Neural Style Transfer: a Julia notebook

Written by Cemil Cengiz (@cemilcengiz).

This notebook implements deep CNN based image style transfer algorithm from "Image Style Transfer Using Convolutional Neural Networks" (Gatys et al., CVPR 2016). The proposed technique takes two images as input, i.e. a content image (generally a photograph) and a style image (generally an artwork painting). Then, it produces an output image such that the content(objects in the image) resembles the "content image" whereas the style i.e. the texture is similar to the "style image". In order words, it re-draws the "content image" using the artistic style of the "style image".

The images below show an original photograph followed by two different styles applied by the network.

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Happy birthday Raymond Smullyan

A mathematician friend of mine recently told me of a mathematician friend of his who everyday "takes a nap". Now, I never take naps. But I often fall asleep while reading -- which is very different from deliberately taking a nap! I am far more like my dogs Peekaboo, Peekatoo and Trixie than like my mathematician friend once removed. These dogs never take naps; they merely fall asleep. They fall asleep wherever and whenever they choose (which, incidentally is most of the time!). Thus these dogs are true sages.

I think this is all that Chinese philosophy is really about; the rest is mere elaboration!

Raymond Smullyan, The Tao is Silent (1977)

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A new dataset and model for learning to understand navigational instructions

Ozan Arkan Can, Deniz Yuret (2018). arXiv:1805.07952. (PDF).

Abstract: In this paper, we present a state-of-the-art model and introduce a new dataset for grounded language learning. Our goal is to develop a model that can learn to follow new instructions given prior instruction-perception-action examples. We based our work on the SAIL dataset which consists of navigational instructions and actions in a maze-like environment. The new model we propose achieves the best results to date on the SAIL dataset by using an improved perceptual component that can represent relative positions of objects. We also analyze the problems with the SAIL dataset regarding its size and balance. We argue that performance on a small, fixed-size dataset is no longer a good measure to differentiate state-of-the-art models. We introduce SAILx, a synthetic dataset generator, and perform experiments where the size and balance of the dataset are controlled.

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Tutorial: Deep Learning with Julia/Knet

Tutorial at Qatar Computing Research Institute, May 13, 2018. Thanks to Dr. Sanjay Chawla for the invitation.

• Introductory slides from JuliaCon 2017.
• MNIST notebook: Loading and visualizing the data.
• Linear notebook: Training, evaluating and visualizing a linear model.
• MLP notebook: Multi-layer perceptron, overfitting, regularization, dropout.
• CNN notebook: Convolutional networks.

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Deep Learning in NLP: A Brief History

Panel presentation at the International Symposium on Brain and Cognitive Science (ISBCS 2018)

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