Accurate sensor placement is critical for modeling spatio-temporal systems such as environmental and climate processes. Neural Processes (NPs), particularly Convolutional Condition…
Boosted Trees on a Diet: Compact Models for Resource-Constrained Devices
Nina Herrmann, Jan Stenkamp, Benjamin Karic +2
Deploying machine learning models on compute-constrained devices has become a key building block of modern IoT applications. In this work, we present a compression scheme for boost…
Remember to correct the bias when using deep learning for regression!
Christian Igel, Stefan Oehmcke
When training deep learning models for least-squares regression, we cannot expect that the training error residuals of the final model, selected after a fixed training time or base…
Magnitude and Uncertainty Pruning Criterion for Neural Networks
Vinnie Ko, Stefan Oehmcke, Fabian Gieseke
Neural networks have achieved dramatic improvements in recent years and depict the state-of-the-art methods for many real-world tasks nowadays. One drawback is, however, that many…
Input Selection for Bandwidth-Limited Neural Network Inference
Stefan Oehmcke, Fabian Gieseke
Data are often accommodated on centralized storage servers. This is the case, for instance, in remote sensing and astronomy, where projects produce several petabytes of data every…