Neural-style, or Neural-Transfer, allows reproducing a given image with a new artistic style. Here I introduce the Neural-Style algorithm proposed by Leon A. Gatys, Alexander S. Ecker and Matthias Bethge. The algorithm receives a style-image, a content-image and an input image, which can be either an empty white image or a copy of a content-image. Thus, it changes the input image to make it resemble the content of the latter one and the style of the former one. Before I start I would like to thank Alexis Jacq for his article. Although the name might make you think that it is indeed transferring the style into another image, the idea is to generate an image that has the minimal distance between both content and style images.

Neuromorphic hardware architectures represent a growing family of potential post-Moore's Law Era platforms. Largely due to event-driving processing inspired by the human brain, these computer platforms can offer significant energy benefits compared to traditional von Neumann processors. Unfortunately there still remains considerable difficulty in successfully programming, configuring and deploying neuromorphic systems. We present the Fugu framework as an answer to this need. Rather than necessitating a developer attain intricate knowledge of how to program and exploit spiking neural dynamics to utilize the potential benefits of neuromorphic computing, Fugu is designed to provide a higher level abstraction as a hardware-independent mechanism for linking a variety of scalable spiking neural algorithms from a variety of sources. Individual kernels linked together provide sophisticated processing through compositionality. Fugu is intended to be suitable for a wide-range of neuromorphic applications, including machine learning, scientific computing, and more brain-inspired neural algorithms. Ultimately, we hope the community adopts this and other open standardization attempts allowing for free exchange and easy implementations of the ever-growing list of spiking neural algorithms.

The impending demise of Moore's Law has begun to broadly impact the computing research community.38 Moore's Law has driven the computing industry for many decades, with nearly every aspect of society benefiting from the advance of improved computing processors, sensors, and controllers. Behind these products has been a considerable research industry, with billions of dollars invested in fields ranging from computer science to electrical engineering. Fundamentally, however, the exponential growth in computing described by Moore's Law was driven by advances in materials science.30,37 From the start, the power of the computer has been limited by the density of transistors. Progressive advances in how to manipulate silicon through advancing lithography methods and new design tools have kept advancing computing in spite of perceived limitations of the dominant fabrication processes of the time.37 There is strong evidence that this time is indeed different, and Moore's Law is soon to be over for good.3,38 Already, Dennard scaling, Moore's Law's lesser known but equally important parallel, appears to have ended.11 Dennard's scaling refers to the property that the reduction of transistor size came with an equivalent reduction of required power.8

In this post, we will learn how to train a style transfer network with Paperspace's Gradient and use the model in ml5.js to create an interactive style transfer mirror. This post is the second on a series of blog posts dedicated to train machine learning models in Paperspace and then use them in ml5.js. You can read the first post in this series on how to train a LSTM network to generate text here. Style Transfer is the technique of recomposing images in the style of other images.1 It first appeared in September 2015, when Gatys et.

Src: github There are dependencies like openCV and Apache Spark but they are optional. I used openCV to perform feature extraction by HOG which speeds up the learning process, apache spark to compare results. Utils classes support computing additional data like confusion matrix and add methods to play with some well known datasets like iris or mnist. As it doesn't require any external libraries maybe someone will find it helpful when studying basics of machine learning.

Similarity search--for example, identifying similar images in a database or similar documents on the web--is a fundamental computing problem faced by large-scale information retrieval systems. We discovered that the fruit fly olfactory circuit solves this problem with a variant of a computer science algorithm (called locality-sensitive hashing). The fly circuit assigns similar neural activity patterns to similar odors, so that behaviors learned from one odor can be applied when a similar odor is experienced. The fly algorithm, however, uses three computational strategies that depart from traditional approaches. These strategies can be translated to improve the performance of computational similarity searches.

"Sandia National Laboratories researchers are drawing inspiration from neurons in the brain, such as these green fluorescent protein-labeled neurons in a mouse neocortex, with the aim of developing neuro-inspired computing systems to reboot computing. "Summary: Researchers explore neural computing to extend Moore's Law. Sandia explores neural computing to extend Moore's Law. Computation is stuck in a rut. The integrated circuits that powered the past 50 years of technological revolution are reaching their physical limits.

Which authors of this paper are endorsers? Disable MathJax (What is MathJax?)

IMAGE: Sandia National Laboratories researchers are drawing inspiration from neurons in the brain, such as these green fluorescent protein-labeled neurons in a mouse neocortex, with the aim of developing neuro-inspired computing... view more ALBUQUERQUE, N.M. - Computation is stuck in a rut. The integrated circuits that powered the past 50 years of technological revolution are reaching their physical limits. This predicament has computer scientists scrambling for new ideas: new devices built using novel physics, new ways of organizing units within computers and even algorithms that use new or existing systems more efficiently. To help coordinate new ideas, Sandia National Laboratories has assisted organizing the Institute of Electrical and Electronics Engineers (IEEE) International Conference on Rebooting Computing held Oct. 17-19. "We're taking a stab at the scope of what neural algorithms can do. We're not trying to be exhaustive, but rather we're trying to highlight the kind of application over which algorithms may be impactful," said Brad Aimone, a computational neuroscientist and co-author of one paper.

While it has taken over 50000 pounds and a large collection of artists to hand-paint each frame of a the Van Gogh movie: Loving Vincent, recent advances in neural algorithms artistic style (Gatys et al.) allow one to capture his art style on a computer. Although the work of Gatys et al. worked well on images, the naive method for extending it to movies does not produce great results. The video below shows a new method of rendering movies in a given art style using optical flow to move the textures with the objects in the scene. A technical report is available here: https://arxiv.org/abs/1605.08153. Now that the code is written, the movie is generated with little human input.