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Papers that changed AI, with a plain-language explainer for each.
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- 2017
Attention Is All You Need
NeurIPS
A 2017 paper that introduced the Transformer, a neural network built entirely on attention with no recurrence. It set new machine translation results and became the architecture behind almost every large language model since.
- 2017
Mask R-CNN
arXiv
Mask R-CNN is a 2017 framework for object instance segmentation. It detects objects and produces a high-quality mask for each instance by adding a parallel mask branch to Faster R-CNN.
- 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
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
arXiv
A 2016 paper describing Google's neural machine translation system, GNMT: a deep LSTM encoder-decoder with attention, designed to make end-to-end translation practical by addressing rare words, parallelism and inference cost.
- 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
Identity Mappings in Deep Residual Networks
arXiv
A 2016 analysis of residual networks arguing that with identity skip connections and the activation placed after addition, signals travel directly between blocks; a new residual unit built on this eases training, and a 1001-layer network reaches 4.62% error on CIFAR-10.
- 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
Deep Residual Learning for Image Recognition
CVPR
The 2015 paper introduced residual networks, which add each layer's input back to its output through a shortcut connection. This made networks of over a hundred layers trainable and won first place in several ILSVRC and COCO tasks in 2015.
- 2015
You Only Look Once: Unified, Real-Time Object Detection
arXiv
The 2015 YOLO paper recast object detection as a single neural network's regression over bounding boxes and class probabilities, achieving real-time detection while reducing false positives on empty background.
- 2015
You Only Look Once: Unified, Real-Time Object Detection
arXiv
The 2015 paper introduced YOLO, which treats object detection as a single regression from image pixels to bounding boxes and class probabilities. One neural network makes predictions in one evaluation, reaching real-time speed.
- 2015
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
arXiv
A 2015 paper that introduced a Region Proposal Network sharing full-image convolutional features with the detection network, making proposals nearly cost-free and enabling end-to-end training, 5 fps GPU detection, and state-of-the-art accuracy.
- 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.
- 2015
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
arXiv
A 2015 paper by Sergey Ioffe and Christian Szegedy that proposed normalising each layer's inputs per training mini-batch, which allowed higher learning rates, looser initialisation and much faster training of deep networks.
- 2015
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
arXiv
A 2015 arXiv paper by Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho and co-authors that introduced an image-captioning model with visual attention, able to show where it is looking while writing each word.
- 2015
Deep learning
Nature
A 2015 Nature review by Yann LeCun, Yoshua Bengio and Geoffrey Hinton explaining deep learning: models of multiple processing layers that learn layered representations of data, and the results this produced in speech, vision and other fields.
- 2014
Adam: A Method for Stochastic Optimization
arXiv (ICLR 2015)
Adam is a stochastic optimisation algorithm from a 2014 paper by Diederik P. Kingma and Jimmy Ba that sets per-parameter step sizes from running estimates of the gradient's first and second moments, needing little memory and little hyper-parameter tuning.
- 2014
Deep Speech: Scaling up end-to-end speech recognition
arXiv
A 2014 paper presenting an end-to-end deep-learning speech recognition system that removes hand-engineered noise modules and the phoneme dictionary, relying instead on recurrent networks trained across multiple GPUs and synthesized data.
- 2014
Show and Tell: A Neural Image Caption Generator
arXiv
A 2014 paper by Vinyals and colleagues that trained a single deep network to turn a photograph into a sentence, reporting a BLEU-1 score of 59 on Pascal against 25 for the previous state of the art.
- 2014
Conditional Generative Adversarial Nets
arXiv
A 2014 paper by Mehdi Mirza and Simon Osindero that adds conditioning data to both the generator and the discriminator, enabling a GAN to generate samples such as MNIST digits conditioned on class labels.
- 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.