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Diffusion models achieve state-of-the-art performance in various generation tasks.
On generalization bounds of a family of recurrent neural networks
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Plug and play language models: A simple approach to controlled text generation
Dathathri, S · 1912
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Reverse-time diffusion equation models
Anderson, B. D · 1982
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Time reversal of diffusions
Haussmann, U. G · 1986
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Approximation by superpositions of a sigmoidal function
Cybenko, G · 1989
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Universal approximation bounds for superpositions of a sigmoidal function
Barron, A. R · 1993
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Some inequalities of the incomplete gamma and related functions
Qi, F · 1999
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Nonlinear dimensionality reduction by locally linear embedding
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A global geometric framework for nonlinear dimensionality reduction
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Generative modeling with denoising auto-encoders and langevin sampling
Block, A · 2002
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Statistical guarantees of generative adversarial networks for distribution estimation
Chen, M · 2002
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Score-based generative modeling through stochastic differential equations
Song, Y · 2011
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A connection between score matching and denoising autoencoders
Vincent, P · 2011
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Generative adversarial nets
Goodfellow, I · 2014
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Variational inference with normalizing flows
Rezende, D · 2015
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U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J · 2015
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Brownian motion, martingales, and stochastic calculus
Le Gall, J.-F · 2016
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Nonparametric regression using deep neural networks with relu activation function
Schmidt-Hieber, J · 2017
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Attention is all you need
Vaswani, A · 2017
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Adaptive approximation and generalization of deep neural network with intrinsic dimensionality
Nakada, R · 2020
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Improved techniques for training score-based generative models
Song, Y · 2020
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Diffusion schrödinger bridge with applications to score-based generative modeling
De Bortoli, V · 2021
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Regularisation of neural networks by enforcing lipschitz continuity
Gouk, H · 2021
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Kim, D · 2021
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Training robust neural networks using lipschitz bounds
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Error bounds for approximations with deep relu networks
Yarotsky, D · 2017
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Suzuki, T · 2018
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High-dimensional probability: An introduction with applications in data science
Vershynin, R · 2018
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Lipschitz regularity of deep neural networks: analysis and efficient estimation
Virmaux, A · 2018
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Generative modeling by estimating gradients of the data distribution
Song, Y · 2019
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High-dimensional statistics: A non-asymptotic viewpoint
Wainwright, M. J · 2019
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Pauli, P · 2021
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The intrinsic dimension of images and its impact on learning
Pope, P · 2021
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Score-based generative modeling in latent space
Vahdat, A · 2021
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Convergence of denoising diffusion models under the manifold hypothesis
De Bortoli, V · 2022
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Let us build bridges: Understanding and extending diffusion generative models
Liu, X · 2022
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Score-based generative models detect manifolds
Pidstrigach, J · 2022
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Hierarchical text-conditional image generation with clip latents
Ramesh, A · 2022
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High-resolution image synthesis with latent diffusion models
Rombach, R · 2022
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Optimal approximation rate of relu networks in terms of width and depth
Shen, Z · 2022
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