604 citations · 688 across the 28 of their papers we have counts for
3 papers · 1 filter
Gaussian Process Neurons Learn Stochastic Activation Functions
Sebastian Urban, Marcus Basalla, Patrick van der Smagt
We propose stochastic, non-parametric activation functions that are fully learnable and individual to each neuron. Complexity and the risk of overfitting are controlled by placing…
Automatic Differentiation for Tensor Algebras
Sebastian Urban, Patrick van der Smagt
Kjolstad et. al. proposed a tensor algebra compiler. It takes expressions that define a tensor element-wise, such as $f_{ij}(a,b,c,d) = \exp\left[-\sum_{k=0}^4 \left((a_{ik}+b_{jk}…
Unsupervised Real-Time Control through Variational Empowerment
Maximilian Karl, Maximilian Soelch, Philip Becker-Ehmck +3
We introduce a methodology for efficiently computing a lower bound to empowerment, allowing it to be used as an unsupervised cost function for policy learning in real-time control.…