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Diffusion models for continuous data gained widespread adoption owing to their high quality generation and control mechanisms.
Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules
David Weininger · 1988
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Learning multiple layers of features from tiny images, 2009
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Reversibility and stochastic networks
Frank P Kelly · 2011
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Text8 dataset, 2011
Matt Mahoney · 2011
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Quantifying the chemical beauty of drugs
G Richard Bickerton, Gaia V Paolini, Jérémy Besnard, Sorel Muresan, and Andrew L Hopkins · 2012
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Enumeration of 166 billion organic small molecules in the chemical universe database gdb-17
Lars Ruddigkeit, Ruud Van Deursen, Lorenz C Blum, and Jean-Louis Reymond · 2012
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Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling
Greg Landrum et al · 2013
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Hidden factors and hidden topics: understanding rating dimensions with review text
Julian McAuley and Jure Leskovec · 2013
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One billion word benchmark for measuring progress in statistical language modeling, 2014
Ciprian Chelba, Tomas Mikolov, Mike Schuster, Qi Ge, Thorsten Brants, Phillipp Koehn, and Tony Robinson · 2014
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Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, and O Anatole Von Lilienfeld · 2014
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun · 2015
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Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation
Nuala A O’Leary, Mathew W Wright, J Rodney Brister, Stacy Ciufo, Diana Haddad, Rich McVeigh, Bhanu Rajput, Barbara Robbertse, Brian Smith-White, Danso Ako-Adjei, Alexander Astashyn, Azat Badretdin, Yiming Bao, Olga Blinkova, Vyacheslav Brover, Vyacheslav Chetvernin, Jinna Choi, Eric Cox, Olga Ermolaeva, Catherine M Farrell, Tamara Goldfarb, Tripti Gupta, Daniel Haft, Eneida Hatcher, Wratko Hlavina, Vinita S Joardar, Vamsi K Kodali, Wenjun Li, Donna Maglott, Patrick Masterson, Kelly M McGarvey, Michael R Murphy, Kathleen O’Neill, Shashikant Pujar, Sanjida H Rangwala, Daniel Rausch, Lillian D Riddick, Conrad Schoch, Andrei Shkeda, Susan S Storz, Hanzhen Sun, Francoise Thibaud-Nissen, Igor Tolstoy, Raymond E Tully, Anjana R Vatsan, Craig Wallin, David Webb, Wendy Wu, Melissa J Landrum, Avi Kimchi, Tatiana Tatusova, Michael DiCuccio, Paul Kitts, Terence D Murphy, and Kim D Pruitt · 2016
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Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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Transformer-xl: Attentive language models beyond a fixed-length context
Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V Le, and Ruslan Salakhutdinov · 2019
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Plug and play language models: A simple approach to controlled text generation
Sumanth Dathathri, Andrea Madotto, Janice Lan, Jane Hung, Eric Frank, Piero Molino, Jason Yosinski, and Rosanne Liu · 2019
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Language models are unsupervised multitask learners, 2019
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
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Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction
Philippe Schwaller, Teodoro Laino, Théophile Gaudin, Peter Bolgar, Christopher A Hunter, Costas Bekas, and Alpha A Lee · 2019
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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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Discrete flows: Invertible generative models of discrete data
Dustin Tran, Keyon Vafa, Kumar Agrawal, Laurent Dinh, and Ben Poole · 2019
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Bert has a mouth, and it must speak: Bert as a markov random field language model
Alex Wang and Kyunghyun Cho · 2019
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Latent normalizing flows for discrete sequences
Zachary Ziegler and Alexander Rush · 2019
Cited alongside, same era.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush · 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.
Digress: Discrete denoising diffusion for graph generation
Clement Vignac, Igor Krawczuk, Antoine Siraudin, Bohan Wang, Volkan Cevher, and Pascal Frossard · 2022
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Alex Graves, Rupesh Kumar Srivastava, Timothy Atkinson, and Faustino Gomez · 2023
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Mamba: Linear-time sequence modeling with selective state spaces
Albert Gu and Tri Dao · 2023
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Discrete diffusion language modeling by estimating the ratios of the data distribution
Aaron Lou, Chenlin Meng, and Stefano Ermon · 2023
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Scalable diffusion models with transformers
William Peebles and Saining Xie · 2023
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Oops i took a gradient: Scalable sampling for discrete distributions
Will Grathwohl, Kevin Swersky, Milad Hashemi, David Duvenaud, and Chris Maddison · 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.
Variational diffusion models
Diederik Kingma, Tim Salimans, Ben Poole, and Jonathan Ho · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Roformer: Enhanced transformer with rotary position embedding
Jianlin Su, Yu Lu, Shengfeng Pan, Ahmed Murtadha, Bo Wen, and Yunfeng Liu · 2021
Cited alongside, same era.
Omninet: Omnidirectional representations from transformers
Yi Tay, Mostafa Dehghani, Vamsi Aribandi, Jai Gupta, Philip M Pham, Zhen Qin, Dara Bahri, Da-Cheng Juan, and Donald Metzler · 2021
Cited alongside, same era.
Fudge: Controlled text generation with future discriminators
Kevin Yang and Dan Klein · 2021
Cited alongside, same era.
Later among the works it cites.
Stay on topic with classifier-free guidance
Guillaume Sanchez, Honglu Fan, Alexander Spangher, Elad Levi, Pawan Sasanka Ammanamanchi, and Stella Biderman · 2023
Later among the works it cites.
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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A reparameterized discrete diffusion model for text generation
Lin Zheng, Jianbo Yuan, Lei Yu, and Lingpeng Kong · 2023
Later among the works it cites.
Andrew Campbell, Jason Yim, Regina Barzilay, Tom Rainforth, and Tommi Jaakkola · 2024
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Cat3d: Create anything in 3d with multi-view diffusion models, 2024
Ruiqi Gao, Aleksander Holynski, Philipp Henzler, Arthur Brussee, Ricardo Martin-Brualla, Pratul Srinivasan, Jonathan T. Barron, and Ben Poole · 2024
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Itai Gat, Tal Remez, Neta Shaul, Felix Kreuk, Ricky TQ Chen, Gabriel Synnaeve, Yossi Adi, and Yaron Lipman · 2024
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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.
Protein design with guided discrete diffusion
Nate Gruver, Samuel Stanton, Nathan Frey, Tim GJ Rudner, Isidro Hotzel, Julien Lafrance-Vanasse, Arvind Rajpal, Kyunghyun Cho, and Andrew G Wilson · 2024
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Latent diffusion for language generation
Justin Lovelace, Varsha Kishore, Chao Wan, Eliot Shekhtman, and Kilian Q Weinberger · 2024
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Classifier-free graph diffusion for molecular property targeting
Matteo Ninniri, Marco Podda, and Davide Bacciu · 2024
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Unlocking guidance for discrete state-space diffusion and flow models
Hunter Nisonoff, Junhao Xiong, Stephan Allenspach, and Jennifer Listgarten · 2024
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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
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Designing dna with tunable regulatory activity using discrete diffusion
Anirban Sarkar, Ziqi Tang, Chris Zhao, and Peter Koo · 2024
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Caduceus: Bi-directional equivariant long-range dna sequence modeling
Yair Schiff, Chia-Hsiang Kao, Aaron Gokaslan, Tri Dao, Albert Gu, and Volodymyr Kuleshov · 2024
Closest in time.
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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Dirichlet flow matching with applications to dna sequence design
Hannes Stark, Bowen Jing, Chenyu Wang, Gabriele Corso, Bonnie Berger, Regina Barzilay, and Tommi Jaakkola · 2024
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Improving and unifying discrete&continuous-time discrete denoising diffusion
Lingxiao Zhao, Xueying Ding, Lijun Yu, and Leman Akoglu · 2024
Closest in time.