_Objective:_ Design Feed-Forward Neural Network (fully connected) that can be trained even with very deep architectures.

*   _Dataset:_ [MNIST](yann.lecun.com/exdb/mnist/), [CIFAR10](https://www.cs.toronto.edu/%7Ekriz/cifar.html), [Tox21](https://tripod.nih.gov/tox21/challenge/) and [UCI tasks](https://archive.ics.uci.edu/ml/datasets/optical+recognition+of+handwritten+digits).
*   _Code:_ [here](https://github.com/bioinf-jku/SNNs)

## Inner-workings:

They introduce a new activation functio the Scaled Exponential Linear Unit (SELU) which has the nice property of making neuron activations converge to a fixed point with zero-mean and unit-variance.  
They also demonstrate that upper and lower bounds and the variance and mean for very mild conditions which basically means that there will be no exploding or vanishing gradients.

The activation function is:  
[![screen shot 2017-06-14 at 11 38 27 am](https://user-images.githubusercontent.com/17261080/27125901-1a4f7276-50f6-11e7-857d-ebad1ac94789.png)](https://user-images.githubusercontent.com/17261080/27125901-1a4f7276-50f6-11e7-857d-ebad1ac94789.png)  
With specific parameters for alpha and lambda to ensure the previous properties. The tensorflow impementation is:

    def selu(x):
        alpha = 1.6732632423543772848170429916717
        scale = 1.0507009873554804934193349852946
        return scale*np.where(x>=0.0, x, alpha*np.exp(x)-alpha)
    

They also introduce a new dropout (alpha-dropout) to compensate for the fact that [![screen shot 2017-06-14 at 11 44 42 am](https://user-images.githubusercontent.com/17261080/27126174-e67d212c-50f6-11e7-8952-acad98b850be.png)](https://user-images.githubusercontent.com/17261080/27126174-e67d212c-50f6-11e7-8952-acad98b850be.png)

## Results:

Batch norm becomes obsolete and they are also able to train deeper architectures. This becomes a good choice to replace shallow architectures where random forest or SVM used to be the best results. They outperform most other techniques on small datasets.  
[![screen shot 2017-06-14 at 11 36 30 am](https://user-images.githubusercontent.com/17261080/27125798-bd04c256-50f5-11e7-8a74-b3b6a3fe82ee.png)](https://user-images.githubusercontent.com/17261080/27125798-bd04c256-50f5-11e7-8a74-b3b6a3fe82ee.png)

Might become a new standard for fully-connected activations in the future.