Research Papers

The research that matters, distilled — search, filter by topic, sort, and group.

107 results

		Topics
Zero-Shot Text-to-Image Generation	OpenAI	Architecture	Feb 24, 2021
Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity	Google	ArchitectureTraining	Jan 11, 2021
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale	Google	Architecture	Oct 22, 2020
Denoising Diffusion Probabilistic Models	UC Berkeley	Architecture	Jun 19, 2020
Language Models are Few-Shot Learners	OpenAI	TrainingEvaluation	May 28, 2020
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks	Meta	Architecture	May 22, 2020
Scaling Laws for Neural Language Models	OpenAI	Training	Jan 23, 2020
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer	Google	Training	Oct 23, 2019
RoBERTa: A Robustly Optimized BERT Pretraining Approach	Meta	Training	Jul 26, 2019
Language Models are Unsupervised Multitask Learners	OpenAI	TrainingEvaluation	Feb 14, 2019
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding	Google	ArchitectureTraining	Oct 11, 2018
Improving Language Understanding by Generative Pre-Training	OpenAI	Training	Jun 11, 2018
Mastering Chess and Shogi by Self-Play with a General RL Algorithm	DeepMind	Reinforcement Learning	Dec 5, 2017
Proximal Policy Optimization Algorithms	OpenAI	Reinforcement Learning	Jul 20, 2017
Attention Is All You Need	Google	Architecture	Jun 12, 2017
Mastering the Game of Go with Deep Neural Networks and Tree Search	DeepMind	Reinforcement Learning	Jan 27, 2016
Deep Residual Learning for Image Recognition	Microsoft	Architecture	Dec 10, 2015
Deep Learning	Nature	Architecture	May 28, 2015
Distilling the Knowledge in a Neural Network	Google	Training	Mar 9, 2015
Batch Normalization: Accelerating Deep Network Training	Google	Training	Feb 11, 2015
Adam: A Method for Stochastic Optimization	University of Toronto	Training	Dec 22, 2014
GloVe: Global Vectors for Word Representation	Stanford	Training	Oct 1, 2014
Sequence to Sequence Learning with Neural Networks	Google	Architecture	Sep 10, 2014
Dropout: A Simple Way to Prevent Neural Networks from Overfitting	University of Toronto	Training	Jun 15, 2014