Fetching the paper…
Reading the bibliography…
Discrete diffusion has emerged as a powerful framework for generative modeling in discrete domains, yet efficiently sampling from these models remains challenging.
Implementing random scan Gibbs samplers
Richard A Levine, Zhaoxia Yu, William G Hanley, and John J Nitao · 2005
Earlier work this paper cites.
Optimizing random scan Gibbs samplers
Richard A Levine and George Casella · 2006
Earlier work this paper cites.
Text8 dataset
Matt Mahoney · 2006
Earlier work this paper cites.
Machine Learning: A Probabilistic Perspective
Kevin P. Murphy · 2012
Earlier work this paper cites.
Adaptive Gibbs samplers and related MCMC methods
Krzysztof Łatuszyński, Gareth O Roberts, and Jeffrey S Rosenthal · 2013
Earlier work this paper cites.
Analyzing Hogwild parallel Gaussian Gibbs sampling
Matthew J Johnson, James Saunderson, and Alan Willsky · 2013
Earlier work this paper cites.
A* sampling
Chris J Maddison, Daniel Tarlow, and Tom Minka · 2014
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.
Improving Gibbs sampler scan quality with DoGS
Ioannis Mitliagkas and Lester Mackey · 2017
Earlier work this paper cites.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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.
Informed proposals for local MCMC in discrete spaces
Giacomo Zanella · 2019
Earlier work this paper cites.
Stochastic beams and where to find them: The Gumbel-top-k trick for sampling sequences without replacement
Wouter Kool, Herke Van Hoof, and Max Welling · 2019
Earlier work this paper cites.
XLNet: Generalized autoregressive pretraining for language understanding
Zhilin Yang · 2019
Earlier work this paper cites.
Decoupled weight decay regularization, 2019
Ilya Loshchilov and Frank Hutter · 2019
Cited alongside, same era.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
Zero-shot text-to-image generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever · 2021
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
Cited alongside, same era.
Step-unrolled denoising autoencoders for text generation
Nikolay Savinov, Junyoung Chung, Mikolaj Binkowski, Erich Elsen, and Aaron van den Oord · 2021
A reparameterized discrete diffusion model for text generation
Lin Zheng, Jianbo Yuan, Lei Yu, and Lingpeng Kong · 2023
Later among the works it cites.
Discrete diffusion language modeling by estimating the ratios of the data distribution
Aaron Lou, Chenlin Meng, and Stefano Ermon · 2023
Later among the works it cites.
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, et al · 2023
Later among the works it cites.
Protein generation with evolutionary diffusion: sequence is all you need
Sarah Alamdari, Nitya Thakkar, Rianne van den Berg, Alex Lu, Nicolo Fusi, Ava Amini, and Kevin Yang · 2023
Later among the works it cites.
DiGress: Discrete denoising diffusion for graph generation
Clement Vignac, Igor Krawczuk, Antoine Siraudin, Bohan Wang, Volkan Cevher, and Pascal Frossard · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
Cited alongside, same era.
A continuous time framework for discrete denoising models
Andrew Campbell, Joe Benton, Valentin De Bortoli, Thomas Rainforth, George Deligiannidis, and Arnaud Doucet · 2022
Cited alongside, same era.
Concrete score matching: Generalized score matching for discrete data
Chenlin Meng, Kristy Choi, Jiaming Song, and Stefano Ermon · 2022
Cited alongside, same era.
Gradient estimation with discrete stein operators
Jiaxin Shi, Yuhao Zhou, Jessica Hwang, Michalis K Titsias, and Lester Mackey · 2022
Cited alongside, same era.
Score-based continuous-time discrete diffusion models
Haoran Sun, Lijun Yu, Bo Dai, Dale Schuurmans, and Hanjun Dai · 2022
Cited alongside, same era.
MaskGIT: Masked generative image transformer
Huiwen Chang, Han Zhang, Lu Jiang, Ce Liu, and William T Freeman · 2022
Cited alongside, same era.
Later among the works it cites.
Simplified and generalized masked diffusion for discrete data
Jiaxin Shi, Kehang Han, Zhe Wang, Arnaud Doucet, and Michalis K Titsias · 2024
Closest in time.
Simple and effective masked diffusion language models
Subham Sekhar Sahoo, Marianne Arriola, Yair Schiff, Aaron Gokaslan, Edgar Marroquin, Justin T Chiu, Alexander Rush, and Volodymyr Kuleshov · 2024
Closest in time.
Your absorbing discrete diffusion secretly models the conditional distributions of clean data
Jingyang Ou, Shen Nie, Kaiwen Xue, Fengqi Zhu, Jiacheng Sun, Zhenguo Li, and Chongxuan Li · 2024
Closest in time.
From denoising diffusions to denoising Markov models
Joe Benton, Yuyang Shi, Valentin De Bortoli, George Deligiannidis, and Arnaud Doucet · 2024
Closest in time.
Discrete flow matching
Itai Gat, Tal Remez, Neta Shaul, Felix Kreuk, Ricky TQ Chen, Gabriel Synnaeve, Yossi Adi, and Yaron Lipman · 2024
Closest in time.
Train for the worst, plan for the best: Understanding token ordering in masked diffusions
Jaeyeon Kim, Kulin Shah, Vasilis Kontonis, Sham Kakade, and Sitan Chen · 2025
Closest in time.
Masked diffusion models are secretly time-agnostic masked models and exploit inaccurate categorical sampling
Kaiwen Zheng, Yongxin Chen, Hanzi Mao, Ming-Yu Liu, Jun Zhu, and Qinsheng Zhang · 2025
Closest in time.
Large language diffusion models
Shen Nie, Fengqi Zhu, Zebin You, Xiaolu Zhang, Jingyang Ou, Jun Hu, Jun Zhou, Yankai Lin, Ji-Rong Wen, and Chongxuan Li · 2025
Closest in time.
Remasking discrete diffusion models with inference-time scaling
Guanghan Wang, Yair Schiff, Subham Sekhar Sahoo, and Volodymyr Kuleshov · 2025
Closest in time.