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Discrete diffusion models are a class of generative models that produce samples from an approximated data distribution within a discrete state space.
Generative modeling by estimating gradients of the data distribution, 2020
Song, Y. and Ermon, S · 1907
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Crafting papers on machine learning
Langley, P · 2000
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Deberta: Decoding-enhanced bert with disentangled attention, 2021
He, P., Liu, X., Gao, J., and Chen, W · 2006
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Denoising diffusion probabilistic models, 2020
Ho, J., Jain, A., and Abbeel, P · 2006
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Score-based generative modeling through stochastic differential equations, 2021
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B · 2011
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Some properties of path measures
Léonard, C · 2014
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U-net: Convolutional networks for biomedical image segmentation, 2015
Ronneberger, O., Fischer, P., and Brox, T · 2015
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Weighting a resampled particle in sequential monte carlo
Martino, L., Elvira, V., and Louzada, F · 2016
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Wu, Y., Schuster, M., Chen, Z., Le, Q. V., Norouzi, M., Macherey, W., Krikun, M., Cao, Y., Gao, Q., Macherey, K., Klingner, J., Shah, A., Johnson, M., Liu, X., Łukasz Kaiser, Gouws, S., Kato, Y., Kudo, T., Kazawa, H., Stevens, K., Kurian, G., Patil, N., Wang, W., Young, C., Smith, J., Riesa, J., Rudnick, A., Vinyals, O., Corrado, G., Hughes, M., and Dean, J · 2016
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Stochastic processes: From applications to theory
Del Moral, P. and Penev, S · 2017
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Bertscore: Evaluating text generation with bert
Zhang*, T., Kishore*, V., Wu*, F., Weinberger, K. Q., and Artzi, Y · 2020
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Diffusion models beat gans on image synthesis, 2021
Dhariwal, P. and Nichol, A · 2021
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DExperts: Decoding-time controlled text generation with experts and anti-experts
Liu, A., Sap, M., Lu, X., Swayamdipta, S., Bhagavatula, C., Smith, N. A., and Choi, Y · 2021
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A continuous time framework for discrete denoising models, 2022
Campbell, A., Benton, J., Bortoli, V. D., Rainforth, T., Deligiannidis, G., and Doucet, A · 2022
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Equivariant diffusion for molecule generation in 3d, 2022
Hoogeboom, E., Satorras, V. G., Vignac, C., and Welling, M · 2022
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High-resolution image synthesis with latent diffusion models, 2022
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022
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Building normalizing flows with stochastic interpolants, 2023
Albergo, M. S. and Vanden-Eijnden, E · 2023
Cited alongside, same era.
Stochastic interpolants: A unifying framework for flows and diffusions, 2023
Albergo, M. S., Boffi, N. M., and Vanden-Eijnden, E · 2023
Cited alongside, same era.
Stable video diffusion: Scaling latent video diffusion models to large datasets, 2023
Blattmann, A., Dockhorn, T., Kulal, S., Mendelevitch, D., Kilian, M., Lorenz, D., Levi, Y., English, Z., Voleti, V., Letts, A., Jampani, V., and Rombach, R · 2023
Cited alongside, same era.
Efficient training of energy-based models using jarzynski equality
Carbone, D., Hua, M., Coste, S., and Vanden-Eijnden, E · 2023
Cited alongside, same era.
Dpok: Reinforcement learning for fine-tuning text-to-image diffusion models
Fan, Y., Watkins, O., Du, Y., Liu, H., Ryu, M., Boutilier, C., Abbeel, P., Ghavamzadeh, M., Lee, K., and Lee, K · 2023
What does guidance do? a fine-grained analysis in a simple setting, 2024
Chidambaram, M., Gatmiry, K., Chen, S., Lee, H., and Lu, J · 2024
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Directly fine-tuning diffusion models on differentiable rewards, 2024
Clark, K., Vicol, P., Swersky, K., and Fleet, D. J · 2024
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Equivariant neural diffusion for molecule generation
Cornet, F. R. J., Bartosh, G., Schmidt, M. N., and Naesseth, C. A · 2024
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DEFT: efficient finetuning of conditional diffusion models by learning the generalised h-transform
Denker, A., Vargas, F., Padhy, S., Didi, K., Mathis, S. V., Dutordoir, V., Barbano, R., Mathieu, E., Komorowska, U. J., and Lio, P · 2024
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Training-free guidance for discrete diffusion models for molecular generation, 2024
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Cited alongside, same era.
Likelihood-based diffusion language models, 2023
Gulrajani, I. and Hashimoto, T. B · 2023
Cited alongside, same era.
Flow matching for generative modeling, 2023
Lipman, Y., Chen, R. T. Q., Ben-Hamu, H., Nickel, M., and Le, M · 2023
Cited alongside, same era.
Sdxl: Improving latent diffusion models for high-resolution image synthesis, 2023
Podell, D., English, Z., Lacey, K., Blattmann, A., Dockhorn, T., Müller, J., Penna, J., and Rombach, R · 2023
Cited alongside, same era.
Vargas, F., Grathwohl, W., and Doucet, A · 2023
Cited alongside, same era.
Digress: Discrete denoising diffusion for graph generation
Vignac, C., Krawczuk, I., Siraudin, A., Wang, B., Cevher, V., and Frossard, P · 2023
Cited alongside, same era.
De novo design of protein structure and function with rfdiffusion
Watson, J. L., Juergens, D., Bennett, N. R., Trippe, B. L., Yim, J., Eisenach, H. E., Ahern, W., Borst, A. J., Ragotte, R. J., Milles, L. F., et al · 2023
Cited alongside, same era.
Nets: A non-equilibrium transport sampler
Albergo, M. S. and Vanden-Eijnden, E · 2024
Cited alongside, same era.
Kerby, T. J. and Moon, K. R · 2024
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Li, X., Zhao, Y., Wang, C., Scalia, G., Eraslan, G., Nair, S., Biancalani, T., Ji, S., Regev, A., Levine, S., and Uehara, M · 2024
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Flow matching guide and code, 2024
Lipman, Y., Havasi, M., Holderrieth, P., Shaul, N., Le, M., Karrer, B., Chen, R. T. Q., Lopez-Paz, D., Ben-Hamu, H., and Gat, I · 2024
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Discrete diffusion language modeling by estimating the ratios of the data distribution, 2024
Lou, A., Meng, C., and Ermon, S · 2024
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Unlocking guidance for discrete state-space diffusion and flow models, 2024
Nisonoff, H., Xiong, J., Allenspach, S., and Listgarten, J · 2024
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Simplified and generalized masked diffusion for discrete data
Shi, J., Han, K., Wang, Z., Doucet, A., and Titsias, M · 2024
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Transport meets variational inference: Controlled monte carlo diffusions
Vargas, F., Nusken, N., Padhy, S., and Blessing, D · 2024
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Amortizing intractable inference in diffusion models for vision, language, and control
Venkatraman, S., Jain, M., Scimeca, L., Kim, M., Sendera, M., Hasan, M., Rowe, L., Mittal, S., Lemos, P., Bengio, E., Adam, A., Rector-Brooks, J., Bengio, Y., Berseth, G., and Malkin, N · 2024
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Practical and asymptotically exact conditional sampling in diffusion models
Wu, L., Trippe, B., Naesseth, C., Blei, D., and Cunningham, J. P · 2024
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Domingo-Enrich, C., Drozdzal, M., Karrer, B., and Chen, R. T. Q · 2025
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Uehara, M., Zhao, Y., Wang, C., Li, X., Regev, A., Levine, S., and Biancalani, T · 2025
Closest in time.