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Score-based generative models (SGMs) have recently demonstrated impressive results in terms of both sample quality and distribution coverage.
A family of embedded Runge–Kutta formulae
J. R. Dormand and P. J. Prince · 1980
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Reverse-time diffusion equation models
Brian D.O. Anderson · 1982
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The mnist database of handwritten digits
Yann LeCun · 1998
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Estimation of Non-Normalized Statistical Models by Score Matching
Aapo Hyvärinen · 2005
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Interpretation and Generalization of Score Matching
Siwei Lyu · 2009
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Learning multiple layers of features from tiny images, 2009
Alex Krizhevsky et al · 2009
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Regularized estimation of image statistics by Score Matching
Durk P Kingma and Yann L. Cun · 2010
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A Connection between Score Matching and Denoising Autoencoders
Pascal Vincent · 2011
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Monte Carlo theory, methods and examples
c · 2013
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Generalized Denoising Auto-Encoders as Generative Models
Yoshua Bengio, Li Yao, Guillaume Alain, and Pascal Vincent · 2013
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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Semi-supervised learning with deep generative models
Diederik P Kingma, Shakir Mohamed, Danilo Jimenez Rezende, and Max Welling · 2014
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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
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Variational inference with normalizing flows
Danilo Jimenez Rezende and Shakir Mohamed · 2015
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Deep Unsupervised Learning Using Nonequilibrium Thermodynamics
Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Scheduled denoising autoencoders
Krzysztof J. Geras and Charles A. Sutton · 2015
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Importance weighted autoencoders
Yuri Burda, Roger Grosse, and Ruslan Salakhutdinov · 2015
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Improved variational inference with inverse autoregressive flow
Diederik P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling · 2016
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Xi Chen, Diederik P Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
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Discrete variational autoencoders
Jason Tyler Rolfe · 2016
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Adversarial autoencoders, 2016
Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, Ian Goodfellow, and Brendan Frey · 2016
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Ladder variational autoencoders
Casper Kaae Sønderby, Tapani Raiko, Lars Maaløe, Søren Kaae Sønderby, and Ole Winther · 2016
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Generating sentences from a continuous space
Samuel R Bowman, Luke Vilnis, Oriol Vinyals, Andrew Dai, Rafal Jozefowicz, and Samy Bengio · 2016
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Semi-supervised learning with generative adversarial networks
Augustus Odena · 2016
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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Tim Salimans, Andrej Karpathy, Xi Chen, and Diederik P Kingma · 2017
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Deep Energy Estimator Networks
Saeed Saremi, Arash Mehrjou, Bernhard Schölkopf, and Aapo Hyvärinen · 2018
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Graphvae: Towards generation of small graphs using variational autoencoders
Martin Simonovsky and Nikos Komodakis · 2018
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Junction tree variational autoencoder for molecular graph generation
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2018
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DVAE#: Discrete variational autoencoders with relaxed Boltzmann priors
Arash Vahdat, Evgeny Andriyash, and William G Macready · 2018
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DVAE++: Discrete variational autoencoders with overlapping transformations
Arash Vahdat, William G. Macready, Zhengbing Bian, Amir Khoshaman, and Evgeny Andriyash · 2018
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Latent constraints: Learning to generate conditionally from unconditional generative models
Jesse Engel, Matthew Hoffman, and Adam Roberts · 2018
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Vae with a vampprior
Jakub Tomczak and Max Welling · 2018
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Neural discrete representation learning
Aaron van den Oord, Oriol Vinyals, and Koray Kavukcuoglu · 2018
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Neural ordinary differential equations
Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, and David Duvenaud · 2018
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PixelSNAIL: An improved autoregressive generative model
XI Chen, Nikhil Mishra, Mostafa Rohaninejad, and Pieter Abbeel · 2018
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Glow: Generative flow with invertible 1x1 convolutions
Durk P Kingma and Prafulla Dhariwal · 2018
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Progressive growing of GANs for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2018
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Inverse molecular design using machine learning: Generative models for matter engineering
Benjamin Sanchez-Lengeling and Alán Aspuru-Guzik · 2018
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Generative Modeling by Estimating Gradients of the Data Distribution
Yang Song and Stefano Ermon · 2019
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Augmented neural ODEs
Emilien Dupont, Arnaud Doucet, and Yee Whye Teh · 2019
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Sliced Score Matching: A Scalable Approach to Density and Score Estimation
Yang Song, Sahaj Garg, Jiaxin Shi, and Stefano Ermon · 2019
Undirected graphical models as approximate posteriors
Arash Vahdat, Evgeny Andriyash, and William G Macready · 2020
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Learning latent space energy-based prior model
Bo Pang, Tian Han, Erik Nijkamp, Song-Chun Zhu, and Ying Nian Wu · 2020
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NCP-VAE: Variational autoencoders with noise contrastive priors
Jyoti Aneja, Alexander Schwing, Jan Kautz, and Arash Vahdat · 2020
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From variational to deterministic autoencoders
Partha Ghosh, Mehdi S. M. Sajjadi, Antonio Vergari, Michael Black, and Bernhard Scholkopf · 2020
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Taming transformers for high-resolution image synthesis
Patrick Esser, Robin Rombach, and Björn Ommer · 2020
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Learning Energy-Based Models in High-Dimensional Spaces with Multi-scale Denoising Score Matching
Zengyi Li, Yubei Chen, and Friedrich T. Sommer · 2019
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Graphite: Iterative generative modeling of graphs
Aditya Grover, Aaron Zweig, and Stefano Ermon · 2019
Cited alongside, same era.
Efficient graph generation with graph recurrent attention networks
Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, Will Hamilton, David K Duvenaud, Raquel Urtasun, and Richard Zemel · 2019
Cited alongside, same era.
BIVA: A very deep hierarchy of latent variables for generative modeling
Lars Maaløe, Marco Fraccaro, Valentin Liévin, and Ole Winther · 2019
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Resampled priors for variational autoencoders
Matthias Bauer and Andriy Mnih · 2019
Cited alongside, same era.
Variational autoencoder with implicit optimal priors
Hiroshi Takahashi, Tomoharu Iwata, Yuki Yamanaka, Masanori Yamada, and Satoshi Yagi · 2019
Cited alongside, same era.
Your gan is secretly an energy-based model and you should use discriminator driven latent sampling
Tong Che, Ruixiang ZHANG, Jascha Sohl-Dickstein, Hugo Larochelle, Liam Paull, Yuan Cao, and Yoshua Bengio · 2020
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Dual contradistinctive generative autoencoder
Gaurav Parmar, Dacheng Li, Kwonjoon Lee, and Zhuowen Tu · 2020
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Distribution augmentation for generative modeling
Heewoo Jun, Rewon Child, Mark Chen, John Schulman, Aditya Ramesh, Alec Radford, and Ilya Sutskever · 2020
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Training generative adversarial networks with limited data
Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, and Timo Aila · 2020
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Adversarial latent autoencoders
Stanislav Pidhorskyi, Donald A Adjeroh, and Gianfranco Doretto · 2020
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The tools of generative art, from flash to neural networks
J. Bailey · 2020
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Deepfakes and disinformation: Exploring the impact of synthetic political video on deception, uncertainty, and trust in news
Cristian Vaccari and Andrew Chadwick · 2020
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Optimus: Organizing sentences via pre-trained modeling of a latent space
Chunyuan Li, Xiang Gao, Yuan Li, Xiujun Li, Baolin Peng, Yizhe Zhang, and Jianfeng Gao · 2020
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Jukebox: A generative model for music
Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford, and Ilya Sutskever · 2020
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Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2021
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Maximum likelihood training of score-based diffusion models
Yang Song, Conor Durkan, Iain Murray, and Stefano Ermon · 2021
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2021
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alex Nichol · 2021
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Diff-tts: A denoising diffusion model for text-to-speech
Myeonghun Jeong, Hyeongju Kim, Sung Jun Cheon, Byoung Jin Choi, and Nam Soo Kim · 2021
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Symbolic music generation with diffusion models
Gautam Mittal, Jesse Engel, Curtis Hawthorne, and Ian Simon · 2021
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Diffusion probabilistic models for 3d point cloud generation
Shitong Luo and Wei Hu · 2021
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Very deep VAEs generalize autoregressive models and can outperform them on images
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A variational perspective on diffusion-based generative models and score matching
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Improved denoising diffusion probabilistic models
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Adversarial score matching and improved sampling for image generation
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Learning energy-based models by diffusion recovery likelihood
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Knowledge distillation in iterative generative models for improved sampling speed
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Noise estimation for generative diffusion models
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Refining deep generative models via discriminator gradient flow
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Controllable and compositional generation with latent-space energy-based models
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Diffusion priors in variational autoencoders
Antoine Wehenkel and Gilles Louppe · 2021
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D2c: Diffusion-denoising models for few-shot conditional generation
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Diederik P Kingma, Tim Salimans, Ben Poole, and Jonathan Ho · 2021
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Score matching model for unbounded data score
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VAEBM: A symbiosis between variational autoencoders and energy-based models
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Semantic segmentation with generative models: Semi-supervised learning and strong out-of-domain generalization
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