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On 30 November 2020 DeepMind announced that the organisers of CASP14 had recognised its AlphaFold system as a solution to the 50-year-old protein-folding problem, with accuracy approaching that of experimental methods.
In December 2018 DeepMind's AlphaFold placed first overall at CASP13, a blind protein-structure prediction assessment, giving the best prediction for 25 of the 43 hardest targets.
The 2015 ImageNet classification contest was won by ResNet, a network up to 152 layers deep whose layers learn residual functions relative to their inputs; it reached 3.57% error on the test set and, by one account, exceeded human performance.
At ILSVRC 2014, GoogLeNet won the classification track with 6.67% top-5 error, while VGG's network won localisation and took second in classification; more than 50 institutions took part.
In 2013 the ILSVRC classification task was won by Clarifai with an ensemble of convolutional networks at 11.7% top-5 error, while OverFeat won localisation. Zeiler and Fergus's visualization paper, also reporting ImageNet results, appeared the same month.
In October 2012 AlexNet, a deep neural network built by a University of Toronto team, won the ImageNet image-classification challenge by a wide margin. Its result showed that large neural networks trained on GPUs could outperform hand-designed vision methods.
A Kaggle competition run in August–October 2012 in which competitors predicted molecules' biological activities from numerical descriptors of their chemical structures; a deep-learning team led by Geoffrey Hinton took first place.
On 13 April 2010, Fei-Fei Li announced the first ImageNet Large Scale Visual Recognition Challenge, a 1,000-category image categorization benchmark. It ran for eight editions, from 2010 to 2017.
At ICDAR 2009, Alex Graves won three connected handwriting competitions — French, Farsi and Arabic — with LSTM networks trained using Connectionist Temporal Classification, the first time a recurrent neural network won an international contest.
An open competition run by Netflix from 2006 to 2009 that offered $1,000,000 to any team able to improve its movie recommendation algorithm by 10%, and released 100 million anonymous ratings for contestants to work with.
