Fetching the paper…
Reading the bibliography…
Denoising Diffusion models have demonstrated their proficiency for generative sampling.
A family of embedded runge-kutta formulae
John R Dormand and Peter J Prince · 1980
Earlier work this paper cites.
Computer methods for ordinary differential equations and differential-algebraic equations , volume 61
Uri M Ascher and Linda R Petzold · 1998
Earlier work this paper cites.
An introduction to numerical analysis
Endre Süli and David F Mayers · 2003
Earlier work this paper cites.
Explicit exponential runge–kutta methods for semilinear parabolic problems
Marlis Hochbruck and Alexander Ostermann · 2005
Earlier work this paper cites.
Wavegrad: Estimating gradients for waveform generation
Nanxin Chen, Yu Zhang, Heiga Zen, Ron J Weiss, Mohammad Norouzi, and William Chan · 2009
Earlier work this paper cites.
Exponential integrators
Marlis Hochbruck and Alexander Ostermann · 2010
Earlier work this paper cites.
Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee et al · 2013
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
Earlier work this paper cites.
Continuous control with deep reinforcement learning
Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2016
Earlier work this paper cites.
Density estimation using real NVP
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2017
Earlier work this paper cites.
Gans trained by a two time-scale update rule converge to a nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, Günter Klambauer, and Sepp Hochreiter · 2017
Earlier work this paper cites.
Neural ordinary differential equations
Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud · 2018
Earlier work this paper cites.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
Earlier work this paper cites.
Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
Earlier work this paper cites.
Generative adversarial networks
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2020
Earlier work this paper cites.
Bootstrap your own latent-a new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
Diffwave: A versatile diffusion model for audio synthesis
Zhifeng Kong, Wei Ping, Jiaji Huang, Kexin Zhao, and Bryan Catanzaro · 2020
Cited alongside, same era.
Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2020
Cited alongside, same era.
Structured denoising diffusion models in discrete state-spaces
Jacob Austin, Daniel D Johnson, Jonathan Ho, Daniel Tarlow, and Rianne van den Berg · 2021
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 · 2021
Later among the works it cites.
Score-based generative modeling in latent space
Arash Vahdat, Karsten Kreis, and Jan Kautz · 2021
Later among the works it cites.
Diffuseq: Sequence to sequence text generation with diffusion models
Shansan Gong, Mukai Li, Jiangtao Feng, Zhiyong Wu, and LingPeng Kong · 2022
Later among the works it cites.
Nu-wave 2: A general neural audio upsampling model for various sampling rates
Seungu Han and Junhyeok Lee · 2022
Later among the works it cites.
Cascaded diffusion models for high fidelity image generation
Jonathan Ho, Chitwan Saharia, William Chan, David J Fleet, Mohammad Norouzi, and Tim Salimans · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
Cited alongside, same era.
Argmax flows and multinomial diffusion: Learning categorical distributions
Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, and Max Welling · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Gotta go fast when generating data with score-based models
Alexia Jolicoeur-Martineau, Ke Li, Rémi Piché-Taillefer, Tal Kachman, and Ioannis Mitliagkas · 2021
Cited alongside, same era.
Nu-wave: A diffusion probabilistic model for neural audio upsampling
Junhyeok Lee and Seungu Han · 2021
Cited alongside, same era.
Diffsinger: Diffusion acoustic model for singing voice synthesis
Jinglin Liu, Chengxi Li, Yi Ren, Feiyang Chen, Peng Liu, and Zhou Zhao · 2021
Cited alongside, same era.
Knowledge distillation in iterative generative models for improved sampling speed
Eric Luhman and Troy Luhman · 2021
Cited alongside, same era.
Prodiff: Progressive fast diffusion model for high-quality text-to-speech
Rongjie Huang, Zhou Zhao, Huadai Liu, Jinglin Liu, Chenye Cui, and Yi Ren · 2022
Later among the works it cites.
Elucidating the design space of diffusion-based generative models
Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine · 2022
Later among the works it cites.
Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps
Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu · 2022
Later among the works it cites.
On distillation of guided diffusion models
Chenlin Meng, Ruiqi Gao, Diederik P Kingma, Stefano Ermon, Jonathan Ho, and Tim Salimans · 2022
Later among the works it cites.
Scalable diffusion models with transformers
William Peebles and Saining Xie · 2022
Later among the works it cites.
Hierarchical text-conditional image generation with clip latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
Later among the works it cites.
High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
Later among the works it cites.
Progressive distillation for fast sampling of diffusion models
Tim Salimans and Jonathan Ho · 2022
Later among the works it cites.
Fast sampling of diffusion models via operator learning
Hongkai Zheng, Weili Nie, Arash Vahdat, Kamyar Azizzadenesheli, and Anima Anandkumar · 2022
Later among the works it cites.
f-DM: A multi-stage diffusion model via progressive signal transformation
Jiatao Gu, Shuangfei Zhai, Yizhe Zhang, Miguel Ángel Bautista, and Joshua M. Susskind · 2023
Closest in time.