27 citations · 59 across the 19 of their papers we have counts for
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Temporal Convolution Derived Multi-Layered Reservoir Computing
Johannes Viehweg, Dominik Walther, Patrick Mäder
The prediction of time series is a challenging task relevant in such diverse applications as analyzing financial data, forecasting flow dynamics or understanding biological process…
Privacy Preserving Federated Learning with Convolutional Variational Bottlenecks
Daniel Scheliga, Patrick Mäder, Marco Seeland
Gradient inversion attacks are an ubiquitous threat in federated learning as they exploit gradient leakage to reconstruct supposedly private training data. Recent work has proposed…
Flipped Classroom: Effective Teaching for Time Series Forecasting
Philipp Teutsch, Patrick Mäder
Sequence-to-sequence models based on LSTM and GRU are a most popular choice for forecasting time series data reaching state-of-the-art performance. Training such models can be deli…
Dropout is NOT All You Need to Prevent Gradient Leakage
Daniel Scheliga, Patrick Mäder, Marco Seeland
Gradient inversion attacks on federated learning systems reconstruct client training data from exchanged gradient information. To defend against such attacks, a variety of defense…