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Papers that changed AI, with a plain-language explainer for each.
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- 2017
Progressive Growing of GANs for Improved Quality, Stability, and Variation
arXiv
A 2017 paper by Karras, Aila, Laine and Lehtinen that trains generative adversarial networks by growing the generator and discriminator from low to high resolution, improving stability, quality and variation.
- 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
Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks
arXiv
CycleGAN learns image-to-image translation between two domains without aligned pairs, combining an adversarial loss with a cycle-consistency constraint to constrain the mapping.
- 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
WaveNet: A Generative Model for Raw Audio
arXiv
WaveNet is a deep neural network that generates raw audio waveforms one sample at a time, predicting each sample from all the ones before it. Used for text-to-speech, it was rated by listeners as more natural than the systems it was compared with.
- 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
Generative Adversarial Nets
arXiv
Goodfellow, Bengio, and co-authors proposed a generative-model framework with adversarially trained generator and discriminator.
- 2013
Auto-Encoding Variational Bayes
arXiv
A 2013 paper by Diederik P. Kingma and Max Welling that introduced a stochastic variational inference and learning algorithm for directed latent-variable models with intractable posteriors, making approximate inference practical on large datasets.
- 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.
- 2012
Large Scale Distributed Deep Networks
NeurIPS
A 2012 NIPS paper by Jeffrey Dean, Andrew Ng and colleagues that introduced DistBelief, a framework for training deep networks across clusters of thousands of machines, together with the Downpour SGD and Sandblaster optimisation procedures.
- 2012
ImageNet Classification with Deep Convolutional Neural Networks
NeurIPS
The 2012 NeurIPS paper that introduced AlexNet, a large convolutional network trained on GPUs. It won the ILSVRC-2012 image classification competition by a wide margin and is widely seen as a turning point for deep learning in computer vision.
- 2010
Phone recognition with the mean-covariance restricted Boltzmann Machine
NIPS
A 2010 NIPS paper by Dahl, Ranzato, Mohamed and Hinton applies Deep Belief Nets and a mean-covariance RBM to acoustic modeling, reaching 20.5% phone error on speaker-independent TIMIT phone recognition.
- 2009
Large-scale Deep Unsupervised Learning using Graphics Processors
ICML
A 2009 ICML paper by Rajat Raina, Anand Madhavan and Andrew Y. Ng that used graphics processors to train deep belief networks and sparse coding models many times faster than multicore CPUs.
- 2006
A fast learning algorithm for deep belief nets
Neural Computation
A 2006 paper by Hinton, Osindero and Teh that uses complementary priors and a greedy layer-by-layer algorithm to train deep belief nets, then fine-tunes them; the resulting three-hidden-layer model both modelled and classified handwritten digits well.
- 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.
- 1992
Practical issues in temporal difference learning (TD-Gammon)
Machine Learning
Gerald Tesauro's 1992 paper tested whether temporal difference learning could master a complex real-world task, using self-play backgammon as the case study.
- 1988
Learning to predict by the methods of temporal differences
Machine Learning
Richard Sutton's 1988 paper introduced temporal-difference learning, a family of incremental prediction methods that assign credit from changes between successive predictions and are shown to be more economical and accurate than conventional supervised approaches on most prediction problems.
- 1985
A Learning Algorithm for Boltzmann Machines
Cognitive Science 9
A 1985 Cognitive Science paper by Ackley, Hinton and Sejnowski that presents a general learning rule for connectionist networks and reports simple examples of efficient internal representations.