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Diffusion models produce impressive results in modalities ranging from images and video to protein design and text.
Safe and effective importance sampling
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Feynman-Kac formulae: genealogical and interacting particle systems with applications
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Genealogical particle analysis of rare events
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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 · 2011
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Improved diffusion monte carlo
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Deep unsupervised learning using nonequilibrium thermodynamics
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A diversity-promoting objective function for neural conversation models
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The sample size required in importance sampling
Sourav Chatterjee and Persi Diaconis · 2018
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Sequential monte carlo methods
Arnaud Doucet and Anthony Lee · 2018
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Neural network acceptability judgments
Alex Warstadt, Amanpreet Singh, and Samuel R Bowman · 2018
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Elements of sequential monte carlo
Christian A Naesseth, Fredrik Lindsten, Thomas B Schön, et al · 2019
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Applied stochastic differential equations , volume 10
Simo Särkkä and Arno Solin · 2019
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Openwebtext corpus
Aaron Gokaslan, Vanya Cohen, Ellie Pavlick, and Stefanie Tellex · 2019
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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An introduction to sequential Monte Carlo , volume 4
Nicolas Chopin, Omiros Papaspiliopoulos, et al · 2020
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Textattack: A framework for adversarial attacks, data augmentation, and adversarial training in nlp
John Morris, Eli Lifland, Jin Yong Yoo, Jake Grigsby, Di Jin, and Yanjun Qi · 2020
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Diederik P Kingma, Tim Salimans, Ben Poole, and Jonathan Ho · 2021
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Structured denoising diffusion models in discrete state-spaces
Jacob Austin, Daniel D Johnson, Jonathan Ho, Daniel Tarlow, and Rianne Van Den Berg · 2021
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Glide: Towards photorealistic image generation and editing with text-guided diffusion models
Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen · 2021
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Improved denoising diffusion probabilistic models
Alex Nichol and Prafulla Dhariwal · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Video diffusion models
Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, and David J Fleet · 2022
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Universal guidance for diffusion models
Arpit Bansal, Hong-Min Chu, Avi Schwarzschild, Soumyadip Sengupta, Micah Goldblum, Jonas Geiping, and Tom Goldstein · 2023
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Sdxl: Improving latent diffusion models for high-resolution image synthesis
Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach · 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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Sequential monte carlo steering of large language models using probabilistic programs
Alexander K Lew, Tan Zhi-Xuan, Gabriel Grand, and Vikash K Mansinghka · 2023
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Where to diffuse, how to diffuse, and how to get back: Automated learning for multivariate diffusions
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Diffusion-lm improves controllable text generation, 2022
Xiang Lisa Li, John Thickstun, Ishaan Gulrajani, Percy Liang, and Tatsunori B. Hashimoto · 2022
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Rl with kl penalties is better viewed as bayesian inference
Tomasz Korbak, Ethan Perez, and Christopher L Buckley · 2022
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Diffusion probabilistic modeling of protein backbones in 3d for the motif-scaffolding problem
Brian L Trippe, Jason Yim, Doug Tischer, David Baker, Tamara Broderick, Regina Barzilay, and Tommi Jaakkola · 2022
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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 · 2022
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Flow matching for generative modeling
Yaron Lipman, Ricky TQ Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le · 2022
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A continuous time framework for discrete denoising models
Andrew Campbell, Joe Benton, Valentin De Bortoli, Thomas Rainforth, George Deligiannidis, and Arnaud Doucet · 2022
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Raghav Singhal, Mark Goldstein, and Rajesh Ranganath · 2023
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Scalable diffusion models with transformers
William Peebles and Saining Xie · 2023
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Paraguide: Guided diffusion paraphrasers for plug-and-play textual style transfer, 2024
Zachary Horvitz, Ajay Patel, Chris Callison-Burch, Zhou Yu, and Kathleen McKeown · 2024
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Geneval: An object-focused framework for evaluating text-to-image alignment
Dhruba Ghosh, Hannaneh Hajishirzi, and Ludwig Schmidt · 2024
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Imagereward: Learning and evaluating human preferences for text-to-image generation
Jiazheng Xu, Xiao Liu, Yuchen Wu, Yuxuan Tong, Qinkai Li, Ming Ding, Jie Tang, and Yuxiao Dong · 2024
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Carles Domingo-Enrich, Michal Drozdzal, Brian Karrer, and Ricky TQ Chen · 2024
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Diffusion model alignment using direct preference optimization
Bram Wallace, Meihua Dang, Rafael Rafailov, Linqi Zhou, Aaron Lou, Senthil Purushwalkam, Stefano Ermon, Caiming Xiong, Shafiq Joty, and Nikhil Naik · 2024
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Divide-and-conquer posterior sampling for denoising diffusion priors
Yazid Janati, Badr Moufad, Alain Durmus, Eric Moulines, and Jimmy Olsson · 2024
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Xiner Li, Yulai Zhao, Chenyu Wang, Gabriele Scalia, Gokcen Eraslan, Surag Nair, Tommaso Biancalani, Aviv Regev, Sergey Levine, and Masatoshi Uehara · 2024
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Reinforcement learning for fine-tuning text-to-image diffusion models
Ying Fan, Olivia Watkins, Yuqing Du, Hao Liu, Moonkyung Ryu, Craig Boutilier, Pieter Abbeel, Mohammad Ghavamzadeh, Kangwook Lee, and Kimin Lee · 2024
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Diffusion posterior sampling for linear inverse problem solving: A filtering perspective
Zehao Dou and Yang Song · 2024
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What’s the score? automated denoising score matching for nonlinear diffusions
Raghav Singhal, Mark Goldstein, and Rajesh Ranganath · 2024
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Simple and effective masked diffusion language models, 2024
Subham Sekhar Sahoo, Marianne Arriola, Yair Schiff, Aaron Gokaslan, Edgar Marroquin, Justin T Chiu, Alexander Rush, and Volodymyr Kuleshov · 2024
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Simplified and generalized masked diffusion for discrete data
Jiaxin Shi, Kehang Han, Zhe Wang, Arnaud Doucet, and Michalis K Titsias · 2024
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Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn · 2024
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Overview of the multilingual text detoxification task at pan 2024
Daryna Dementieva, Daniil Moskovskiy, Nikolay Babakov, Abinew Ali Ayele, Naquee Rizwan, Frolian Schneider, Xintog Wang, Seid Muhie Yimam, Dmitry Ustalov, Elisei Stakovskii, Alisa Smirnova, Ashraf Elnagar, Animesh Mukherjee, and Alexander Panchenko · 2024
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