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Found 8 papers, 4 papers with code
Gaussian Process Neurons
We propose a method to learn stochastic activation functions for use in probabilistic neural networks.
Gaussian Process Neurons Learn Stochastic Activation Functions
The proposed model can intrinsically handle uncertainties in its inputs and self-estimate the confidence of its predictions.
Automatic Differentiation for Tensor Algebras
For the function $f_{ij} (x) = x_i^2$, the derivative of the loss is $(dx)_i=\partial l/\partial x_i=\sum_j (df)_{ij}2x_i$; the sum is necessary because index $j$ does not appear in the indices of $f$.
Theano: A Python framework for fast computation of mathematical expressions
Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements.
A Differentiable Transition Between Additive and Multiplicative Neurons
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 neuron should perform.
A Neural Transfer Function for a Smooth and Differentiable Transition Between Additive and Multiplicative Interactions
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 neuron should perform.
On Fast Dropout and its Applicability to Recurrent Networks
Recurrent Neural Networks (RNNs) are rich models for the processing of sequential data.
Unsupervised Feature Learning for low-level Local Image Descriptors
Unsupervised feature learning has shown impressive results for a wide range of input modalities, in particular for object classification tasks in computer vision.
