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Recent studies have demonstrated the strong empirical performance of diffusion models on discrete sequences across domains from natural language to biological sequence generation.
Pyrosetta: a script-based interface for implementing molecular modeling algorithms using rosetta
Sidhartha Chaudhury, Sergey Lyskov, and Jeffrey J Gray · 2010
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2010
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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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An integrated encyclopedia of dna elements in the human genome
ENCODE Project Consortium et al · 2012
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Continuous-time Markov chains and applications: a singular perturbation approach , volume 37
George G Yin and Qing Zhang · 2012
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Adam: A method for stochastic optimization
Diederik P Kingma · 2014
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Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2016
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The concrete distribution: A continuous relaxation of discrete random variables
Chris J Maddison, Andriy Mnih, and Yee Whye Teh · 2016
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Markov chains and mixing times , volume 107
David A Levin and Yuval Peres · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin · 2018
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Openwebtext corpus
Aaron Gokaslan, Vanya Cohen, Ellie Pavlick, and Stefanie Tellex · 2019
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Offline reinforcement learning: Tutorial, review, and perspectives on open problems
Sergey Levine, Aviral Kumar, George Tucker, and Justin Fu · 2020
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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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Effective gene expression prediction from sequence by integrating long-range interactions
Žiga Avsec, Vikram Agarwal, Daniel Visentin, Joseph R Ledsam, Agnieszka Grabska-Barwinska, Kyle R Taylor, Yannis Assael, John Jumper, Pushmeet Kohli, and David R Kelley · 2021
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Machine learning for designing next-generation mrna therapeutics
Sebastian M Castillo-Hair and Georg Seelig · 2021
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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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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Jaspar 2022: the 9th release of the open-access database of transcription factor binding profiles
Jaime A Castro-Mondragon, Rafael Riudavets-Puig, Ieva Rauluseviciute, Roza Berhanu Lemma, Laura Turchi, Romain Blanc-Mathieu, Jeremy Lucas, Paul Boddie, Aziz Khan, Nicolás Manosalva Pérez, et al · 2022
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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, et al · 2022
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Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2022
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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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Score-based continuous-time discrete diffusion models
Haoran Sun, Lijun Yu, Bo Dai, Dale Schuurmans, and Hanjun Dai · 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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Dirichlet diffusion score model for biological sequence generation
Pavel Avdeyev, Chenlai Shi, Yuhao Tan, Kseniia Dudnyk, and Jian Zhou · 2023
Andrew Campbell, Jason Yim, Regina Barzilay, Tom Rainforth, and Tommi Jaakkola · 2024
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An all-atom protein generative model
Alexander E Chu, Jinho Kim, Lucy Cheng, Gina El Nesr, Minkai Xu, Richard W Shuai, and Po-Ssu Huang · 2024
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Carles Domingo-Enrich, Michal Drozdzal, Brian Karrer, and Ricky TQ Chen · 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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Gradient guidance for diffusion models: An optimization perspective
Yingqing Guo, Hui Yuan, Yukang Yang, Minshuo Chen, and Mengdi Wang · 2024
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Cited alongside, same era.
Universal guidance for diffusion models
Arpit Bansal, Hong-Min Chu, Avi Schwarzschild, Soumyadip Sengupta, Micah Goldblum, Jonas Geiping, and Tom Goldstein · 2023
Cited alongside, same era.
Training diffusion models with reinforcement learning
Kevin Black, Michael Janner, Yilun Du, Ilya Kostrikov, and Sergey Levine · 2023
Cited alongside, same era.
Monte carlo guided diffusion for bayesian linear inverse problems
Gabriel Cardoso, Yazid Janati El Idrissi, Sylvain Le Corff, and Eric Moulines · 2023
Cited alongside, same era.
Directly fine-tuning diffusion models on differentiable rewards
Kevin Clark, Paul Vicol, Kevin Swersky, and David J Fleet · 2023
Cited alongside, same era.
Aira, 2023
Nicholas Kluge Corrêa · 2023
Cited alongside, same era.
DPOK: 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 · 2023
Cited alongside, same era.
Machine-guided design of synthetic cell type-specific cis-regulatory elements
Sager J Gosai, Rodrigo I Castro, Natalia Fuentes, John C Butts, Susan Kales, Ramil R Noche, Kousuke Mouri, Pardis C Sabeti, Steven K Reilly, and Ryan Tewhey · 2023
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reglm: Designing realistic regulatory dna with autoregressive language models
Avantika Lal, David Garfield, Tommaso Biancalani, and Gokcen Eraslan · 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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Particle denoising diffusion sampler
Angus Phillips, Hai-Dang Dau, Michael John Hutchinson, Valentin De Bortoli, George Deligiannidis, and Arnaud Doucet · 2024
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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
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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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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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Cell-type-directed design of synthetic enhancers
Ibrahim I Taskiran, Katina I Spanier, Hannah Dickmänken, Niklas Kempynck, Alexandra Pančíková, Eren Can Ekşi, Gert Hulselmans, Joy N Ismail, Koen Theunis, Roel Vandepoel, et al · 2024
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Amortizing intractable inference in diffusion models for vision, language, and control
Siddarth Venkatraman, Moksh Jain, Luca Scimeca, Minsu Kim, Marcin Sendera, Mohsin Hasan, Luke Rowe, Sarthak Mittal, Pablo Lemos, Emmanuel Bengio, et al · 2024
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Aligning protein generative models with experimental fitness via direct preference optimization
Talal Widatalla, Rafael Rafailov, and Brian Hie · 2024
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Practical and asymptotically exact conditional sampling in diffusion models
Luhuan Wu, Brian Trippe, Christian Naesseth, David Blei, and John P Cunningham · 2024
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Probabilistic inference in language models via twisted sequential monte carlo
Stephen Zhao, Rob Brekelmans, Alireza Makhzani, and Roger Grosse · 2024
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Beta diffusion
Mingyuan Zhou, Tianqi Chen, Zhendong Wang, and Huangjie Zheng · 2024
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Fine-tuning language models from human preferences
Daniel M Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving · 2024
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