#neural-networks

[gadgets360technews]

This AI is learning how to replicate your childish penis drawings

This AI is learning how to replicate your childish penis drawings

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Bored? You can draw crude dicks like you did in your middle school notebooks, only this time you’ll help feed an AI. 

Reddit user and researcher RichardRNN (get it?) trained a recurrent neural network (which is what RNN stands for) to doodle dicks. Naturally, it’s called DickRNN. The researcher apparently fed the RNN about 10,000 penile doodlesin order to get it up and running, and now…

View On WordPress

[prehensile]

via Pinboard

[prehensile]

via Pinboard

[prehensile]

via Pinboard

[zzkt]

“A frame by frame reconstruction of the film Blade Runner using a type of artificial neural network called an autoencoder.The model is a variational autoencoder trained with a learned similarity metric [Larsen et al. 2015 - arxiv.org/abs/1512.09300] that I implemented in TensorFlow at a resolution of 256x144. It has been trained on every individual frame of Blade Runner for 6 epochs before reconstructing the film.

This video is the outcome from a research project as carried out for my dissertation of the Msci in Creative Computing at Goldsmiths, University of London. 

Read more about the project in my medium piece: medium.com/@Terrybroad/autoencoding-blade-runner-88941213abbe#.ndgb0hpxz

(via  Terence Broad)

[zzkt]

DeepFool: A simple and accurate method to fool deep neural networks

State-of-the-art deep neural networks have achieved impressive results on many image classification tasks. However, these same architectures have been shown to be unstable to small, well sought, perturbations of the images. In this paper, we fill this gap and propose the DeepFool framework to efficiently compute perturbations that fools deep network and thus reliably quantify the robustness of arbitrary classifiers.

via http://gitxiv.com/posts/iZpWrvKdXmtRaQusa/deepfool-a-simple-and-accurate-method-to-fool-deep-neural

[justphilll]

I wish I had more time to look into this in depth.