Home›Papers
Papers
Papers
Papers that changed AI, with a plain-language explainer for each.
Total 53 · Clear filters
- 2020
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
arXiv 2010.11929 (ICLR 2021)
A 2020 paper by Alexey Dosovitskiy and colleagues showing that a pure Transformer applied directly to sequences of image patches — the Vision Transformer, or ViT — matches state-of-the-art convolutional networks on image classification when pre-trained on large amounts of data.
- 2020
Scaling Laws for Neural Language Models
arXiv
A 2020 paper finding that language model loss falls predictably, as a power law, with model size, data and compute. It gave labs a quantitative reason to build much larger models and shaped the scale-up that followed.
- 2019
C4: Colossal Clean Crawled Corpus (T5 paper)
JMLR
A 2019 paper that casts every language task as text-to-text, systematically compares transfer-learning choices, releases the C4 corpus and models, and reports state-of-the-art results on summarization, question answering, and classification.
- 2019
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer (T5)
JMLR
A 2019 Google paper that frames every language task as text in, text out, using one encoder-decoder Transformer and one training setup. It also introduced the C4 dataset, and the released T5 models became common research starting points.
- 2019
Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
arXiv 1909.08053
An NVIDIA paper from 2019 presenting an intra-layer model parallelism method, implemented with a few communication operations in native PyTorch, that trained transformer models of up to 8.3 billion parameters and improved several benchmarks.
- 2019
SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems
arXiv
SuperGLUE is a benchmark for general-purpose language understanding, released in 2019 as a harder successor to GLUE. It bundles eight tasks, a single-number metric, an analysis toolkit and a public leaderboard.
- 2018
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
NAACL
Google's 2018 BERT paper introduced an encoder-only Transformer that reads text in both directions and is pre-trained by hiding words. It set new results on eleven language tasks and made download-and-fine-tune the default workflow.
- 2018
Improving Language Understanding by Generative Pre-Training (GPT)
OpenAI
OpenAI's 2018 paper introducing GPT, a Transformer decoder pre-trained to predict the next token and then fine-tuned for specific tasks. It improved the state of the art on 9 of 12 tasks studied and set the recipe the GPT series followed.
- 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
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.
- 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
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.
- 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
Neural Machine Translation by Jointly Learning to Align and Translate
ICLR
Submitted to arXiv in September 2014 by Dzmitry Bahdanau, Kyunghyun Cho and Yoshua Bengio, this paper let a translation model look back at the whole source sentence while producing each word. This mechanism, called attention, later became a basis for Transformer models.
- 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.
- 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.
- 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.