Fetching the paper…
Reading the bibliography…
Training-free guidance enables controlled generation in diffusion and flow models, but most methods rely on gradients and assume differentiable objectives.
music21: A toolkit for computer-aided musicology and symbolic music data
Michael Scott Cuthbert and Christopher Ariza · 2010
Earlier work this paper cites.
Tweedie’s formula and selection bias
Bradley Efron · 2011
Earlier work this paper cites.
Padel-descriptor: An open source software to calculate molecular descriptors and fingerprints
Chun Wei Yap · 2011
Earlier work this paper cites.
Improving autodock vina using random forest: the growing accuracy of binding affinity prediction by the effective exploitation of larger data sets
Hongjian Li, Kwong-Sak Leung, Man-Hon Wong, and Pedro J Ballester · 2015
Earlier work this paper cites.
Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2016
Earlier work this paper cites.
Enabling factorized piano music modeling and generation with the maestro dataset
Curtis Hawthorne, Andriy Stasyuk, Adam Roberts, Ian Simon, Cheng-Zhi Anna Huang, Sander Dieleman, Erich Elsen, Jesse Engel, and Douglas Eck · 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.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Earlier work this paper cites.
Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2020
Earlier work this paper cites.
Learning to summarize with human feedback
Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul F Christiano · 2020
Earlier work this paper cites.
Pop909: A pop-song dataset for music arrangement generation
Ziyu Wang, Ke Chen, Junyan Jiang, Yiyi Zhang, Maoran Xu, Shuqi Dai, Xianbin Gu, and Gus Xia · 2020
Earlier work this paper cites.
On the evaluation of generative models in music
Li-Chia Yang and Alexander Lerch · 2020
Earlier work this paper cites.
Structured denoising diffusion models in discrete state-spaces
Jacob Austin, Daniel D Johnson, Jonathan Ho, Daniel Tarlow, and Rianne Van Den Berg · 2021
Earlier work this paper cites.
Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
Earlier work this paper cites.
Argmax flows and multinomial diffusion: Learning categorical distributions
Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, and Max Welling · 2021
Earlier work this paper cites.
Compound word transformer: Learning to compose full-song music over dynamic directed hypergraphs
Wen-Yi Hsiao, Jen-Yu Liu, Yin-Cheng Yeh, and Yi-Hsuan Yang · 2021
Earlier work this paper cites.
Therapeutics data commons: Machine learning datasets and tasks for drug discovery and development
Kexin Huang, Tianfan Fu, Wenhao Gao, Yue Zhao, Yusuf Roohani, Jure Leskovec, Connor W Coley, Cao Xiao, Jimeng Sun, and Marinka Zitnik · 2021
Earlier work this paper cites.
Webgpt: Browser-assisted question-answering with human feedback
Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, et al · 2021
Earlier work this paper cites.
Is conditional generative modeling all you need for decision-making?
Anurag Ajay, Yilun Du, Abhi Gupta, Joshua Tenenbaum, Tommi Jaakkola, and Pulkit Agrawal · 2022
Earlier work this paper cites.
A continuous time framework for discrete denoising models
Andrew Campbell, Joe Benton, Valentin De Bortoli, Thomas Rainforth, George Deligiannidis, and Arnaud Doucet · 2022
Earlier work this paper cites.
Diffusion posterior sampling for general noisy inverse problems
Hyungjin Chung, Jeongsol Kim, Michael T Mccann, Marc L Klasky, and Jong Chul Ye · 2022
Earlier work this paper cites.
Sample efficiency matters: a benchmark for practical molecular optimization
Wenhao Gao, Tianfan Fu, Jimeng Sun, and Connor Coley · 2022
Earlier work this paper cites.
Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2022
Earlier work this paper cites.
Decoding gene regulation in the fly brain
Jasper Janssens, Sara Aibar, Ibrahim Ihsan Taskiran, Joy N Ismail, Alicia Estacio Gomez, Gabriel Aughey, Katina I Spanier, Florian V De Rop, Carmen Bravo Gonzalez-Blas, Marc Dionne, et al · 2022
Earlier work this paper cites.
Guided-tts: A diffusion model for text-to-speech via classifier guidance
Heeseung Kim, Sungwon Kim, and Sungroh Yoon · 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.
Digress: Discrete denoising diffusion for graph generation
Clement Vignac, Igor Krawczuk, Antoine Siraudin, Bohan Wang, Volkan Cevher, and Pascal Frossard · 2022
Cited alongside, same era.
Dirichlet diffusion score model for biological sequence generation
Pavel Avdeyev, Chenlai Shi, Yuhao Tan, Kseniia Dudnyk, and Jian Zhou · 2023
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.
Diffusion posterior sampling for linear inverse problem solving: A filtering perspective
Zehao Dou and Yang Song · 2024
Later among the works it cites.
Itai Gat, Tal Remez, Neta Shaul, Felix Kreuk, Ricky TQ Chen, Gabriel Synnaeve, Yossi Adi, and Yaron Lipman · 2024
Later among the works it cites.
Gradient guidance for diffusion models: An optimization perspective
Yingqing Guo, Hui Yuan, Yukang Yang, Minshuo Chen, and Mengdi Wang · 2024
Later among the works it cites.
Symbolic music generation with non-differentiable rule guided diffusion
Yujia Huang, Adishree Ghatare, Yuanzhe Liu, Ziniu Hu, Qinsheng Zhang, Chandramouli S Sastry, Siddharth Gururani, Sageev Oore, and Yisong Yue · 2024
Later among the works it cites.
Optimizing diffusion noise can serve as universal motion priors
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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.
Diffusion policy: Visuomotor policy learning via action diffusion
Cheng Chi, Zhenjia Xu, Siyuan Feng, Eric Cousineau, Yilun Du, Benjamin Burchfiel, Russ Tedrake, and Shuran Song · 2023
Cited alongside, same era.
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.
Manifold preserving guided diffusion
Yutong He, Naoki Murata, Chieh-Hsin Lai, Yuhta Takida, Toshimitsu Uesaka, Dongjun Kim, Wei-Hsiang Liao, Yuki Mitsufuji, J Zico Kolter, Ruslan Salakhutdinov, et al · 2023
Cited alongside, same era.
Generating and imputing tabular data via diffusion and flow-based gradient-boosted trees. arxiv, page 2309.09968, 2023. doi: 10.48550
Alexia Jolicoeur-Martineau, Kilian Fatras, and Tal Kachman · 2023
Cited alongside, same era.
Discrete diffusion language modeling by estimating the ratios of the data distribution
Aaron Lou, Chenlin Meng, and Stefano Ermon · 2023
Cited alongside, same era.
Korrawe Karunratanakul, Konpat Preechakul, Emre Aksan, Thabo Beeler, Supasorn Suwajanakorn, and Siyu Tang · 2024
Later among the works it cites.
Derivative-free guidance in continuous and discrete diffusion models with soft value-based decoding
Xiner Li, Yulai Zhao, Chenyu Wang, Gabriele Scalia, Gokcen Eraslan, Surag Nair, Tommaso Biancalani, Shuiwang Ji, Aviv Regev, Sergey Levine, et al · 2024
Later among the works it cites.
Yaron Lipman, Marton Havasi, Peter Holderrieth, Neta Shaul, Matt Le, Brian Karrer, Ricky TQ Chen, David Lopez-Paz, Heli Ben-Hamu, and Itai Gat · 2024
Later among the works it cites.
Implicit diffusion: Efficient optimization through stochastic sampling
Pierre Marion, Anna Korba, Peter Bartlett, Mathieu Blondel, Valentin De Bortoli, Arnaud Doucet, Felipe Llinares-López, Courtney Paquette, and Quentin Berthet · 2024
Later among the works it cites.
Unlocking guidance for discrete state-space diffusion and flow models
Hunter Nisonoff, Junhao Xiong, Stephan Allenspach, and Jennifer Listgarten · 2024
Later among the works it cites.
Particle denoising diffusion sampler
Angus Phillips, Hai-Dang Dau, Michael John Hutchinson, Valentin De Bortoli, George Deligiannidis, and Arnaud Doucet · 2024
Later among the works it cites.
Understanding and improving training-free loss-based diffusion guidance
Yifei Shen, Xinyang Jiang, Yifan Yang, Yezhen Wang, Dongqi Han, and Dongsheng Li · 2024
Later among the works it cites.
Non-differentiable diffusion guidance for improved molecular geometry
Yuchen Shen, Chenhao Zhang, Chenghui Zhou, Sijie Fu, Newell Washburn, and Barnabas Poczos · 2024
Later among the works it cites.
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
Later among the works it cites.
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
Later among the works it cites.
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
Later among the works it cites.
Chenyu Wang, Masatoshi Uehara, Yichun He, Amy Wang, Tommaso Biancalani, Avantika Lal, Tommi Jaakkola, Sergey Levine, Hanchen Wang, and Aviv Regev · 2024
Later among the works it cites.
Guidance with spherical gaussian constraint for conditional diffusion
Lingxiao Yang, Shutong Ding, Yifan Cai, Jingyi Yu, Jingya Wang, and Ye Shi · 2024
Later among the works it cites.
Tfg: Unified training-free guidance for diffusion models
Haotian Ye, Haowei Lin, Jiaqi Han, Minkai Xu, Sheng Liu, Yitao Liang, Jianzhu Ma, James Zou, and Stefano Ermon · 2024
Later among the works it cites.
Generalized protein pocket generation with prior-informed flow matching
Zaixi Zhang, Marinka Zitnik, and Qi Liu · 2024
Later among the works it cites.
Tfg-flow: Training-free guidance in multimodal generative flow, 2025
Haowei Lin, Shanda Li, Haotian Ye, Yiming Yang, Stefano Ermon, Yitao Liang, and Jianzhu Ma · 2025
Closest in time.
Inference-time scaling for diffusion models beyond scaling denoising steps
Nanye Ma, Shangyuan Tong, Haolin Jia, Hexiang Hu, Yu-Chuan Su, Mingda Zhang, Xuan Yang, Yandong Li, Tommi Jaakkola, Xuhui Jia, et al · 2025
Closest in time.
A general framework for inference-time scaling and steering of diffusion models
Raghav Singhal, Zachary Horvitz, Ryan Teehan, Mengye Ren, Zhou Yu, Kathleen McKeown, and Rajesh Ranganath · 2025
Closest in time.
Feynman-kac correctors in diffusion: Annealing, guidance, and product of experts
Marta Skreta, Tara Akhound-Sadegh, Viktor Ohanesian, Roberto Bondesan, Alán Aspuru-Guzik, Arnaud Doucet, Rob Brekelmans, Alexander Tong, and Kirill Neklyudov · 2025
Closest in time.
Inference-time alignment in diffusion models with reward-guided generation: Tutorial and review
Masatoshi Uehara, Yulai Zhao, Chenyu Wang, Xiner Li, Aviv Regev, Sergey Levine, and Tommaso Biancalani · 2025
Closest in time.