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Existing Score-Based Models (SBMs) can be categorized into constrained SBMs (CSBMs) or unconstrained SBMs (USBMs) according to their parameterization approaches.
A Family of Embedded Runge-Kutta Formulae
Dormand, J. R. and Prince, P. J · 1980
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A Stochastic Estimator of the Trace of the Influence Matrix for Laplacian Smoothing Splines
Hutchinson, M. F · 1989
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Exponential Convergence of Langevin Distributions and Their Discrete Approximations
Roberts, G. O. and Tweedie, R. L · 1996
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Optimal Scaling of Discrete Approximations to Langevin Diffusions
Roberts, G. O. and Rosenthal, J. S · 1998
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Evaluating Derivatives - Principles and Techniques of Algorithmic Differentiation, Second Edition
Griewank, A. and Walther, A · 2000
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Estimation of Non-Normalized Statistical Models by Score Matching
Hyvärinen, A · 2005
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Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A. and Hinton, G · 2009
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Reading Digits in Natural Images with Unsupervised Feature Learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., and Ng, A. Y · 2011
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A Connection between Score Matching and Denoising Autoencoders
Vincent, P · 2011
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Bayesian Learning via Stochastic Gradient Langevin Dynamics
Welling, M. and Teh, Y. W · 2011
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Estimating the Hessian by Back-Propagating Curvature
Martens, J., Sutskever, I., and Swersky, K · 2012
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On Autoencoder Scoring
Kamyshanska, H. and Memisevic, R · 2013
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What Regularized Auto-Encoders Learn from the Data-Generating Distribution
Alain, G. and Bengio, Y · 2014
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Stochastic Gradient Hamiltonian Monte Carlo
Chen, T., Fox, E., and Guestrin, C · 2014
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The Potential Energy of an Autoencoder
Kamyshanska, H. and Memisevic, R · 2015
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Adam: A Method for Stochastic Optimization
Kingma, D. P. and Ba, J · 2015
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Chapter 1 - Vectors and Matrices
Andrilli, S. and Hecker, D · 2016
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Conservativeness of Untied Auto-Encoders
Im, D. J., Belghazi, M. I., and Memisevic, R · 2016
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Pixel Recurrent Neural Networks
Van Oord, A., Kalchbrenner, N., and Kavukcuoglu, K · 2016
Cited alongside, same era.
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
On Approximating ∇ f \nabla f with Neural Networks
Saremi, S · 2019
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Generative Modeling by Estimating Gradients of the Data Distribution
Song, Y. and Ermon, S · 2019
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Sliced Score Matching: A Scalable Approach to Density and Score Estimation
Song, Y., Garg, S., Shi, J., and Ermon, S · 2019
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Denoising Diffusion Probabilistic Models
Ho, J., Jain, A., and Abbeel, P · 2020
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Reliable Fidelity and Diversity Metrics for Generative Models
Naeem, M. F., Oh, S. J., Uh, Y., Choi, Y., and Yoo, J · 2020
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Improved Techniques for Training Score-Based Generative Models
Song, Y. and Ermon, S · 2020
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Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S · 2017
Cited alongside, same era.
Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space
Nguyen, A. M., Clune, J., Bengio, Y., Dosovitskiy, A., and Yosinski, J · 2017
Cited alongside, same era.
Searching for Activation Functions
Ramachandran, P., Zoph, B., and Le, Q. V · 2017
Cited alongside, same era.
A Note on the Inception Score
Barratt, S. and Sharma, R · 2018
Cited alongside, same era.
Deep Energy Estimator Networks
Saremi, S., Mehrjou, A., Schölkopf, B., and Hyvärinen, A · 2018
Cited alongside, same era.
Improved Precision and Recall Metric for Assessing Generative Models
Kynkäänniemi, T., Karras, T., Laine, S., Lehtinen, J., and Aila, T · 2019
Cited alongside, same era.
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Helmholtz Decomposition and Rotation Potentials in n-Dimensional Cartesian Coordinates
Glötzl, E. and Richters, O · 2021
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Improved Denoising Diffusion Probabilistic Models
Nichol, A. Q. and Dhariwal, P · 2021
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Should EBMs Model the Energy or the Score?
Salimans, T. and Ho, J · 2021
Later among the works it cites.
Denoising Likelihood Score Matching for Conditional Score-based Data Generation
Chao, C.-H., Sun, W.-F., Cheng, B.-W., Lo, Y.-C., Chang, C.-C., Liu, Y.-L., Chang, Y.-L., Chen, C.-P., and Lee, C.-Y · 2022
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Elucidating the Design Space of Diffusion-Based Generative Models
Karras, T., Aittala, M., Aila, T., and Laine, S · 2022
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Poisson Flow Generative Models
Xu, Y., Liu, Z., Tegmark, M., and Jaakkola, T · 2022
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