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Diffusion models have become a leading paradigm in generative AI, with score estimation via denoising score matching as a central component.
Time reversal of diffusions
Ulrich G Haussmann and Etienne Pardoux · 1986
Earlier work this paper cites.
Real analysis: modern techniques and their applications
Gerald B Folland · 1999
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Some inequalities of the incomplete Gamma and related functions
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Rademacher and Gaussian complexities: Risk bounds and structural results
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Generative adversarial nets
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Auto-encoding variational Bayes
Diederik P Kingma and Max Welling · 2014
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Adam: A method for stochastic optimization
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Variational inference with normalizing flows
Danilo Rezende and Shakir Mohamed · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
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Breaking the curse of dimensionality with convex neural networks
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SGDR: Stochastic gradient descent with warm restarts
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Early stopping for kernel boosting algorithms: A general analysis with localized complexities
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Energy-based generative adversarial network
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Neural temporal-difference learning converges to global optima
Qi Cai, Zhuoran Yang, Jason D Lee, and Zhaoran Wang · 2019
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Gradient descent finds global minima of deep neural networks
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Gradient descent provably optimizes over-parameterized neural networks
Simon S. Du, Xiyu Zhai, Barnabas Poczos, and Aarti Singh · 2019
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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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High-dimensional statistics: A non-asymptotic viewpoint
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Plug and play language models: A simple approach to controlled text generation
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Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Gradient descent with early stopping is provably robust to label noise for overparameterized neural networks
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Towards non-asymptotic convergence for diffusion-based generative models
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Neural policy gradient methods: Global optimality and rates of convergence
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Gradient descent optimizes over-parameterized deep ReLU networks
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Deep learning: a statistical viewpoint
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Analyzing the discrepancy principle for kernelized spectral filter learning algorithms
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Diffusion Schrödinger bridge with applications to score-based generative modeling
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A good score does not lead to a good generative model
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