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Diffusion models have gained traction as powerful algorithms for synthesizing high-quality images.
A family of embedded runge-kutta formulae
J.R. Dormand and P.J. Prince · 1980
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A stochastic estimator of the trace of the influence matrix for laplacian smoothing splines
Michael F Hutchinson · 1989
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The eigenvalues of mega-dimensional matrices
John Skilling · 1989
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Information theory, inference and learning algorithms
David JC MacKay · 2003
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Data compression
Thomas M Cover and Joy A Thomas · 2005
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Weighted random sampling with a reservoir
Pavlos S. Efraimidis and Paul G. Spirakis · 2005
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Fluctuation relations: a pedagogical overview, 2012
Richard E. Spinney and Ian J. Ford · 2012
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Rnade: The real-valued neural autoregressive density-estimator
Benigno Uria, Iain Murray, and Hugo Larochelle · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Deep unsupervised learning using nonequilibrium thermodynamics, 2015
Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Conditional image generation with pixelcnn decoders
Aaron Van den Oord, Nal Kalchbrenner, Lasse Espeholt, Oriol Vinyals, Alex Graves, et al · 2016
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Pixel recurrent neural networks
Aäron Van Den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu · 2016
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Good semi-supervised learning that requires a bad gan
Zihang Dai, Zhilin Yang, Fan Yang, William W Cohen, and Russ R Salakhutdinov · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Tim Salimans, Andrej Karpathy, Xi Chen, and Diederik P Kingma · 2017
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Pixeldefend: Leveraging generative models to understand and defend against adversarial examples
Yang Song, Taesup Kim, Sebastian Nowozin, Stefano Ermon, and Nate Kushman · 2017
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Neural ordinary differential equations
Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud · 2018
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Ffjord: Free-form continuous dynamics for scalable reversible generative models
Will Grathwohl, Ricky TQ Chen, Jesse Bettencourt, Ilya Sutskever, and David Duvenaud · 2018
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Glow: Generative flow with invertible 1x1 convolutions
Durk P Kingma and Prafulla Dhariwal · 2018
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Image transformer
Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Lukasz Kaiser, Noam Shazeer, Alexander Ku, and Dustin Tran · 2018
Cited alongside, same era.
Film: Visual reasoning with a general conditioning layer
Ethan Perez, Florian Strub, Harm De Vries, Vincent Dumoulin, and Aaron Courville · 2018
Cited alongside, same era.
Reparameterizable subset sampling via continuous relaxations
Sang Michael Xie and Stefano Ermon · 2019
Cited alongside, same era.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2020
Cited alongside, same era.
Autoregressive quantile flows for predictive uncertainty estimation
Phillip Si, Allan Bishop, and Volodymyr Kuleshov · 2022
Later among the works it cites.
Grigory Bartosh, Dmitry Vetrov, and Christian A Naesseth · 2023
Closest in time.
On the importance of noise scheduling for diffusion models
Ting Chen · 2023
Closest in time.
Calibrated uncertainty estimation improves bayesian optimization, 2023
Shachi Deshpande and Volodymyr Kuleshov · 2023
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A flexible diffusion model
Weitao Du, He Zhang, Tao Yang, and Yuanqi Du · 2023
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simple diffusion: End-to-end diffusion for high resolution images
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Diffusion schrödinger bridge with applications to score-based generative modeling
Valentin De Bortoli, James Thornton, Jeremy Heng, and Arnaud Doucet · 2021
Cited alongside, same era.
Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
Cited alongside, same era.
Variational diffusion models
Diederik Kingma, Tim Salimans, Ben Poole, and Jonathan Ho · 2021
Cited alongside, same era.
Priorgrad: Improving conditional denoising diffusion models with data-dependent adaptive prior
Sang-gil Lee, Heeseung Kim, Chaehun Shin, Xu Tan, Chang Liu, Qi Meng, Tao Qin, Wei Chen, Sungroh Yoon, and Tie-Yan Liu · 2021
Cited alongside, same era.
Implicit MLE: backpropagating through discrete exponential family distributions
Mathias Niepert, Pasquale Minervini, and Luca Franceschi · 2021
Cited alongside, same era.
Grad-tts: A diffusion probabilistic model for text-to-speech
Vadim Popov, Ivan Vovk, Vladimir Gogoryan, Tasnima Sadekova, and Mikhail Kudinov · 2021
Cited alongside, same era.
High-resolution image synthesis with latent diffusion models, 2021
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2021
Cited alongside, same era.
Emiel Hoogeboom, Jonathan Heek, and Tim Salimans · 2023
Closest in time.
Understanding the diffusion objective as a weighted integral of elbos
Diederik P Kingma and Ruiqi Gao · 2023
Closest in time.
Minimizing trajectory curvature of ode-based generative models
Sangyun Lee, Beomsu Kim, and Jong Chul Ye · 2023
Closest in time.
Reflected diffusion models
Aaron Lou and Stefano Ermon · 2023
Closest in time.
Diffenc: Variational diffusion with a learned encoder
Beatrix MG Nielsen, Anders Christensen, Andrea Dittadi, and Ole Winther · 2023
Closest in time.
Svnr: Spatially-variant noise removal with denoising diffusion
Naama Pearl, Yaron Brodsky, Dana Berman, Assaf Zomet, Alex Rav Acha, Daniel Cohen-Or, and Dani Lischinski · 2023
Closest in time.
Diffusion bridge mixture transports, schrödinger bridge problems and generative modeling
Stefano Peluchetti · 2023
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Semi-parametric inducing point networks and neural processes
Richa Rastogi, Yair Schiff, Alon Hacohen, Zhaozhi Li, Ian Lee, Yuntian Deng, Mert R. Sabuncu, and Volodymyr Kuleshov · 2023
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Backpropagation through combinatorial algorithms: Identity with projection works
Subham Sekhar Sahoo, Anselm Paulus, Marin Vlastelica, Vít Musil, Volodymyr Kuleshov, and Georg Martius · 2023
Closest in time.
Semi-autoregressive energy flows: exploring likelihood-free training of normalizing flows
Phillip Si, Zeyi Chen, Subham Sekhar Sahoo, Yair Schiff, and Volodymyr Kuleshov · 2023
Closest in time.
Infodiffusion: Representation learning using information maximizing diffusion models
Yingheng Wang, Yair Schiff, Aaron Gokaslan, Weishen Pan, Fei Wang, Christopher De Sa, and Volodymyr Kuleshov · 2023
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Lossy image compression with conditional diffusion models, 2023
Ruihan Yang and Stephan Mandt · 2023
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Improved techniques for maximum likelihood estimation for diffusion odes
Kaiwen Zheng, Cheng Lu, Jianfei Chen, and Jun Zhu · 2023
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Commoncanvas: Open diffusion models trained on creative-commons images
Aaron Gokaslan, A Feder Cooper, Jasmine Collins, Landan Seguin, Austin Jacobson, Mihir Patel, Jonathan Frankle, Cory Stephenson, and Volodymyr Kuleshov · 2024
Closest in time.
Diffusion schrödinger bridge matching
Yuyang Shi, Valentin De Bortoli, Andrew Campbell, and Arnaud Doucet · 2024
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Cross-modal contextualized diffusion models for text-guided visual generation and editing
Ling Yang, Zhilong Zhang, Zhaochen Yu, Jingwei Liu, Minkai Xu, Stefano Ermon, and Bin Cui · 2024
Closest in time.