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Models Released in 2019
Browse every generative AI model released in 2019 — large language models, image generators, code models and more — ordered by release date. Compare their specs and rankings on our benchmarks page, or put any two side by side with compare. See also models from 2018 and models from 2020.
Graph-based Semi-Supervised Learning (GSSL) Model on MNIST
🇺🇸 West Virginia University
RNN + char4-MS-vec
🇯🇵 NTT Communication Science Laboratories
RNN + char3-MS-vec
🇯🇵 NTT Communication Science Laboratories
RNN Baseline
🇺🇸 Massachusetts Institute of Technology (MIT)
R-Transformer
🇺🇸 Michigan State University
Pluribus
🇺🇸 Facebook AI Research
All-attention network + adaptive span
🇺🇸 Facebook AI Research
RoBERTa Base
RoBERTa Large
Tensorized Transformer (257M)
🇺🇸 Tianjin University
Tensorized Transformer (OBW)
🇺🇸 Tianjin University
Tensorized Transformer (PTB)
🇺🇸 Tianjin University
Tensorized Transformer (W103)
🇺🇸 Tianjin University
Tensorized Transformer (large PTB)
🇺🇸 Tianjin University
TAPE Transformer
🇺🇸 University of California (UC) Berkeley
Walking Minotaur robot
🇺🇸 University of California (UC) Berkeley
LaNet-L (CIFAR-10)
🇺🇸 Brown University
PG-SWGAN
🇨🇭 ETH Zurich
SAGAN
🇺🇸 Rutgers University
Char-CNN-BiLSTM
🇺🇸 Capital One
RNN + char2-MS-vec
🇯🇵 NTT Communication Science Laboratories
4 layer QRNN + dynamic evaluation
🇺🇸 University of Texas at Austin
AWD-LSTM + MoS + Partial Shuffled
🇺🇸 University of Texas at Austin
AdvSoft + 4 layer QRNN + dynamic evaluation (WT103)
🇺🇸 University of Texas at Austin
Adversarial + AWD-LSTM-MoS + partial shuffled
🇺🇸 University of Texas at Austin
AWD-LSTM + Phrase Induction + finetuning (PTB)
🇺🇸 Massachusetts Institute of Technology (MIT)
Transformer-XL Large + Phrase Induction
🇺🇸 Massachusetts Institute of Technology (MIT)
VQ-VAE-2 (FFHQ)
VQ-VAE-2 (ImageNet)
XLNet
🇺🇸 Carnegie Mellon University (CMU)
DLRM-2020
🇺🇸 Facebook AI
Grover-Mega
🇺🇸 University of Washington
MnasNet-A1 + SSDLite
MnasNet-A3
EfficientNet-B1
RSM
Cerenaut
Flow++ (CIFAR10)
🇺🇸 University of California (UC) Berkeley
AWD-LSTM-DRILL + dynamic evaluation† (PTB)
🇨🇭 IDIAP
AWD-LSTM-DRILL + dynamic evaluation† (WT2)
🇨🇭 IDIAP
RaptorX-Contact
🇺🇸 Toyota Technological Institute at Chicago
Neuro-Symbolic Concept Learner
🇺🇸 Massachusetts Institute of Technology (MIT)
MuseNet
🇺🇸 OpenAI
Sparse Transformer (CIFAR10)
🇺🇸 OpenAI
Sparse Transformer (Enwik8)
🇺🇸 OpenAI
Sparse Transformer (ImageNet)
🇺🇸 OpenAI
DANet
🇨🇳 Chinese Academy of Sciences
BERT-Large-CAS (PTB+WT2+WT103)
🇺🇸 Amazon
BERT-Large-CAS (WT103)
🇺🇸 Amazon
BERT-Large-CAS (WT2)
🇺🇸 Amazon
LTM
🇦🇺 Murdoch University
SpecAugment
🇺🇸 Google Brain
Transformer-XL + RMS dynamic eval
🇺🇸 University of Edinburgh
MEGNet (crystal band gap model)
🇺🇸 University of California San Diego
MEGNet (crystal elasticity model)
🇺🇸 University of California San Diego
MEGNet (crystal formation energy model)
🇺🇸 University of California San Diego
MEGNet (molecule model)
🇺🇸 University of California San Diego
True-Regularization+Finetune+Dynamic-Eval
🇨🇳 Mobvoi
WeNet (PTB)
🇺🇸 Amazon
WeNet (Penn Treebank)
🇺🇸 Amazon
Cross-lingual alignment
🇺🇸 Tel Aviv University
2019 was another fast-moving year for generative AI. The models listed above span large language models (LLMs), image and video generators, code models and more, each with its own parameter count, context window, licensing and availability. To understand how the models of 2019 actually perform, compare their scores on our benchmarks page, and use compare to evaluate any two releases side by side.
Tracking AI by release year makes it easy to see how the frontier moves. Browse AI models from 2018 or AI models from 2020 to compare how capabilities, context windows and open-weights availability evolved year over year.