#neural_network

Using #neural_networks for Geometric Representation
▻https://gpuopen.com/learn/using_neural_networks_for_geometric_representation

AMD GPUOpen - Graphics and game developer resources Explore how Neural Intersection Functions (NIF) and the enhanced #LSNIF are poised to reshape ray tracing by replacing traditional #BVH traversal with efficient, GPU-friendly neural networks for accelerated performance and high-fidelity imagery.

#Article #Drivers #Single_Blog #NIF #Raytracing

Neural Supersampling and #denoising for Real-time #path_tracing
▻https://gpuopen.com/learn/neural_supersampling_and_denoising_for_real-time_path_tracing

AMD GPUOpen - Graphics and game developer resources Read our #Research for a neural denoising and supersampling technique, with the aim of achieving real-time path tracing.

#Article #Single_Blog #hybrid_raytracing #neural_network #Raytracing

Let’s Enhance – free online image upscale and enhancement with neural networks
▻https://letsenhance.io

#jpeg #superrésolution #outil lancé par des Ukrainiens

voir aussi #RAISR de google et d’autres

#neural_networks #photographie

Dreams of imaginary people, by Mike Tyka, 2017
▻http://www.miketyka.com/projects/dreams

#art #étrange #beau

[1703.01041] Large-Scale Evolution of Image Classifiers
▻https://arxiv.org/abs/1703.01041

Neural networks have proven effective at solving difficult problems but designing their architectures can be challenging, even for image classification problems alone. Evolutionary algorithms provide a technique to discover such networks automatically. Despite significant computational requirements, we show that evolving models that rival large, hand-designed architectures is possible today.

#neural_networks #evolution

Deep-Learning Machine Listens to Bach, Then Writes Its Own Music in the Same Style
▻https://www.technologyreview.com/s/603137/deep-learning-machine-listens-to-bach-then-writes-its-own-music-in
▻https://www.youtube.com/watch?v=QiBM7-5hA6o

The #machine-learning technique is straightforward. Hadjeres and Pachet begin by creating a data set to train their #neural_network. They begin with 352 chorales composed by #Bach and then transpose these to other keys that lie within a predefined vocal range, to give a data set of 2,503 chorales. They use 80 percent of these to train their neural network to recognize Bach harmonies and the rest to validate it.

Google’s Multilingual Neural Machine Translation System : Enabling Zero-Shot Translation
▻https://arxiv.org/abs/1611.04558

Un aperçu du fonctionnement de #google_translate (cet article traite du système en production).

La nouveauté #recherche est qu’en mélangeant toutes les langues dans un unique modèle, les résultats sont d’une qualité équivalente tout en utilisant moins de phrases que dans les systèmes où les langues sont uniquement traitées par paires.

Mieux, un système entraîné sur des traductions EN<->PT et ES<->PT saura traduire EN<->ES avec une bonne performance, à la façon du « style transfer » vu sur les peintures.

D’où la question : le modèle ainsi entraîné reconnaît-il des « concepts fondamentaux » du langage ?

We propose a simple, elegant solution to use a single Neural Machine Translation (NMT) model to translate between multiple languages.

#traduction_automatique #machine_learning #neural_network

Researchers from Google develop AI that designs it’s own form of encryption

▻http://www.itwnet.com/news/researchers-google-develop-ai-designs-it%E2%80%99s-own-form-encryption

Researcher from the deep learning project Google Brain are working on artificial intelligence that is capable of creating it’s own form of AI-generated and human-independent encryption. The neural networks can autonomously encrypt and decrypt information.

According to a research paper Martín Abadi and David G. Andersen from Google have designed three neural networks named Alice, Bob and Eve. The researchers assigned each AI a different task. Alice had to send a encrypted message to Bob that only Bob could read. Meanwhile Eve had to figure out how to intercept and decode this message.

The experiment started with a plain-text message that Alice encrypted. Over the course of 15.000 attempts the neural network was able to design it’s own encryption strategy. Bob simultaneously figured out how to decrypt this same message. The message could not be deciphered by Eve.

The paper:
Learning to Protect Communications with Adversarial Neural Cryptography
(Martín Abadi, David G. Andersen (Google Brain))

►https://arxiv.org/pdf/1610.06918v1.pdf

#encryption
#AI #IA
#neural_network

“Daddy’s Car”, la première chanson pop composée par une intelligence artificielle
▻http://www.konbini.com/fr/tendances-2/daddys-car-ia-sony-pop

http://www.konbini.com/fr/files/2016/09/ex-machina-download-wallpapers_0.jpg

Le système FlowMachines de #Sony, une #intelligence_artificielle capable de composer des #chansons “à la manière de”, a dévoilé ses deux premières créations, dont un morceau façon Beatles.

#IA #IA #neural_network #musique

►https://www.youtube.com/watch?v=LSHZ_b05W7o

▻http://zinc.mondediplo.net/messages/38015 via BoOz

Google unleashes deep learning tech on language with Neural Machine Translation | TechCrunch
▻https://techcrunch.com/2016/09/27/google-unleashes-deep-learning-tech-on-language-with-neural-machine-tran

https://tctechcrunch2011.files.wordpress.com/2016/02/shutterstock_181640888.png?w=764&h=400&crop=1

Translating from one language to another is hard, and creating a system that does it automatically is a major challenge, partly because there’s just so many words, phrases, and rules to deal with. Fortunately, neural networks eat big, complicated data sets for breakfast. #Google has been working on a machine learning translation technique for years, and today is its official debut.

The Google Neural Machine Translation system, deployed today for Chinese-English queries, is a step up in complexity from existing methods

#traduction #AI #IA #machine_learning #neural_network

▻http://zinc.mondediplo.net/messages/37993 via BoOz

The neural networks zoo
▻http://www.asimovinstitute.org/neural-network-zoo

http://www.asimovinstitute.org/wp-content/uploads/2016/09/neuralnetworks.png

AI can recognise your face even if you’re pixelated

“Researchers at the University of Texas at Austin and Cornell Tech say that they’ve trained a piece of software that can undermine the privacy benefits of standard content-masking techniques like blurring and pixelation by learning to read or see what’s meant to be hidden in images—anything from a blurred house number to a pixelated human face in the background of a photo.”

▻https://www.wired.com/2016/09/machine-learning-can-identify-pixelated-faces-researchers-show/?mbid=social_fb

The researchers were able to defeat three privacy protection technologies, starting with YouTube’s proprietary blur tool. YouTube allows uploaders to select objects or figures that they want to blur, but the team used their attack to identify obfuscated faces in videos. In another example of their method, the researchers attacked pixelation (also called mosaicing). To generate different levels of pixelation, they used their own implementation of a standard mosaicing technique that the researchers say is found in Photoshop and other commons programs. And finally, they attacked a tool called Privacy Preserving Photo Sharing (P3), which encrypts identifying data in JPEG photos so humans can’t see the overall image, while leaving other data components in the clear so computers can still do things with the files like compress them.

#surveillance
#privacy
#anonymity
#AI #IA #machine_learning #neural_network

Turn your two-bit doodles into fine artworks with deep neural networks, generate seamless textures from photos...
►https://github.com/alexjc/neural-doodle #neural_networks #réseaux_neuronaux #dessin #peinture

https://github.com/alexjc/neural-doodle/raw/master/docs/Portraits_example.jpg

Terrapattern
▻http://www.terrapattern.com

http://www.newyorker.com/wp-content/uploads/2016/05/Terrapattern-5-690.jpg

“similar-image search” for satellite photos. It’s an open-source tool for discovering “patterns of interest” in unlabeled satellite imagery—a prototype for exploring the unmapped, and the unmappable.

(...) Terrapattern is ideal for discovering, locating and labeling typologies that aren’t customarily indicated on maps. These might include ephemeral or temporally-contingent features (such as vehicles or construction sites), or the sorts of banal infrastructure (like fracking wells or smokestacks) that only appear on specialist blueprints, if they appear at all.

(...) the Terrapattern prototype is intended to demonstrate a workflow by which users—such as journalists, citizen scientists, humanitarian agencies, social justice activists, archaeologists, urban planners, and other researchers—can easily search for visually consistent “patterns of interest”. We are particularly keen to help people identify, characterize and track indicators which have not been detected or measured previously, and which have sociological, humanitarian, scientific, or cultural significance.

▻http://www.newyorker.com/tech/elements/meet-terrapattern-google-earths-missing-search-engine

#photographie #satellite #IA #neural_network #moteur_de_recherche #cartographie

DeepOSM - train a deep learning neural network with #Openstreetmap features & orbital imagery
▻https://github.com/trailbehind/DeepOSM #deep_learning #neural_network #satellite #maps #cartographie

Writing with the machine
▻https://www.robinsloan.com/notes/writing-with-the-machine

Finally got this running: snappy in-editor “autocomplete” powered by a neural net trained on old sci-fi stories.

(...)
I’d been reading about #deep_learning for a couple of years, but it wasn’t until a long conversation earlier this year with an old friend (who is eye-poppingly excited about these techniques) that I felt motivated to dig in myself. And, I have to report: it really is a remarkable community at a remarkable moment. Tracking papers on Arxiv, projects on Github, and threads on Twitter, you get the sense of a group of people nearly tripping over themselves to do the next thing — to push the state of the art forward.

That’s all buoyed by a strong (recent?) culture of clear #explanation.

#IA #écrire #neural_networks #machine_learning#science-fiction #archives

alexjc/neural-doodle
►https://github.com/alexjc/neural-doodle

https://github.com/alexjc/neural-doodle/blob/master/docs/Workflow.gif?raw=true

Use a deep neural network to borrow the skills of real artists and turn your two-bit doodles into masterpieces! This project is an implementation of Semantic Style Transfer (Champandard, 2016), based on the Neural Patches algorithm (Li, 2016). Read more about the motivation in this in-depth article and watch this workflow video for inspiration.

chuanli11/CNNMRF: code for paper “Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis”
▻https://github.com/chuanli11/CNNMRF

#ia #generative_art #images #neural_network #machine_learning (ou pas)

Tinker With a Neural Network Right Here in Your Browser.

A #Neural_Network Playground
▻http://playground.tensorflow.org

13 frameworks for mastering Machine Learning

▻http://www.infoworld.com/article/3026262/data-science/13-frameworks-for-mastering-machine-learning.html

Apache Spark MLlib, Apache Singa, Caffe, Microsoft Azure ML Studio, Microsoft Distributed Machine Learning Toolkit, , Microsoft Computational Network Toolkit, Amazon Machine Learning, Google TensorFlow, Veles, Brainstorm, mlpack 2, Marvin.

#machine_learning
#deep_learning
#neural_networks

Composing Music With Recurrent Neural Networks
▻http://www.hexahedria.com/2015/08/03/composing-music-with-recurrent-neural-networks

http://www.hexahedria.com/wp-content/uploads/2015/08/diags-figure8.pdf.png

Watch This Open Source AI Learn to Dominate Super Mario World in Just 24 Hours - Singularity HUB
▻http://singularityhub.com/2015/06/18/watch-this-open-source-ai-learn-to-dominate-super-mario-world-in-jus
▻https://www.youtube.com/watch?v=qv6UVOQ0F44

In a new YouTube video, Seth Bling explains the magic behind software he developed to learn how to play Nintendo’s Super Mario World.

“This program started out knowing absolutely nothing about Super Mario World or Super Nintendos,” Bling says. “In fact, it didn’t even know that pressing ’right’ on the controller would make the player go towards the end of the level.”

le code:
▻http://pastebin.com/ZZmSNaHX

#machine_learning #algorithmes_génétiques #neural_networks #jeux_vidéo

Using #Waifu2x to Upscale Japanese Prints
▻http://ejohn.org/blog/using-waifu2x-to-upscale-japanese-prints

One tool that I came across yesterday is called Waifu2x. It’s a convolutional neural network (CNN) that is designed to optimally “upscale” images (taking small images and generating a larger image). The creator of this tool built it to better upscale poorly-sized Anime images and video. This

▻http://waifu2x.udp.jp

https://dl.dropbox.com/s/wu63a9wvy9hbaki/waifu2x.png?dl=0

#neural_networks #images #outil

à noter @baroug : comme ce programme commence par subir un #apprentissage sur un certain type d’#images, ça marche mieux avec ces images-là (en l’occurrence des images avec des aplats) qu’avec des photos texturées.

Synaptic - The javascript neural network library
▻http://synaptic.juancazala.com/#/self-organizing-map

In this demo, a hopfield network will be fed with patterns (words) and the outputs will be used as a key to store the patterns in a map. The patterns that the network finds similiar will be recognized and grouped under the same key.

Deep Visual-Semantic Alignments for Generating Image Descriptions

Stanford University researchers are using/training improved neural networks to generate descriptions of images. Recognition of objects is nothing new, but those algorithms struggle with descriptions of scenes, and that’s precisely where progress has been made here.

Abstract

We present a model that generates free-form natural language descriptions of image regions*. Our model leverages datasets of images and their sentence descriptions to learn about the inter-modal correspondences between text and visual data. Our approach is based on a novel combination of Convolutional Neural Networks over image regions, bidirectional Recurrent Neural Networks over sentences, and a structured objective that aligns the two modalities through a multimodal embedding. We then describe a Recurrent Neural Network architecture that uses the inferred alignments to learn to generate novel descriptions of image regions. We demonstrate the effectiveness of our alignment model with ranking experiments on Flickr8K, Flickr30K and COCO datasets, where we substantially improve on the state of the art. We then show that the sentences created by our generative model outperform retrieval baselines on the three aforementioned datasets and a new dataset of region-level annotations.

▻http://cs.stanford.edu/people/karpathy/deepimagesent

http://cs.stanford.edu/people/karpathy/deepimagesent/dogball.png http://cs.stanford.edu/people/karpathy/deepimagesent/accordion.png http://cs.stanford.edu/people/karpathy/deepimagesent/tennis.png

#neural_network
#natural_language
#image_recognition
#AI

Google scientist Jeff Dean on how #neural_networks are improving everything #Google does - Puget Sound Business Journal
▻http://www.bizjournals.com/seattle/blog/techflash/2013/08/google-scientist-jeff-dean-on-how.html?page=all

#informatique #intelligence_artificielle #it_has_begun