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Diffusion models, a powerful and universal generative AI technology, have achieved tremendous success in computer vision, audio, reinforcement learning, and computational biology.
R. A. Bradley and M. E. Terry, “Rank analysis of incomplete block designs: I. the method of paired comparisons,” Biometrika , vol. 39, no. 3/4, pp. 324–345, 1952
1952
Earlier work this paper cites.
B. D. Anderson, “Reverse-time diffusion equation models,” Stochastic Processes and their Applications , vol. 12, no. 3, pp. 313–326, 1982
1982
Earlier work this paper cites.
D. G. Luenberger, Y. Ye et al. , Linear and nonlinear programming . Springer, 1984, vol. 2
1984
Earlier work this paper cites.
U. G. Haussmann and E. Pardoux, “Time reversal of diffusions,” The Annals of Probability , pp. 1188–1205, 1986
1986
Earlier work this paper cites.
S. Geman and C. Graffigne, “Markov random field image models and their applications to computer vision,” in Proceedings of the international congress of mathematicians , vol. 1. Berkeley, CA, 1986, p. 2
1986
Earlier work this paper cites.
G. Cybenko, “Approximation by superpositions of a sigmoidal function,” Mathematics of Control, Signals and Systems , vol. 2, no. 4, pp. 303–314, 1989
1989
Earlier work this paper cites.
K. Hornik, M. Stinchcombe, and H. White, “Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks,” Neural Networks , vol. 3, no. 5, pp. 551–560, 1990
1990
Earlier work this paper cites.
A. R. Barron, “Approximation and estimation bounds for artificial neural networks,” Machine Learning , vol. 14, pp. 115–133, 1994
1994
Earlier work this paper cites.
A. Hyvärinen and P. Dayan, “Estimation of non-normalized statistical models by score matching.” Journal of Machine Learning Research , vol. 6, no. 4, 2005
2005
Earlier work this paper cites.
M. Mezard and A. Montanari, Information, physics, and computation . Oxford University Press, 2009
2009
Earlier work this paper cites.
M. Ranzato, A. Krizhevsky, and G. Hinton, “Factored 3-way restricted boltzmann machines for modeling natural images,” in Proceedings of the thirteenth international conference on artificial intelligence and statistics . JMLR Workshop and Conference Proceedings, 2010, pp. 621–628
2010
Earlier work this paper cites.
E. Delage and Y. Ye, “Distributionally robust optimization under moment uncertainty with application to data-driven problems,” Operations research , vol. 58, no. 3, pp. 595–612, 2010
2010
Earlier work this paper cites.
J. Goh and M. Sim, “Distributionally robust optimization and its tractable approximations,” Operations research , vol. 58, no. 4-part-1, pp. 902–917, 2010
2010
Earlier work this paper cites.
P. Vincent, “A connection between score matching and denoising autoencoders,” Neural computation , vol. 23, no. 7, pp. 1661–1674, 2011
2011
Earlier work this paper cites.
K. J. Åström, Introduction to stochastic control theory . Courier Corporation, 2012
2012
Earlier work this paper cites.
W. H. Fleming and R. W. Rishel, Deterministic and stochastic optimal control . Springer Science & Business Media, 2012, vol. 1
2012
Earlier work this paper cites.
2013
Earlier work this paper cites.
R. Eldan, “Thin shell implies spectral gap up to polylog via a stochastic localization scheme,” Geometric and Functional Analysis , vol. 23, no. 2, pp. 532–569, 2013
2013
Earlier work this paper cites.
J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli, “Deep unsupervised learning using nonequilibrium thermodynamics,” in Proceedings of the International conference on machine learning . PMLR, 2015, pp. 2256–2265
2015
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18 . Springer, 2015, pp. 234–241
2015
Earlier work this paper cites.
P. Agrawal, A. V. Nair, P. Abbeel, J. Malik, and S. Levine, “Learning to poke by poking: Experiential learning of intuitive physics,” Advances in neural information processing systems , vol. 29, 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
A. Creswell, T. White, V. Dumoulin, K. Arulkumaran, B. Sengupta, and A. A. Bharath, “Generative adversarial networks: An overview,” IEEE signal processing magazine , vol. 35, no. 1, pp. 53–65, 2018
2018
Earlier work this paper cites.
D. Yarotsky, “Optimal approximation of continuous functions by very deep relu networks,” in Conference on learning theory . PMLR, 2018, pp. 639–649
2018
Earlier work this paper cites.
R. Eldan, “Gaussian-width gradient complexity, reverse log-sobolev inequalities and nonlinear large deviations,” Geometric and Functional Analysis , vol. 28, no. 6, pp. 1548–1596, 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
D. P. Kingma and P. Dhariwal, “Glow: Generative flow with invertible 1x1 convolutions,” Advances in Neural Information Processing Systems , vol. 31, 2018
2018
Earlier work this paper cites.
D. P. Kingma, M. Welling et al. , “An introduction to variational autoencoders,” Foundations and Trends® in Machine Learning , vol. 12, no. 4, pp. 307–392, 2019
2019
Earlier work this paper cites.
Y. Song and S. Ermon, “Generative modeling by estimating gradients of the data distribution,” Advances in Neural Information Processing Systems , vol. 32, 2019
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
M. Chen, H. Jiang, W. Liao, and T. Zhao, “Efficient approximation of deep relu networks for functions on low dimensional manifolds,” Advances in Neural Information Processing Systems , vol. 32, 2019
2019
Earlier work this paper cites.
Y. Pan, D. Wu, C. Luo, and A. Dolgui, “User activity measurement in rating-based online-to-offline (o2o) service recommendation,” Information Sciences , vol. 479, pp. 180–196, 2019
2019
Earlier work this paper cites.
D. Kuhn, P. M. Esfahani, V. A. Nguyen, and S. Shafieezadeh-Abadeh, “Wasserstein distributionally robust optimization: Theory and applications in machine learning,” in Operations research & management science in the age of analytics . Informs, 2019, pp. 130–166
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial networks,” Communications of the ACM , vol. 63, no. 11, pp. 139–144, 2020
2020
Earlier work this paper cites.
J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in Neural Information Processing Systems , vol. 33, pp. 6840–6851, 2020
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
Y. Song and S. Ermon, “Improved techniques for training score-based generative models,” Advances in neural information processing systems , vol. 33, pp. 12 438–12 448, 2020
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
A. Kumar and S. Levine, “Model inversion networks for model-based optimization,” Advances in Neural Information Processing Systems , vol. 33, pp. 5126–5137, 2020
2020
Earlier work this paper cites.
I. Gühring, G. Kutyniok, and P. Petersen, “Error bounds for approximations with deep relu neural networks in w s, p norms,” Analysis and Applications , vol. 18, no. 05, pp. 803–859, 2020
2020
Earlier work this paper cites.
A. J. Schmidt-Hieber, “Nonparametric regression using deep neural networks with relu activation function,” Annals of Statistics , vol. 48, no. 4, pp. 1875–1897, 2020
2020
Earlier work this paper cites.
Y. Song, S. Garg, J. Shi, and S. Ermon, “Sliced score matching: A scalable approach to density and score estimation,” in Uncertainty in Artificial Intelligence . PMLR, 2020, pp. 574–584
2020
Earlier work this paper cites.
R. Eldan, “Taming correlations through entropy-efficient measure decompositions with applications to mean-field approximation,” Probability Theory and Related Fields , vol. 176, no. 3, pp. 737–755, 2020
2020
Earlier work this paper cites.
Y. Pan and D. Wu, “A novel recommendation model for online-to-offline service based on the customer network and service location,” Journal of Management Information Systems , vol. 37, no. 2, pp. 563–593, 2020
2020
Earlier work this paper cites.
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
Y. Tashiro, J. Song, Y. Song, and S. Ermon, “CSDI: Conditional score-based diffusion models for probabilistic time series imputation,” Advances in Neural Information Processing Systems , vol. 34, pp. 24 804–24 816, 2021
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
E. Hoogeboom, D. Nielsen, P. Jaini, P. Forré, and M. Welling, “Argmax flows and multinomial diffusion: Learning categorical distributions,” Advances in Neural Information Processing Systems , vol. 34, pp. 12 454–12 465, 2021
2021
Earlier work this paper cites.
J. Austin, D. D. Johnson, J. Ho, D. Tarlow, and R. Van Den Berg, “Structured denoising diffusion models in discrete state-spaces,” Advances in Neural Information Processing Systems , vol. 34, pp. 17 981–17 993, 2021
2021
Earlier work this paper cites.
D. Watson, W. Chan, J. Ho, and M. Norouzi, “Learning fast samplers for diffusion models by differentiating through sample quality,” in International Conference on Learning Representations , 2021
2021
Earlier work this paper cites.
A. Q. Nichol and P. Dhariwal, “Improved denoising diffusion probabilistic models,” in Proceedings of the International Conference on Machine Learning . PMLR, 2021, pp. 8162–8171
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
Y.-J. Lu, Y. Tsao, and S. Watanabe, “A study on speech enhancement based on diffusion probabilistic model,” in 2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) . IEEE, 2021, pp. 659–666
2021
Earlier work this paper cites.
M. Janner, Q. Li, and S. Levine, “Offline reinforcement learning as one big sequence modeling problem,” Advances in Neural Information Processing Systems , 2021
2021
Earlier work this paper cites.
L. Chen, K. Lu, A. Rajeswaran, K. Lee, A. Grover, M. Laskin, P. Abbeel, A. Srinivas, and I. Mordatch, “Decision transformer: Reinforcement learning via sequence modeling,” Advances in neural information processing systems , vol. 34, pp. 15 084–15 097, 2021
2021
Earlier work this paper cites.
E. D. Zhong, T. Bepler, B. Berger, and J. H. Davis, “CryoDRGN: reconstruction of heterogeneous cryo-EM structures using neural networks,” Nature Methods , vol. 18, no. 2, pp. 176–185, 2021
2021
Earlier work this paper cites.
J.-E. Shin, A. J. Riesselman, A. W. Kollasch, C. McMahon, E. Simon, C. Sander, A. Manglik, A. C. Kruse, and D. S. Marks, “Protein design and variant prediction using autoregressive generative models,” Nature Communications , vol. 12, no. 1, p. 2403, 2021
2021
Earlier work this paper cites.
C.-W. Huang, J. H. Lim, and A. C. Courville, “A variational perspective on diffusion-based generative models and score matching,” Advances in Neural Information Processing Systems , vol. 34, pp. 22 863–22 876, 2021
2021
Earlier work this paper cites.
A. Vahdat, K. Kreis, and J. Kautz, “Score-based generative modeling in latent space,” Advances in Neural Information Processing Systems , vol. 34, pp. 11 287–11 302, 2021
2021
Earlier work this paper cites.
J. Lu, Z. Shen, H. Yang, and S. Zhang, “Deep network approximation for smooth functions,” SIAM Journal on Mathematical Analysis , vol. 53, no. 5, pp. 5465–5506, 2021
2021
Earlier work this paper cites.
V. De Bortoli, J. Thornton, J. Heng, and A. Doucet, “Diffusion schrödinger bridge with applications to score-based generative modeling,” Advances in Neural Information Processing Systems , vol. 34, pp. 17 695–17 709, 2021
2021
Earlier work this paper cites.
Y. Chen, “An almost constant lower bound of the isoperimetric coefficient in the kls conjecture,” Geometric and Functional Analysis , vol. 31, pp. 34–61, 2021
2021
Earlier work this paper cites.
P. Dhariwal and A. Nichol, “Diffusion models beat gans on image synthesis,” Advances in Neural Information Processing Systems , vol. 34, pp. 8780–8794, 2021
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
Y. Jin, Z. Yang, and Z. Wang, “Is pessimism provably efficient for offline RL?” in International Conference on Machine Learning . PMLR, 2021, pp. 5084–5096
2021
Cited alongside, same era.
R. Huang, Z. Zhao, H. Liu, J. Liu, C. Cui, and Y. Ren, “Prodiff: Progressive fast diffusion model for high-quality text-to-speech,” in Proceedings of the 30th ACM International Conference on Multimedia , 2022, pp. 2595–2605
2022
Cited alongside, same era.
2022
Cited alongside, same era.
O. Avrahami, D. Lischinski, and O. Fried, “Blended diffusion for text-driven editing of natural images,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 18 208–18 218
2022
Cited alongside, same era.
A. Bansal, H.-M. Chu, A. Schwarzschild, S. Sengupta, M. Goldblum, J. Geiping, and T. Goldstein, “Universal guidance for diffusion models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 843–852
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
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G. Kim, T. Kwon, and J. C. Ye, “Diffusionclip: Text-guided diffusion models for robust image manipulation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 2426–2435
2022
Cited alongside, same era.
C. Saharia, W. Chan, S. Saxena, L. Li, J. Whang, E. L. Denton, K. Ghasemipour, R. Gontijo Lopes, B. Karagol Ayan, T. Salimans et al. , “Photorealistic text-to-image diffusion models with deep language understanding,” Advances in Neural Information Processing Systems , vol. 35, pp. 36 479–36 494, 2022
2022
Cited alongside, same era.
X. Li, J. Thickstun, I. Gulrajani, P. S. Liang, and T. B. Hashimoto, “Diffusion-lm improves controllable text generation,” Advances in Neural Information Processing Systems , vol. 35, pp. 4328–4343, 2022
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
A. Güngör, S. U. Dar, Ş. Öztürk, Y. Korkmaz, H. A. Bedel, G. Elmas, M. Ozbey, and T. Çukur, “Adaptive diffusion priors for accelerated MRI reconstruction,” Medical Image Analysis , p. 102872, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
T. Weiss, E. Mayo Yanes, S. Chakraborty, L. Cosmo, A. M. Bronstein, and R. Gershoni-Poranne, “Guided diffusion for inverse molecular design,” Nature Computational Science , pp. 1–10, 2023
2023
Later among the works it cites.
J. L. Watson, D. Juergens, N. R. Bennett, B. L. Trippe, J. Yim, H. E. Eisenach, W. Ahern, A. J. Borst, R. J. Ragotte, and L. F. Milles, “De novo design of protein structure and function with rfdiffusion,” Nature , vol. 620, no. 7976, pp. 1089–1100, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
F.-A. Croitoru, V. Hondru, R. T. Ionescu, and M. Shah, “Diffusion models in vision: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
J. Richter, S. Welker, J.-M. Lemercier, B. Lay, and T. Gerkmann, “Speech enhancement and dereverberation with diffusion-based generative models,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , 2023
2023
Later among the works it cites.
C.-Y. Yu, S.-L. Yeh, G. Fazekas, and H. Tang, “Conditioning and sampling in variational diffusion models for speech super-resolution,” in ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2023, pp. 1–5
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
W. Peebles and S. Xie, “Scalable diffusion models with transformers,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 4195–4205
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
M. Celentano, Z. Fan, and S. Mei, “Local convexity of the tap free energy and amp convergence for z 2-synchronization,” The Annals of Statistics , vol. 51, no. 2, pp. 519–546, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
S. Chen, G. Daras, and A. Dimakis, “Restoration-degradation beyond linear diffusions: A non-asymptotic analysis for ddim-type samplers,” in International Conference on Machine Learning . PMLR, 2023, pp. 4462–4484
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
J. Bu, D. Simchi-Levi, and L. Wang, “Offline pricing and demand learning with censored data,” Management Science , vol. 69, no. 2, pp. 885–903, 2023
2023
Later among the works it cites.
B. Zhu, M. Jordan, and J. Jiao, “Principled reinforcement learning with human feedback from pairwise or k-wise comparisons,” in Proceedings of the International Conference on Machine Learning . PMLR, 2023, pp. 43 037–43 067
2023
Later among the works it cites.
A. Lou and S. Ermon, “Reflected diffusion models,” arXiv preprint arXiv:2304.04740 , 2023
2023
Later among the works it cites.
J. E. Santos, Z. R. Fox, N. Lubbers, and Y. T. Lin, “Blackout diffusion: generative diffusion models in discrete-state spaces,” in Proceedings of the International Conference on Machine Learning . PMLR, 2023, pp. 9034–9059
2023
Later among the works it cites.
2023
Later among the works it cites.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
2024
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J. Benton, V. De Bortoli, A. Doucet, and G. Deligiannidis, “Nearly d d -linear convergence bounds for diffusion models via stochastic localization,” in Proceedings of the International Conference on Learning Representations , 2024
2024
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2024
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2024
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2024
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2024
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L. Rout, N. Raoof, G. Daras, C. Caramanis, A. Dimakis, and S. Shakkottai, “Solving linear inverse problems provably via posterior sampling with latent diffusion models,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
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C. Xu, J. Lee, X. Cheng, and Y. Xie, “Flow-based distributionally robust optimization,” IEEE Journal on Selected Areas in Information Theory , 2024
2024
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Y. Li, J. Guo, R. Wang, and J. Yan, “From distribution learning in training to gradient search in testing for combinatorial optimization,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
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