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Generative diffusion models have recently emerged as a leading approach for generating high-dimensional data.
Dynamical model of elementary particles based on an analogy with superconductivity. i
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Plasmons, gauge invariance, and mass
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Broken symmetry and the mass of gauge vector mesons
Englert, F. and Brout, R. (1964) · 1964
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Broken symmetries and the masses of gauge bosons
Higgs, P. W. (1964) · 1964
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Invariant variation problems
Noether, E. (1971) · 1971
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Phase transitions and critical phenomena
Stanley, H. E. (1971) · 1971
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Reverse-time diffusion equation models
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The role of symmetry in fundamental physics
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Path integrals and symmetry breaking for optimal control theory
Kappen, H. J. (2005) · 2005
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Wavegrad: Estimating gradients for waveform generation
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Diffwave: A versatile diffusion model for audio synthesis
Kong, Z., Ping, W., Huang, J., Zhao, K., and Catanzaro, B. (2020) · 2009
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Denoising diffusion implicit models
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A connection between score matching and denoising autoencoders
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Nice: Non-linear independent components estimation
Dinh, L., Krueger, D., and Bengio, Y. (2014) · 2014
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Dynamics of the standard model
Donoghue, J. F., Golowich, E., and Holstein, B. R. (2014) · 2014
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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) · 2014
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M. (2014) · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J., Mohamed, S., and Wierstra, D. (2014) · 2014
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Critical dynamics: a field theory approach to equilibrium and non-equilibrium scaling behavior
Täuber, U. C. (2014) · 2014
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Variational inference with normalizing flows
Rezende, D. and Mohamed, S. (2015) · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S. (2015) · 2015
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Normalizing flows on tori and spheres
Rezende, D. J., Papamakarios, G., Racaniere, S., Albergo, M., Kanwar, G., Shanahan, P., and Cranmer, K. (2020) · 2020
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Lightface: A hybrid deep face recognition framework
Serengil, S. I. and Ozpinar, A. (2020) · 2020
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Catastrophic forgetting and mode collapse in gans
Thanh-Tung, H. and Tran, T. (2020) · 2020
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Sampling using SU ( n ) \mathrm{SU}(n) gauge equivariant flows
Boyda, D., Kanwar, G., Racanière, S., Rezende, D. J., Albergo, M. S., Cranmer, K., Hackett, D. C., and Shanahan, P. E. (2021) · 2021
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A program to build e (n)-equivariant steerable cnns
Cesa, G., Lang, L., and Weiler, M. (2021) · 2021
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E (n) equivariant graph neural networks
Satorras, V. G., Hoogeboom, E., and Welling, M. (2021) · 2021
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Group equivariant convolutional networks
Cohen, T. and Welling, M. (2016) · 2016
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Improved techniques for training gans
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X. (2016) · 2016
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Pixel recurrent neural networks
Van Den Oord, A., Kalchbrenner, N., and Kavukcuoglu, K. (2016) · 2016
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Are gans created equal? a large-scale study
Lucic, M., Kurach, K., Michalski, M., Gelly, S., and Bousquet, O. (2018) · 2018
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Cubenet: Equivariance to 3d rotation and translation
Worrall, D. and Brostow, G. (2018) · 2018
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Lorentz group equivariant neural network for particle physics
Bogatskiy, A., Anderson, B., Offermann, J., Roussi, M., Miller, D., and Kondor, R. (2020) · 2020
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Score-based generative modeling through stochastic differential equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B. (2021) · 2021
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Weiler, M., Forré, P., Verlinde, E., and Welling, M. (2021) · 2021
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An optimal control perspective on diffusion-based generative modeling
Berner, J., Richter, L., and Ullrich, K. (2022) · 2022
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Ho, J., Salimans, T., Gritsenko, A., Chan, W., Norouzi, M., and Fleet, D. J. (2022) · 2022
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Equivariant diffusion for molecule generation in 3d
Hoogeboom, E., Satorras, V. G., Vignac, C., and Welling, M. (2022) · 2022
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Pseudo numerical methods for diffusion models on manifolds
Liu, L., Ren, Y., Lin, Z., and Zhao, Z. (2022) · 2022
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Make-a-video: Text-to-video generation without text-video data
Singer, U., Polyak, A., Hayes, T., Yin, X., An, J., Zhang, S., Hu, Q., Yang, H., Ashual, O., Gafni, O., et al. (2022) · 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) · 2022
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Audioldm: Text-to-audio generation with latent diffusion models
Liu, H., Chen, Z., Yuan, Y., Mei, X., Liu, X., Mandic, D., Wang, W., and Plumbley, M. D. (2023) · 2023
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