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Diffusion models have been successful on a range of conditional generation tasks including molecular design and text-to-image generation.
An empirical Bayes approach to statistics
Herbert Robbins · 1956
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Novel approach to nonlinear/non-Gaussian Bayesian state estimation
Neil J Gordon, David J Salmond, and Adrian FM Smith · 1993
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Filtering via simulation: Auxiliary particle filters
Michael K Pitt and Neil Shephard · 1999
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Sequential Monte Carlo methods in practice
Arnaud Doucet, Nando De Freitas, and Neil James Gordon · 2001
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Tweedie’s formula and selection bias
Bradley Efron · 2011
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A connection between score matching and denoising autoencoders
Pascal Vincent · 2011
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Gaussian approximation of non-linear measurement models on Lie groups
Gregory Chirikjian and Marin Kobilarov · 2014
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Amortized inference in probabilistic reasoning
Samuel Gershman and Noah Goodman · 2014
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Twisted particle filters
Nick Whiteley and Anthony Lee · 2014
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The iterated auxiliary particle filter
Pieralberto Guarniero, Adam M Johansen, and Anthony Lee · 2017
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The sample size required in importance sampling
Sourav Chatterjee and Persi Diaconis · 2018
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Elements of sequential Monte Carlo
Christian A Naesseth, Fredrik Lindsten, and Thomas B Schön · 2019
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An introduction to sequential Monte Carlo
Nicolas Chopin and Omiros Papaspiliopoulos · 2020
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Controlled sequential Monte Carlo
Jeremy Heng, Adrian N Bishop, George Deligiannidis, and Arnaud Doucet · 2020
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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.
Diffusion models beat GANs on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
Cited alongside, same era.
Highly accurate protein structure prediction with AlphaFold
John M. Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Zídek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon A A Kohl, Andy Ballard, Andrew Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Jonas Adler, Trevor Back, Stig Petersen, David A. Reiman, Ellen Clancy, Michal Zielinski, Martin Steinegger, Michalina Pacholska, Tamas Berghammer, Sebastian Bodenstein, David Silver, Oriol Vinyals, Andrew W. Senior, Koray Kavukcuoglu, Pushmeet Kohli, and Demis Hassabis · 2021
Cited alongside, same era.
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.
Palette: Image-to-image diffusion models
Chitwan Saharia, William Chan, Huiwen Chang, Chris Lee, Jonathan Ho, Tim Salimans, David Fleet, and Mohammad Norouzi · 2022
Later among the works it cites.
Conditional simulation using diffusion Schrödinger bridges
Yuyang Shi, Valentin De Bortoli, George Deligiannidis, and Arnaud Doucet · 2022
Later among the works it cites.
Scaffolding protein functional sites using deep learning
Jue Wang, Sidney Lisanza, David Juergens, Doug Tischer, Joseph L Watson, Karla M Castro, Robert Ragotte, Amijai Saragovi, Lukas F Milles, Minkyung Baek, Ivan Anishchenko, Wei Yang, Derrick R Hicks, Marc Exposit, Thomas Schlichthaerle, Jung-Ho Chun, Nathaniel Dauparas, Justas Bennett, Basile I M Wicky, Andrew Muenks, Frank DiMaio, Bruno Correia, Sergey Ovchinnikov, and David Baker · 2022
Later among the works it cites.
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SDEdit: Guided image synthesis and editing with stochastic differential equations
Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, and Stefano Ermon · 2021
Cited alongside, same era.
Robust deep learning-based protein sequence design using ProteinMPNN
Justas Dauparas, Ivan Anishchenko, Nathaniel Bennett, Hua Bai, Robert J Ragotte, Lukas F Milles, Basile IM Wicky, Alexis Courbet, Rob J de Haas, Neville Bethel, , Philip J Leung, Timothy Huddy, Sam Pellock, Doug Tischer, F Chan, Brian Koepnick, H Nguyen, Alex Kang, B Sankaran, Asim K. Bera, Neil P. King, and David Baker · 2022
Cited alongside, same era.
Riemannian score-based generative modelling
Valentin De Bortoli, Emile Mathieu, Michael John Hutchinson, James Thornton, Yee Whye Teh, and Arnaud Doucet · 2022
Cited alongside, same era.
Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2022
Cited alongside, same era.
Video diffusion models
Jonathan Ho, Tim Salimans, Alexey A Gritsenko, William Chan, Mohammad Norouzi, and David J Fleet · 2022
Cited alongside, same era.
Elucidating the design space of diffusion-based generative models
Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine · 2022
Cited alongside, same era.
Denoising diffusion restoration models
Bahjat Kawar, Michael Elad, Stefano Ermon, and Jiaming Song · 2022
Cited alongside, same era.
Arpit Bansal, Hong-Min Chu, Avi Schwarzschild, Soumyadip Sengupta, Micah Goldblum, Jonas Geiping, and Tom Goldstein · 2023
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Monte Carlo guided diffusion for Bayesian linear inverse problems
Gabriel Cardoso, Yazid Janati El Idrissi, Sylvain Le Corff, and Eric Moulines · 2023
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Diffusion posterior sampling for general noisy inverse problems
Hyungjin Chung, Jeongsol Kim, Michael T Mccann, Marc L Klasky, and Jong Chul Ye · 2023
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Reduce, reuse, recycle: Compositional generation with energy-based diffusion models and MCMC
Yilun Du, Conor Durkan, Robin Strudel, Joshua B Tenenbaum, Sander Dieleman, Rob Fergus, Jascha Sohl-Dickstein, Arnaud Doucet, and Will Grathwohl · 2023
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Pseudoinverse-guided diffusion models for inverse problems
Jiaming Song, Arash Vahdat, Morteza Mardani, and Jan Kautz · 2023
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Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem
Brian L Trippe, Jason Yim, Doug Tischer, Tamara Broderick, David Baker, Regina Barzilay, and Tommi Jaakkola · 2023
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De novo design of protein structure and function with RFdiffusion
Joseph L. Watson, David Juergens, Nathaniel R. Bennett, Brian L. Trippe, Jason Yim, Helen E. Eisenach, Woody Ahern, Andrew J. Borst, Robert J. Ragotte, Lukas F. Milles, Basile I. M. Wicky, Nikita Hanikel, Samuel J. Pellock, Alexis Courbet, William Sheffler, Jue Wang, Preetham Venkatesh, Isaac Sappington, Susana Vázquez Torres, Anna Lauko, Valentin De Bortoli, Emile Mathieu, Sergey Ovchinnikov, Regina Barzilay, Tommi S. Jaakkola, Frank DiMaio, Minkyung Baek, and David Baker · 2023
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SE(3) diffusion model with application to protein backbone generation
Jason Yim, Brian L Trippe, Valentin De Bortoli, Emile Mathieu, Arnaud Doucet, Regina Barzilay, and Tommi Jaakkola · 2023
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Towards coherent image inpainting using denoising diffusion implicit models
Guanhua Zhang, Jiabao Ji, Yang Zhang, Mo Yu, Tommi Jaakkola, and Shiyu Chang · 2023
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