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[1] R. Tweedie,et al. Exponential convergence of Langevin distributions and their discrete approximations , 1996 .
[2] Aapo Hyvärinen,et al. Estimation of Non-Normalized Statistical Models by Score Matching , 2005, J. Mach. Learn. Res..
[3] E. Grafarend. Linear and nonlinear models : fixed effects, random effects, and mixed models , 2006 .
[4] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[5] Pascal Vincent,et al. A Connection Between Score Matching and Denoising Autoencoders , 2011, Neural Computation.
[6] Yee Whye Teh,et al. Bayesian Learning via Stochastic Gradient Langevin Dynamics , 2011, ICML.
[7] B. Efron. Tweedie’s Formula and Selection Bias , 2011, Journal of the American Statistical Association.
[8] Eero P. Simoncelli,et al. Least Squares Estimation Without Priors or Supervision , 2011, Neural Computation.
[9] Yinda Zhang,et al. LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop , 2015, ArXiv.
[10] Xiaogang Wang,et al. Deep Learning Face Attributes in the Wild , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).
[11] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[12] Alex Graves,et al. Conditional Image Generation with PixelCNN Decoders , 2016, NIPS.
[13] Sepp Hochreiter,et al. Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) , 2015, ICLR.
[14] Sepp Hochreiter,et al. GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium , 2017, NIPS.
[15] David Berthelot,et al. BEGAN: Boundary Equilibrium Generative Adversarial Networks , 2017, ArXiv.
[16] Ian D. Reid,et al. RefineNet: Multi-path Refinement Networks for High-Resolution Semantic Segmentation , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[17] Aaron C. Courville,et al. Improved Training of Wasserstein GANs , 2017, NIPS.
[18] Sashank J. Reddi,et al. On the Convergence of Adam and Beyond , 2018, ICLR.
[19] Olivier Bachem,et al. Assessing Generative Models via Precision and Recall , 2018, NeurIPS.
[20] Jaakko Lehtinen,et al. Progressive Growing of GANs for Improved Quality, Stability, and Variation , 2017, ICLR.
[21] Yuichi Yoshida,et al. Spectral Normalization for Generative Adversarial Networks , 2018, ICLR.
[22] Rishi Sharma,et al. A Note on the Inception Score , 2018, ArXiv.
[23] Zengyi Li,et al. Learning Energy-Based Models in High-Dimensional Spaces with Multi-scale Denoising Score Matching , 2019, 1910.07762.
[24] Igor Mordatch,et al. Implicit Generation and Generalization with Energy Based Models , 2018 .
[25] Ali Razavi,et al. Generating Diverse High-Fidelity Images with VQ-VAE-2 , 2019, NeurIPS.
[26] Yang Song,et al. Sliced Score Matching: A Scalable Approach to Density and Score Estimation , 2019, UAI.
[27] Yang Song,et al. Generative Modeling by Estimating Gradients of the Data Distribution , 2019, NeurIPS.
[28] Aapo Hyvärinen,et al. Neural Empirical Bayes , 2019, J. Mach. Learn. Res..
[29] Timo Aila,et al. A Style-Based Generator Architecture for Generative Adversarial Networks , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[30] Michael S. Bernstein,et al. HYPE: A Benchmark for Human eYe Perceptual Evaluation of Generative Models , 2019, NeurIPS.
[31] Stefano Ermon,et al. Bridging the Gap Between $f$-GANs and Wasserstein GANs , 2019, ICML.
[32] Eero P. Simoncelli,et al. Solving Linear Inverse Problems Using the Prior Implicit in a Denoiser , 2020, ArXiv.
[33] Ioannis Mitliagkas,et al. Adversarial score matching and improved sampling for image generation , 2020, ICLR.