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Papers that changed AI, with a plain-language explainer for each.
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- 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.
- 2014
DeepFace: Closing the Gap to Human-Level Performance in Face Verification
CVPR 2014
DeepFace is a 2014 face verification pipeline that rebuilt the alignment and representation stages of the standard face pipeline, reaching 97.35% accuracy on Labeled Faces in the Wild with a nine-layer network of over 120 million parameters.
- 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
Visualizing and Understanding Convolutional Networks
arXiv / ECCV 2014
Zeiler and Fergus ask why large convolutional networks perform so well on ImageNet, introduce a visualization technique for intermediate layers, and use ablation to find an architecture that outperforms Krizhevsky et al. The model also transfers to Caltech-101 and Caltech-256.
- 2013
Rich feature hierarchies for accurate object detection and semantic segmentation
arXiv
R-CNN, posted to arXiv by Girshick, Donahue, Darrell and Malik in November 2013, applied convolutional neural networks to region proposals for object detection and lifted mean average precision on PASCAL VOC 2012 to 53.3%, more than 30% above the previous best[^1].
- 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
Building High-level Features Using Large Scale Unsupervised Learning
ICML
In 2012, Quoc Le, Andrew Ng and colleagues trained a large unsupervised neural network on 10 million internet images and showed it could detect faces without a single labelled face.
- 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.
- 2011
Real-Time Human Pose Recognition in Parts from a Single Depth Image
A CVPR 2011 Best Paper that recovers 3D body joint positions from a single depth image without temporal information, by classifying pixels into body parts, and runs at 200 frames per second on consumer hardware.
- 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
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.
- 2009
Learning Multiple Layers of Features from Tiny Images (CIFAR-10/100)
UT
CIFAR-10 and CIFAR-100 are labeled image datasets drawn from the 80 million tiny images collection, documented in a 2009 technical report by Alex Krizhevsky, Vinod Nair and Geoffrey Hinton. CIFAR-10 holds 60,000 32×32 colour images in 10 classes.
- 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.
- 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
MNIST database of handwritten digits
Proceedings of the IEEE
MNIST is a database of 60,000 training and 10,000 test images of handwritten digits, normalized to 28×28 pixels and described in the 1998 paper by LeCun and colleagues as a shared benchmark for document recognition.
- 1989
Backpropagation Applied to Handwritten Zip Code Recognition
Neural Computation
In 1989 LeCun and colleagues at AT&T Bell Laboratories trained a convolutional network with backpropagation to read handwritten zip-code digits from U.S. mail, reaching 5.0% test error. The work showed that the convolutional design could be learned end to end on a real task.
- 1988
ALVINN: An Autonomous Land Vehicle in a Neural Network
ANIPS11
A 3-layer back-propagation network that takes camera and laser range finder images and outputs the direction to steer, trained on simulated road images and tested on Carnegie Mellon's autonomous navigation test vehicle.