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Deep generative models have made tremendous progress in modeling complex data, often exhibiting generation quality that surpasses a typical human's ability to discern the authenticity of samples.
Inequalities: theory of majorization and its applications
A. W. Marshall, I. Olkin, and B. C. Arnold · 1979
Earlier work this paper cites.
Fundamentals of differential geometry
S. Lang · 1999
Earlier work this paper cites.
Convex optimization
S. Boyd and L. Vandenberghe · 2004
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A. Krizhevsky and G. Hinton · 2009
Earlier work this paper cites.
Optimal transport: old and new , volume 338
C. Villani et al · 2009
Earlier work this paper cites.
Variational inference with normalizing flows
D. Rezende and S. Mohamed · 2015
Earlier work this paper cites.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter · 2017
Earlier work this paper cites.
Data augmentation generative adversarial networks
A. Antreas, S. Amos, and E. Harrison · 2018
Earlier work this paper cites.
Neural ordinary differential equations
R. T. Chen, Y. Rubanova, J. Bettencourt, and D. K. Duvenaud · 2018
Earlier work this paper cites.
Ffjord: Free-form continuous dynamics for scalable reversible generative models
W. Grathwohl, R. T. Chen, J. Bettencourt, I. Sutskever, and D. Duvenaud · 2018
Earlier work this paper cites.
Assessing generative models via precision and recall
M. S.-M. Sajjadi, O. Bachem, M. Lucic, O. Bousquet, and S. Gelly · 2018
Earlier work this paper cites.
Generalization error in deep learning
D. Jakubovitz, R. Giryes, and M. R. Rodrigues · 2019
Earlier work this paper cites.
A style-based generator architecture for generative adversarial networks
T. Karras, S. Laine, and T. Aila · 2019
Earlier work this paper cites.
An introduction to variational autoencoders
D. P. Kingma and M. Welling · 2019
Earlier work this paper cites.
Improved precision and recall metric for assessing generative models
T. Kynkäänniemi, T. Karras, S. Laine, J. Lehtinen, and T. Aila · 2019
Earlier work this paper cites.
Multivariate gaussian variational inference by natural gradient descent
T. D. Barfoot · 2020
Earlier work this paper cites.
Language models are few-shot learners
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
J. Ho, A. Jain, and P. Abbeel · 2020
Cited alongside, same era.
Scaling laws for neural language models
J. Kaplan, S. McCandlish, T. Henighan, T. B. Brown, B. Chess, R. Child, S. Gray, A. Radford, J. Wu, and D. Amodei · 2020
Cited alongside, same era.
Performative prediction
J. Perdomo, T. Zrnic, C. Mendler-Dünner, and M. Hardt · 2020
Cited alongside, same era.
Coupling-based invertible neural networks are universal diffeomorphism approximators
T. Teshima, I. Ishikawa, K. Tojo, K. Oono, M. Ikeda, and M. Sugiyama · 2020
Cited alongside, same era.
Understanding estimation and generalization error of generative adversarial networks
R. Anil, A. M. Dai, O. Firat, M. Johnson, D. Lepikhin, A. Passos, S. Shakeri, E. Taropa, P. Bailey, Z. Chen, et al · 2023
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Sparks of artificial general intelligence: Early experiments with gpt-4
S. Bubeck, V. Chandrasekaran, R. Eldan, J. Gehrke, E. Horvitz, E. Kamar, P. Lee, Y. T. Lee, Y. Li, S. Lundberg, et al · 2023
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Will large-scale generative models corrupt future datasets?
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Feature likelihood score: Evaluating generalization of generative models using samples, 2023
M. Jiralerspong, A. J. Bose, I. Gemp, C. Qin, Y. Bachrach, and G. Gidel · 2023
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Midjourney · 2023
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Cited alongside, same era.
Zero-shot text-to-image generation
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Cited alongside, same era.
Score-based generative modeling through stochastic differential equations
Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole · 2021
Cited alongside, same era.
Palm: Scaling language modeling with pathways
A. Chowdhery, S. Narang, J. Devlin, M. Bosma, G. Mishra, A. Roberts, P. Barham, H. W. Chung, C. Sutton, S. Gehrmann, et al · 2022
Cited alongside, same era.
A survey for in-context learning
Q. Dong, L. L. Li, D. Dai, C. Zheng, Z. Wu, B. Chang, X. Sun, J. Xu, and Z. Sui · 2022
Cited alongside, same era.
Elucidating the design space of diffusion-based generative models
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