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Score-based generative models (SGMs) have demonstrated remarkable synthesis quality.
On the Product of Semi-Groups of Operators
H. F. Trotter · 1959
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Some methods of speeding up the convergence of iteration methods
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On the Construction and Comparison of Difference Schemes
Gilbert Strang · 1968
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Hans C. Andersen · 1980
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A family of embedded Runge–Kutta formulae
J. R. Dormand and P. J. Prince · 1980
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Reverse-time diffusion equation models
Brian DO Anderson · 1982
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A unified formulation of the constant temperature molecular dynamics methods
Shuichi Nosé · 1984
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Canonical dynamics: Equilibrium phase-space distributions
William G. Hoover · 1985
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Time Reversal of Diffusions
Ulrich G Haussmann and Etienne Pardoux · 1986
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Hybrid Monte Carlo
Simon Duane, A.D. Kennedy, Brian J. Pendleton, and Duncan Roweth · 1987
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Numerical Solution of Stochastic Differential Equations
Peter E. Kloeden and Eckhard Platen · 1992
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Nosé–Hoover chains: The canonical ensemble via continuous dynamics
Glenn J. Martyna, Michael L. Klein, and Mark Tuckerman · 1992
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Reversible multiple time scale molecular dynamics
M. Tuckerman, B. J. Berne, and G. J. Martyna · 1992
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Annealed importance sampling
Radford M. Neal · 2001
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Thermostat Algorithms for Molecular Dynamics Simulations , volume 173 of Advanced Computer Simulation. Advances in Polymer Science
Philippe H. Hünenberger · 2005
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Estimation of Non-Normalized Statistical Models by Score Matching
Aapo Hyvärinen · 2005
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Simulating Hamiltonian Dynamics
Benedict Leimkuhler and Sebastian Reich · 2005
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Accurate sampling using Langevin dynamics
Giovanni Bussi and Michele Parrinello · 2007
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Canonical sampling through velocity rescaling
Giovanni Bussi, Davide Donadio, and Michele Parrinello · 2007
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Contrastive Divergence in Gaussian Diffusions
Javier R. Movellan · 2008
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Langevin Equation with Colored Noise for Constant-Temperature Molecular Dynamics Simulations
Michele Ceriotti, Giovanni Bussi, and Michele Parrinello · 2009
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Interpretation and Generalization of Score Matching
Siwei Lyu · 2009
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Efficient stochastic thermostatting of path integral molecular dynamics
Michele Ceriotti, Michele Parrinello, Thomas E. Markland, and David E. Manolopoulos · 2010
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Classical Mechanics: From Newton to Einstein: A Modern Introduction, 2nd Edition
Martin W McCall · 2010
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Statistical Mechanics: Theory and Molecular Simulation
Mark E. Tuckerman · 2010
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Equalities and Inequalities: Irreversibility and the Second Law of Thermodynamics at the Nanoscale
Christopher Jarzynski · 2011
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MCMC Using Hamiltonian Dynamics
Radford M. Neal · 2011
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Minimum Probability Flow Learning
Jascha Sohl-Dickstein, Peter Battaglino, and Michael R. DeWeese · 2011
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A Connection Between Score Matching and Denoising Autoencoders
Pascal Vincent · 2011
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Modify the Improved Euler scheme to integrate stochastic differential equations
A. J. Roberts · 2012
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Rational Construction of Stochastic Numerical Methods for Molecular Sampling
Benedict Leimkuhler and Charles Matthews · 2013
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Deep Generative Stochastic Networks Trainable by Backprop
Yoshua Bengio, Eric Laufer, Guillaume Alain, and Jason Yosinski · 2014
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Stochastic Gradient Hamiltonian Monte Carlo
Tianqi Chen, Emily Fox, and Carlos Guestrin · 2014
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Bayesian Sampling Using Stochastic Gradient Thermostats
Nan Ding, Youhan Fang, Ryan Babbush, Changyou Chen, Robert D Skeel, and Hartmut Neven · 2014
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Generative Adversarial Nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Importance Weighted Autoencoders
Yuri Burda, Roger Grosse, and Ruslan Salakhutdinov · 2015
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Adam: A Method for Stochastic Optimization
Diederik P Kingma and Jimmy Ba · 2015
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Molecular Dynamics: With Deterministic and Stochastic Numerical Methods
Benedict Leimkuhler and Charles Matthews · 2015
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Covariance-Controlled Adaptive Langevin Thermostat for Large-Scale Bayesian Sampling
Xiaocheng Shang, Zhanxing Zhu, Benedict Leimkuhler, and Amos J Storkey · 2015
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Deep Unsupervised Learning using Nonequilibrium Thermodynamics
Structured Denoising Diffusion Models in Discrete State-Spaces
Jacob Austin, Daniel Johnson, Jonathan Ho, Danny Tarlow, and Rianne van den Berg · 2021
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WaveGrad: Estimating Gradients for Waveform Generation
Nanxin Chen, Yu Zhang, Heiga Zen, Ron J Weiss, Mohammad Norouzi, and William Chan · 2021
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Diffusion Models Beat GANs on Image Synthesis
Prafulla Dhariwal and Alex Nichol · 2021
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Generative Probabilistic Image Colorization
Chie Furusawa, Shinya Kitaoka, Michael Li, and Yuri Odagiri · 2021
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Learning Energy-Based Models by Diffusion Recovery Likelihood
Ruiqi Gao, Yang Song, Ben Poole, Ying Nian Wu, and Diederik P Kingma · 2021
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Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Pixel Recurrent Neural Networks
Aaron van den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu · 2016
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Learning to Generate Samples from Noise through Infusion Training
Florian Bordes, Sina Honari, and Pascal Vincent · 2017
Cited alongside, same era.
Variational Walkback: Learning a Transition Operator as a Stochastic Recurrent Net
Anirudh Goyal, Nan Rosemary Ke, Surya Ganguli, and Yoshua Bengio · 2017
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GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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From classical to quantum and back: Hamiltonian adaptive resolution path integral, ring polymer, and centroid molecular dynamics
Karsten Kreis, Kurt Kremer, Raffaello Potestio, and Mark E. Tuckerman · 2017
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Neural Ordinary Differential Equations
Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, and David Duvenaud · 2018
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Cascaded Diffusion Models for High Fidelity Image Generation
Jonathan Ho, Chitwan Saharia, William Chan, David J Fleet, Mohammad Norouzi, and Tim Salimans · 2021
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A Variational Perspective on Diffusion-Based Generative Models and Score Matching
Chin-Wei Huang, Jae Hyun Lim, and Aaron Courville · 2021
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Diff-TTS: A Denoising Diffusion Model for Text-to-Speech
Myeonghun Jeong, Hyeongju Kim, Sung Jun Cheon, Byoung Jin Choi, and Nam Soo Kim · 2021
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TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up
Yifan Jiang, Shiyu Chang, and Zhangyang Wang · 2021
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Score Matching Model for Unbounded Data Score
Dongjun Kim, Seungjae Shin, Kyungwoo Song, Wanmo Kang, and Il-Chul Moon · 2021
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Diederik P Kingma, Tim Salimans, Ben Poole, and Jonathan Ho · 2021
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On Fast Sampling of Diffusion Probabilistic Models
Zhifeng Kong and Wei Ping · 2021
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DiffWave: A Versatile Diffusion Model for Audio Synthesis
Zhifeng Kong, Wei Ping, Jiaji Huang, Kexin Zhao, and Bryan Catanzaro · 2021
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SRDiff: Single Image Super-Resolution with Diffusion Probabilistic Models
Haoying Li, Yifan Yang, Meng Chang, Huajun Feng, Zhihai Xu, Qi Li, and Yueting Chen · 2021
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Knowledge Distillation in Iterative Generative Models for Improved Sampling Speed
Eric Luhman and Troy Luhman · 2021
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Diffusion Probabilistic Models for 3D Point Cloud Generation
Shitong Luo and Wei Hu · 2021
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SDEdit: Image Synthesis and Editing with Stochastic Differential Equations
Chenlin Meng, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, and Stefano Ermon · 2021
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The Creation and Detection of Deepfakes: A Survey
Yisroel Mirsky and Wenke Lee · 2021
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Symbolic music generation with diffusion models
Gautam Mittal, Jesse Engel, Curtis Hawthorne, and Ian Simon · 2021
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High-Order Langevin Diffusion Yields an Accelerated MCMC Algorithm
Wenlong Mou, Yi-An Ma, Martin J. Wainwright, Peter L. Bartlett, and Michael I. Jordan · 2021
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Non Gaussian Denoising Diffusion Models
Eliya Nachmani, Robin San Roman, and Lior Wolf · 2021
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Deep Learning for Deepfakes Creation and Detection: A Survey
Thanh Thi Nguyen, Quoc Viet Hung Nguyen, Cuong M. Nguyen, Dung Nguyen, Duc Thanh Nguyen, and Saeid Nahavandi · 2021
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Improved Denoising Diffusion Probabilistic Models
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
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Dual Contradistinctive Generative Autoencoder
Gaurav Parmar, Dacheng Li, Kwonjoon Lee, and Zhuowen Tu · 2021
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Image Super-Resolution via Iterative Refinement
Chitwan Saharia, Jonathan Ho, William Chan, Tim Salimans, David J Fleet, and Mohammad Norouzi · 2021
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Noise Estimation for Generative Diffusion Models
Robin San-Roman, Eliya Nachmani, and Lior Wolf · 2021
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UNIT-DDPM: UNpaired Image Translation with Denoising Diffusion Probabilistic Models
Hiroshi Sasaki, Chris G. Willcocks, and Toby P. Breckon · 2021
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D2C: Diffusion-Denoising Models for Few-shot Conditional Generation
Abhishek Sinha, Jiaming Song, Chenlin Meng, and Stefano Ermon · 2021
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Score-based Generative Modeling in Latent Space
Arash Vahdat, Karsten Kreis, and Jan Kautz · 2021
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Learning to Efficiently Sample from Diffusion Probabilistic Models
Daniel Watson, Jonathan Ho, Mohammad Norouzi, and William Chan · 2021
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VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models
Zhisheng Xiao, Karsten Kreis, Jan Kautz, and Arash Vahdat · 2021
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3D Shape Generation and Completion through Point-Voxel Diffusion
Linqi Zhou, Yilun Du, and Jiajun Wu · 2021
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GSNs: generative stochastic networks
Guillaume Alain, Yoshua Bengio, Li Yao, Jason Yosinski, Éric Thibodeau-Laufer, Saizheng Zhang, and Pascal Vincent · 2049
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