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Diffusion models have demonstrated exceptional performances in various fields of generative modeling, but suffer from slow sampling speed due to their iterative nature.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Williams, R. J · 1992
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Approximate accelerated stochastic simulation of chemically reacting systems
Gillespie, D. T · 2001
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Monte Carlo methods in financial engineering , volume 53
Glasserman, P · 2004
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Elements of information theory
Cover, T. M. and Thomas, J. A · 2006
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Natural language processing with Python: analyzing text with the natural language toolkit
Bird, S., Klein, E., and Loper, E · 2009
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
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Continuous-time Markov chains: An applications-oriented approach
Anderson, W. J · 2012
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Auto-encoding variational Bayes
Kingma, D. P. and Welling, M · 2014
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Continuous function with continuous one-sided derivative
von Eitzen, H · 2014
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S · 2015
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beta-VAE: Learning basic visual concepts with a constrained variational framework
Higgins, I., Matthey, L., Pal, A., Burgess, C., Glorot, X., Botvinick, M., Mohamed, S., and Lerchner, A · 2017
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Control functionals for monte carlo integration
Oates, C. J., Girolami, M., and Chopin, N · 2017
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Neural discrete representation learning
van den Oord, A., Vinyals, O., and Kavukcuoglu, K · 2017
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Sigmoid-weighted linear units for neural network function approximation in reinforcement learning
Elfwing, S., Uchibe, E., and Doya, K · 2018
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A call for clarity in reporting BLEU scores
Post, M · 2018
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Texygen: A benchmarking platform for text generation models
Zhu, Y., Lu, S., Zheng, L., Guo, J., Zhang, W., Wang, J., and Yu, Y · 2018
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Openwebtext corpus
Gokaslan, A. and Cohen, V · 2019
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GPT-2 output dataset, 2019
OpenAI · 2019
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Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., and Sutskever, I · 2019
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InfoVAE: Balancing learning and inference in variational autoencoders
Zhao, S., Song, J., and Ermon, S · 2019
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
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Structured denoising diffusion models in discrete state-spaces
Austin, J., Johnson, D. D., Ho, J., Tarlow, D., and Van Den Berg, R · 2021
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Accurately computing the log-sum-exp and softmax functions
Blanchard, P., Higham, D. J., and Higham, N. J · 2021
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Wavegrad: Estimating gradients for waveform generation
Chen, N., Zhang, Y., Zen, H., Weiss, R. J., Norouzi, M., and Chan, W · 2021
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Taming transformers for high-resolution image synthesis
Esser, P., Rombach, R., and Ommer, B · 2021
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Argmax flows and multinomial diffusion: Learning categorical distributions
Hoogeboom, E., Nielsen, D., Jaini, P., Forré, P., and Welling, M · 2021
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Stable video diffusion: Scaling latent video diffusion models to large datasets
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
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On the equivalence of consistency-type models: Consistency models, consistent diffusion models, and fokker-planck regularization
Lai, C.-H., Takida, Y., Uesaka, T., Murata, N., Mitsufuji, Y., and Ermon, S · 2023
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On distillation of guided diffusion models
Meng, C., Rombach, R., Gao, R., Kingma, D., Ermon, S., Ho, J., and Salimans, T · 2023
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Improved techniques for training consistency models
Song, Y. and Dhariwal, P · 2023
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Consistency models
Song, Y., Dhariwal, P., Chen, M., and Sutskever, I · 2023
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Diffwave: A versatile diffusion model for audio synthesis
Kong, Z., Ping, W., Huang, J., Zhao, K., and Catanzaro, B · 2021
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Knowledge distillation in iterative generative models for improved sampling speed
Luhman, E. and Luhman, T · 2021
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Glide: Towards photorealistic image generation and editing with text-guided diffusion models
Nichol, A., Dhariwal, P., Ramesh, A., Shyam, P., Mishkin, P., McGrew, B., Sutskever, I., and Chen, M · 2021
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Mauve: Measuring the gap between neural text and human text using divergence frontiers
Pillutla, K., Swayamdipta, S., Zellers, R., Thickstun, J., Welleck, S., Choi, Y., and Harchaoui, Z · 2021
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A continuous time framework for discrete denoising models
Campbell, A., Benton, J., De Bortoli, V., Rainforth, T., Deligiannidis, G., and Doucet, A · 2022
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A short note on an inequality between KL and TV
Canonne, C. L · 2022
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Score-based continuous-time discrete diffusion models
Sun, H., Yu, L., Dai, B., Schuurmans, D., and Dai, H · 2023
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Fast sampling of diffusion models with exponential integrator
Zhang, Q. and Chen, Y · 2023
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On-policy distillation of language models: Learning from self-generated mistakes
Agarwal, R., Vieillard, N., Zhou, Y., Stanczyk, P., Garea, S. R., Geist, M., and Bachem, O · 2024
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Comunità, M., Zhong, Z., Takahashi, A., Yang, S., Zhao, M., Saito, K., Ikemiya, Y., Shibuya, T., Takahashi, S., and Mitsufuji, Y · 2024
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Consistent diffusion models: Mitigating sampling drift by learning to be consistent
Daras, G., Dagan, Y., Dimakis, A., and Daskalakis, C · 2024
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Fast timing-conditioned latent audio diffusion
Evans, Z., Carr, C., Taylor, J., Hawley, S. H., and Pons, J · 2024
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Consistency trajectory models: Learning probability flow ODE trajectory of diffusion
Kim, D., Lai, C.-H., Liao, W.-H., Murata, N., Takida, Y., Uesaka, T., He, Y., Mitsufuji, Y., and Ermon, S · 2024
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Soft mixture denoising: Beyond the expressive bottleneck of diffusion models
Li, Y., van Breugel, B., and van der Schaar, M · 2024
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Liu, A., Broadrick, O., Niepert, M., and Broeck, G. V. d · 2024
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Discrete diffusion modeling by estimating the ratios of the data distribution
Lou, A., Meng, C., and Ermon, S · 2024
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Simple and effective masked diffusion language models
Sahoo, S. S., Arriola, M., Schiff, Y., Gokaslan, A., Marroquin, E., Chiu, J. T., Rush, A., and Kuleshov, V · 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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Beyond autoregression: Fast LLMs via self-distillation through time
Deschenaux, J. and Gulcehre, C · 2025
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Your absorbing discrete diffusion secretly models the conditional distributions of clean data
Ou, J., Nie, S., Xue, K., Zhu, F., Sun, J., Li, Z., and Li, C · 2025
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Jump your steps: Optimizing sampling schedule of discrete diffusion models
Park, Y.-H., Lai, C.-H., Hayakawa, S., Takida, Y., and Mitsufuji, Y · 2025
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Energy-based diffusion language models for text generation
Xu, M., Geffner, T., Kreis, K., Nie, W., Xu, Y., Leskovec, J., Ermon, S., and Vahdat, A · 2025
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