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Guided sampling is a vital approach for applying diffusion models in real-world tasks that embeds human-defined guidance during the sampling procedure.
Stabilizing off-policy q-learning via bootstrapping error reduction
Kumar, A., Fu, J., Tucker, G., and Levine, S · 1906
Earlier work this paper cites.
Crafting papers on machine learning
Langley, P · 2000
Earlier work this paper cites.
Computer vision , volume 3
Shapiro, L. G., Stockman, G. C., et al · 2001
Earlier work this paper cites.
Training products of experts by minimizing contrastive divergence
Hinton, G. E · 2002
Earlier work this paper cites.
Relative entropy policy search
Peters, J., Mulling, K., and Altun, Y · 2010
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2014
Earlier work this paper cites.
Bayesian inference with posterior regularization and applications to infinite latent svms
Zhu, J., Chen, N., and Xing, E. P · 2014
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S · 2015
Earlier work this paper cites.
Continuous control with deep reinforcement learning
Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N. M. O., Erez, T., Tassa, Y., Silver, D., and Wierstra, D · 2016
Earlier work this paper cites.
Addressing function approximation error in actor-critic methods
Fujimoto, S., van Hoof, H., and Meger, D · 2018
Earlier work this paper cites.
Representation learning with contrastive predictive coding
Oord, A. v. d., Li, Y., and Vinyals, O · 2018
Earlier work this paper cites.
Off-policy deep reinforcement learning without exploration
Fujimoto, S., Meger, D., and Precup, D · 2019
Earlier work this paper cites.
Advantage-weighted regression: Simple and scalable off-policy reinforcement learning
Peng, X. B., Kumar, A., Zhang, G., and Levine, S · 2019
Earlier work this paper cites.
Fine-tuning language models from human preferences
Ziegler, D. M., Stiennon, N., Wu, J., Brown, T. B., Radford, A., Amodei, D., Christiano, P., and Irving, G · 2019
Earlier work this paper cites.
D4rl: Datasets for deep data-driven reinforcement learning
Fu, J., Kumar, A., Nachum, O., Tucker, G., and Levine, S · 2020
Earlier work this paper cites.
Generative adversarial networks
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2020
Earlier work this paper cites.
Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
Earlier work this paper cites.
Conservative Q-learning for offline reinforcement learning
Kumar, A., Zhou, A., Tucker, G., and Levine, S · 2020
Earlier work this paper cites.
Awac: Accelerating online reinforcement learning with offline datasets
Nair, A., Gupta, A., Dalal, M., and Levine, S · 2020
Earlier work this paper cites.
Critic regularized regression
Wang, Z., Novikov, A., Zolna, K., Merel, J. S., Springenberg, J. T., Reed, S. E., Shahriari, B., Siegel, N., Gulcehre, C., Heess, N., and de Freitas, N · 2020
Earlier work this paper cites.
Xiao, Z., Yan, Q., and Amit, Y · 2020
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Diffusion models beat GANs on image synthesis
Dhariwal, P. and Nichol, A. Q · 2021
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Classifier-free diffusion guidance
Ho, J. and Salimans, T · 2021
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Variational diffusion models
Kingma, D. P., Salimans, T., Poole, B., and Ho, J · 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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Diffusion-LM improves controllable text generation
Li, X. L., Thickstun, J., Gulrajani, I., Liang, P., and Hashimoto, T. B · 2022
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Diffsinger: Singing voice synthesis via shallow diffusion mechanism
Liu, J., Li, C., Ren, Y., Chen, F., and Zhao, Z · 2022
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DiffuseVAE: Efficient, controllable and high-fidelity generation from low-dimensional latents
Pandey, K., Mukherjee, A., Rai, P., and Kumar, A · 2022
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Imitating human behaviour with diffusion models
Pearce, T., Rashid, T., Kanervisto, A., Bignell, D., Sun, M., Georgescu, R., Macua, S. V., Tan, S. Z., Momennejad, I., Hofmann, K., et al · 2022
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Dreamfusion: Text-to-3d using 2d diffusion
Poole, B., Jain, A., Barron, J. T., and Mildenhall, B · 2022
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Nie, W., Vahdat, A., and Anandkumar, A · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al · 2021
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Is conditional generative modeling all you need for decision-making?
Ajay, A., Du, Y., Gupta, A., Tenenbaum, J., Jaakkola, T., and Agrawal, P · 2022
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Offline reinforcement learning via high-fidelity generative behavior modeling
Chen, H., Lu, C., Ying, C., Su, H., and Zhu, J · 2022
Cited alongside, same era.
Diffusion posterior sampling for general noisy inverse problems
Chung, H., Kim, J., Mccann, M. T., Klasky, M. L., and Ye, J. C · 2022
Cited alongside, same era.
Know your boundaries: The necessity of explicit behavioral cloning in offline rl
Goo, W. and Niekum, S · 2022
Cited alongside, same era.
Diffusion models as plug-and-play priors
Graikos, A., Malkin, N., Jojic, N., and Samaras, D · 2022
Cited alongside, same era.
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Hierarchical text-conditional image generation with CLIP latents
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., and Chen, M · 2022
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High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022
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Palette: Image-to-image diffusion models
Saharia, C., Chan, W., Chang, H., Lee, C., Ho, J., Salimans, T., Fleet, D., and Norouzi, M · 2022
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Progressive distillation for fast sampling of diffusion models
Salimans, T. and Ho, J · 2022
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Lossy compression with gaussian diffusion
Theis, L., Salimans, T., Hoffman, M. D., and Mentzer, F · 2022
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Diffusion-based molecule generation with informative prior bridges
Wu, L., Gong, C., Liu, X., Ye, M., and Liu, Q · 2022
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Geodiff: A geometric diffusion model for molecular conformation generation
Xu, M., Yu, L., Song, Y., Shi, C., Ermon, S., and Tang, J · 2022
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Diffusion probabilistic modeling for video generation
Yang, R., Srivastava, P., and Mandt, S · 2022
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Lion: Latent point diffusion models for 3d shape generation
Zeng, X., Vahdat, A., Williams, F., Gojcic, Z., Litany, O., Fidler, S., and Kreis, K · 2022
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Fast sampling of diffusion models with exponential integrator
Zhang, Q. and Chen, Y · 2022
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gddim: Generalized denoising diffusion implicit models
Zhang, Q., Tao, M., and Chen, Y · 2022
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Egsde: Unpaired image-to-image translation via energy-guided stochastic differential equations
Zhao, M., Bao, F., Li, C., and Zhu, J · 2022
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Deep generative modeling on limited data with regularization by nontransferable pre-trained models
Zhong, Y., Liu, H., Liu, X., Bao, F., Shen, W., and Li, C · 2022
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Magicvideo: Efficient video generation with latent diffusion models
Zhou, D., Wang, W., Yan, H., Lv, W., Zhu, Y., and Feng, J · 2022
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