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 Facebook at StanCon 2018

At Facebook, we rely extensively on statistical methods, e.g., for experimental design and analysis, analysis of time series and other […] Facebook at StanCon 2018 by Kelly Berschauer


 Introducing the CVPR 2018 Learned Image Compression Challenge

  

Posted by Michele Covell, Research Scientist, Google Research Image compression is critical to digital photography — without it, a 12 megapixel image would take 36 megabytes of storage, making most websites prohibitively large. While the signal-processing community has significantly improved imag…


 Facebook partners with the University of Washington to create new AR/VR research center

Today, the University of Washington announced the UW Reality Lab, with Facebook as one of its founding partners.  The lab […] Facebook partners with the University of Washington to create new AR/VR research center by Kelly Berschauer


 TFGAN: A Lightweight Library for Generative Adversarial Networks

     

Posted by Joel Shor, Senior Software Engineer, Machine Perception (Crossposted on the Google Open Source Blog) Training a neural network usually involves defining a loss function, which tells the network how close or far it is from its objective. For example, image classification networks are often…


 Mercedes-Benz Greener Masking Challenge Masking Challenge–1st Place Winner’s Interview

 

To ensure the safety and reliability of each and every unique car configuration before they hit the road, Daimler’s engineers have developed a robust testing system. But, optimizing the speed of their testing system for so many possible feature combinations is complex and time-consuming without a…


 Your Year on Kaggle: Most Memorable Community Stats from 2017

2017 has been an exciting ride for us, and like last year, we’d love to enter the new year sharing and celebrating some of your highlights through stats. There are major machine learning trends, impressive achievements, and fun factoids that all add up to one amazing community. Enjoy! Pu…


 Carvana Image Masking Challenge–1st Place Winner’s Interview

  

This year, Carvana, a successful online used car startup, challenged the Kaggle community to develop an algorithm that automatically removes the photo studio background. This would allow Carvana to superimpose cars on a variety of backgrounds. In this winner’s interview, the first place team…


 Can computers help us synthesize new materials?

Machine-learning system finds patterns in materials “recipes,” even when training data is lacking. Can computers help us synthesize new materials? by Larry Hardesty | MIT News Office


 Evaluation of Speech for the Google Assistant

     

Posted by Enrique Alfonseca, Staff Research Scientist, Google Assistant Voice interactions with technology are becoming a key part of our lives — from asking your phone for traffic conditions to work to using a smart device at home to turn on the lights or play music. The Google Assistant is desi…


 2017: DeepMind’s year in review

2017: DeepMind’s year in review by DeepMind


 Tacotron 2: Generating Human-like Speech from Text

     

Posted by Jonathan Shen and Ruoming Pang, Software Engineers, on behalf of the Google Brain and Machine Perception Teams Generating very natural sounding speech from text (text-to-speech, TTS) has been a research goal for decades. There has been great progress in TTS research over the last few year…


 Collaborating with patients for better outcomes

Collaborating with patients for better outcomes by DeepMind


 Unlocking marine mysteries with artificial intelligence

Students put their AI software for underwater vehicles to the test on the Charles River. Unlocking marine mysteries with artificial intelligence by Mary Beth O’Leary | Department of Mechanical Engineering


 Computer systems predict objects’ responses to physical forces

Results may help explain how humans do the same thing. Computer systems predict objects’ responses to physical forces by Larry Hardesty | MIT News Office


 A Summary of the First Conference on Robot Learning

 

Posted by Vincent Vanhoucke, Principal Scientist, Google Brain Team and Melanie Saldaña, Program Manager, University Relations Whether in the form of autonomous vehicles, home assistants or disaster rescue units, robotic systems of the future will need to be able to operate safely and effectively …


 Building Facebook’s platform for large-scale AR experiences

Facebook is taking another step towards bringing Augmented Reality (AR) into the everyday experiences of people around the globe. Earlier […] Building Facebook’s platform for large-scale AR experiences by Kelly Berschauer


 Introducing Appsperiments: Exploring the Potentials of Mobile Photography

  

Posted by Alex Kauffmann, Interaction Researcher, Google Research Each of the world’s approximately two billion smartphone owners is carrying a camera capable of capturing photos and video of a tonal richness and quality unimaginable even five years ago. Until recently, those cameras behaved …


 Reading a neural network’s mind

Technique illuminates the inner workings of artificial-intelligence systems that process language. Reading a neural network’s mind by Larry Hardesty | MIT News Office


 ONNX V1 released

In September, we released an early version of the Open Neural Network Exchange format (ONNX) with a call to the […] ONNX V1 released by Kelly Berschauer


 Introduction To Neural Networks Part 2 – A Worked Example

 

This tutorial was originally posted here on Ben’s blog, GormAnalysis. The purpose of this article is to hold your hand through the process of designing and training a neural network. Note that this article is Part 2 of Introduction to Neural Networks. R code for this tutorial is provided…


 Block-Sparse GPU Kernels

We’re releasing highly-optimized GPU kernels for an underexplored class of neural network architectures: networks with block-sparse weights. Depending on the chosen sparsity, these kernels can run orders of magnitude faster than cuBLAS or cuSPARSE. We’ve used them to attain state-of-the-art…


 Try this! Researchers devise better recommendation algorithm

Improved recommendation algorithm should work especially well when ratings data are “sparse.” Try this! Researchers devise better recommendation algorithm by Larry Hardesty | MIT News Office


 Building the hardware for the next generation of artificial intelligence

Class taught by Vivienne Sze and Joel Emer brings together traditionally separate disciplines for advances in deep learning. Building the hardware for the next generation of artificial intelligence by Meg Murphy | School of Engineering


 DeepMind papers at NIPS 2017

DeepMind papers at NIPS 2017 by DeepMind


 Facebook showcases latest research at NIPS 2017

4Machine Learning and AI experts from around the world will gather in Long Beach, CA, next week at NIPS 2017 […] Facebook showcases latest research at NIPS 2017 by Kelly Berschauer