44 citations · 52 across the 6 of their papers we have counts for
7 papers
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}…
Theano: A Python framework for fast computation of mathematical expressions
The Theano Development Team, Rami Al-Rfou, Guillaume Alain +110
Theano is a Python library that allows to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Since its introduction, it has bee…
A Differentiable Transition Between Additive and Multiplicative Neurons
Wiebke Köpp, Patrick van der Smagt, Sebastian Urban
Existing approaches to combine both additive and multiplicative neural units either use a fixed assignment of operations or require discrete optimization to determine what function…
A Neural Transfer Function for a Smooth and Differentiable Transition Between Additive and Multiplicative Interactions
Sebastian Urban, Patrick van der Smagt
Existing approaches to combine both additive and multiplicative neural units either use a fixed assignment of operations or require discrete optimization to determine what function…
On Fast Dropout and its Applicability to Recurrent Networks
Justin Bayer, Christian Osendorfer, Daniela Korhammer +3
Recurrent Neural Networks (RNNs) are rich models for the processing of sequential data. Recent work on advancing the state of the art has been focused on the optimization or modell…
Convolutional Neural Networks learn compact local image descriptors
Christian Osendorfer, Justin Bayer, Patrick van der Smagt
A standard deep convolutional neural network paired with a suitable loss function learns compact local image descriptors that perform comparably to state-of-the art approaches.