Home›Papers
Papers
Papers
Papers that changed AI, with a plain-language explainer for each.
Total 73 · Clear filters
- 2018
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
arXiv
GLUE is a benchmark and analysis platform that evaluates natural language understanding models across a diverse set of existing tasks, with an online comparison platform and a hand-crafted diagnostic suite for linguistic analysis.
- 2017
Mastering the game of Go without human knowledge
Nature
AlphaGo Zero learned Go only by playing against itself, starting from random play, and beat the earlier champion-defeating AlphaGo 100 games to 0 after three days of training. The Nature paper appeared in October 2017.
- 2017
Proximal Policy Optimization Algorithms
arXiv
A 2017 paper by Schulman, Wolski, Dhariwal, Radford and Klimov that introduced proximal policy optimization (PPO), a reinforcement learning method that is much simpler to implement and tune and became OpenAI's default RL algorithm.
- 2016
Neural Architecture Search with Reinforcement Learning
arXiv
A 2016 paper by Barret Zoph and Quoc V. Le that uses a recurrent network trained with reinforcement learning to design neural network architectures automatically, matching human-designed models on CIFAR-10 and beating LSTM on Penn Treebank.
- 2016
SQuAD: 100,000+ Questions for Machine Comprehension of Text
arXiv
SQuAD is a reading-comprehension dataset of more than 100,000 crowdworker questions on Wikipedia articles, each answered by a span of its passage. Released in 2016, it gave machine comprehension a large, freely available benchmark with a measured human ceiling.
- 2016
Mastering the game of Go with deep neural networks and tree search
Nature
The 2016 Nature paper describing AlphaGo, which combined deep neural networks with tree search. In October 2015 its distributed version beat the European Go champion Fan Hui 5-0, the first time a program defeated a professional player in full-sized Go without handicap.
- 2015
Human-level control through deep reinforcement learning
Nature
A 2015 Nature paper by Mnih, Kavukcuoglu, Silver and colleagues showed that one deep Q-network agent, given only screen pixels and the game score, learned to play 49 Atari 2600 games at a level comparable to a professional human tester.
- 2014
Neural Turing Machines
arXiv
A 2014 paper by Graves, Wayne and Danihelka that couples a neural network to an external memory it reads and writes through attention, making the whole machine differentiable and trainable by gradient descent.
- 2014
GloVe: Global Vectors for Word Representation
EMNLP 2014
GloVe is a 2014 method that learns word vectors by factorising global word co-occurrence counts. Released together with pre-trained vectors, it became, alongside word2vec, one of the two standard ways of producing static word embeddings.
- 2014
Microsoft COCO: Common Objects in Context
arXiv
A 2014 paper introducing Microsoft COCO, a large dataset of everyday scenes in which common objects are labeled with per-instance segmentations, built to study object recognition as part of scene understanding.
- 2013
Playing Atari with Deep Reinforcement Learning
arXiv
A 2013 paper by Mnih, Kavukcuoglu, Silver and colleagues showed a convolutional neural network trained with a Q-learning variant learning to play seven Atari 2600 games directly from raw pixels, beating prior methods on six and human experts on three.
- 2011
Deep Sparse Rectifier Neural Networks
A 2011 paper by Glorot, Bordes and Bengio reporting that deep networks built from rectifying neurons match or beat tanh networks and reach their best performance without unsupervised pre-training on large labelled supervised tasks.
- 2009
ImageNet: A Large-Scale Hierarchical Image Database
CVPR
The 2009 paper introduced ImageNet, a large, cleanly labelled image database organised by the WordNet hierarchy. It later became the basis of an annual benchmark, the ImageNet Large Scale Visual Recognition Challenge.
- 2009
Learning Multiple Layers of Features from Tiny Images
UT
A 2009 technical report by Alex Krizhevsky that introduces CIFAR-10 and CIFAR-100 and studies multi-layer generative models for learning features from tiny colour images.
- 2004
MapReduce: Simplified Data Processing on Large Clusters
OSDI
Jeffrey Dean and Sanjay Ghemawat's 2004 OSDI paper presents MapReduce, a programming model in which users write a map function and a reduce function while the run-time system parallelizes the work across large clusters of commodity machines.
- 2003
Latent Dirichlet Allocation
JMLR
A generative probabilistic model that represents each document as a finite mixture over topics, fitted with variational approximate inference and EM, and evaluated on document modelling, text classification and collaborative filtering.
- 2001
Random Forests
Machine Learning
Leo Breiman's 2001 paper defines random forests as combinations of tree predictors whose random vectors are sampled independently from the same distribution, analyzes generalization error through tree strength and correlation, and reports error rates competitive with Adaboost and more robust to noise.
- 1999
The PageRank Citation Ranking: Bringing Order to the Web
Brin and Page's PageRank technical report treats hyperlinks as citations to rank web pages and was used to build Google's search engine; it is one of the heavily cited papers on their early work.
- 1998
Gradient-Based Learning Applied to Document Recognition
Proc. IEEE
LeCun, Bottou, Bengio and Haffner’s 1998 review argued that learning systems should replace hand-designed features. It showed that convolutional networks such as LeNet-5 outperformed other methods on handwritten digits, and it introduced the MNIST dataset.
- 1998
The Anatomy of a Large-Scale Hypertextual Web Search Engine
A 1998 conference paper by Sergey Brin and Lawrence Page describing PageRank, an algorithm that ranks web pages by the number and quality of links pointing to them, and the search engine built on that algorithm.