DeepMind Papers @ NIPS (Part 1)

Over the next three blogposts, we’re going to share with you brief descriptions of the papers we are presenting at the NIPS 2016 Conference in Barcelona.Read More

Related Google News:

The 2012 FTC staff papers March 16, 2021
Machine learning patterns with Apache Beam and the Dataflow Runner, part I December 4, 2020
Forbes embraces MongoDB on Google Cloud as part of digital-first strategy November 10, 2020
An Analysis of Online Datasets Using Dataset Search (Published, in Part, as a Dataset) August 25, 2020
Google Summer of Code 2020 Statistics: Part 2 August 13, 2020
Google Summer of Code 2020 Statistics: Part 1 June 12, 2020
Part 2: Fast, scalable and accurate NLP: Why TFX is a perfect match for deploying BERT June 8, 2020
From unlikely start-up to major scientific organisation: Entering our tenth year at DeepMind December 5, 2019

December 2, 2016

AI / Google

Google AIFollow

Google AI@GoogleAI·

14 Apr

Today, in partnership with @StanfordSVL, we are proud to announce the iGibson Challenge on Interactive and Social Navigation for simulating robot navigation in cluttered and busy environments. Grab the dataset and submit your model solution by May 31st. https://goo.gle/2PW79pg

Reply on Twitter 1382386298774691840 Retweet on Twitter 1382386298774691840113 Like on Twitter 1382386298774691840439 Twitter 1382386298774691840

Google AI@GoogleAI·

9 Apr

Check out Monster Mash, an open source sketch-based tool that allows experts and amateurs alike to draw soft, dynamic 3D characters and animate them in minutes. Learn more, grab the code, and try out the demo. https://goo.gle/2Qf4iar

Reply on Twitter 1380601584409796610 Retweet on Twitter 1380601584409796610414 Like on Twitter 13806015844097966101842 Twitter 1380601584409796610

Google AI@GoogleAI·

7 Apr

Last year we launched the Research Scholar Program to strengthen long-term collaboration with the academic community. Today we are pleased to announce the inaugural award recipients, representing 15+ countries and 50+ universities. See the full list ↓ https://goo.gle/3uBmbzn

Reply on Twitter 1379900099430658063 Retweet on Twitter 137990009943065806360 Like on Twitter 1379900099430658063234 Twitter 1379900099430658063

Google AI@GoogleAI·

25 Mar

Many Transformer #NLP models have computational requirements that scale quadratically with input sequence length. Learn how sparse attention methods reduce the dependency from a quadratic to linear and achieve state-of-the-art results on challenging tasks. https://goo.gle/2PxgxPe

Reply on Twitter 1375180886656700416 Retweet on Twitter 1375180886656700416122 Like on Twitter 1375180886656700416525 Twitter 1375180886656700416

Google AI@GoogleAI·

24 Mar

Predicting the function of proteins using #neuralnetworks can greatly aid medical research. We are proud to collaborate with @emblebi to extend the @PfamDB database of functionally annotated proteins by 10% as part of the v34.0 release. Learn more below! https://xfam.wordpress.com/2021/03/24/google-research-team-bring-deep-learning-to-pfam/

Reply on Twitter 1374829385325236226 Retweet on Twitter 1374829385325236226188 Like on Twitter 1374829385325236226541 Twitter 1374829385325236226

Google AI@GoogleAI·

24 Mar

Introducing a new #ReinforcementLearning algorithm for teaching agents how to solve tasks by providing only examples of success, avoiding the need for hand-crafted reward functions while outperforming prior imitation learning approaches. Read more →https://goo.gle/3vVksX1

Reply on Twitter 1374815192475832320 Retweet on Twitter 1374815192475832320346 Like on Twitter 13748151924758323201301 Twitter 1374815192475832320

Google AI@GoogleAI·

23 Mar

Open-domain long-form question answering can provide a testbed to measure the factuality of generative text models in #NLP. Check out a new system that leverages both sparse attention and retrieval-based models to achieve state-of-the-art results. https://goo.gle/3cYRBs9

Reply on Twitter 1374421286655430663 Retweet on Twitter 137442128665543066392 Like on Twitter 1374421286655430663274 Twitter 1374421286655430663

Google AI@GoogleAI·

19 Mar

Today we present an approach for iterative digital game design that leverages #MachineLearning to train agents as play-testers, allowing designers to efficiently balance a game and align it with their original vision. Learn more at http://goo.gle/3rZyggK

Reply on Twitter 1372987074547052548 Retweet on Twitter 1372987074547052548173 Like on Twitter 1372987074547052548511 Twitter 1372987074547052548

Google AI@GoogleAI·

18 Mar

This week @TensorFlow Quantum (#TFQ), the open-source library for the rapid prototyping of quantum #MachineLearning models, celebrates its one year anniversary. Learn more about the original release at http://goo.gle/2Q34oPM, and key results from the past year in the post below!

TensorFlow@TensorFlow

⚡ It's been a year since we released TensorFlow Quantum. In this post, we’ll take a look back at the results, papers, and next steps.

Learn more → http://goo.gle/3vDVdbB

Reply on Twitter 1372655403352301568 Retweet on Twitter 137265540335230156886 Like on Twitter 1372655403352301568273 Twitter 1372655403352301568

Google AI@GoogleAI·

18 Mar

Developing efficient algorithms for distributed computation on very large graphs is an ongoing challenge. Check out a new model that augments the capabilities of #MapReduce, providing a way to solve distributed graph algorithms in fewer computation rounds. https://goo.gle/3rWn2K4

Reply on Twitter 1372600569764876290 Retweet on Twitter 1372600569764876290194 Like on Twitter 1372600569764876290676 Twitter 1372600569764876290