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Modern successes of diffusion models in learning complex, high-dimensional data distributions are attributed, in part, to their capability to construct diffusion processes with analytic transition kernels and score functions.
Reverse-time diffusion equation models
Brian DO Anderson · 1982
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Problem complexity and method efficiency in optimization
Arkadij Semenovič Nemirovskij and David Borisovich Yudin · 1983
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Manipulability of robotic mechanisms
Tsuneo Yoshikawa · 1985
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Reflected brownian motion with skew symmetric data in a polyhedral domain
Ruth J Williams · 1987
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Décomposition polaire et réarrangement monotone des champs de vecteurs
Yann Brenier · 1987
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Change of variables in multiple integrals
Peter D Lax · 1999
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Improved bounds for sampling contingency tables
Ben J Morris · 2002
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Mirror descent and nonlinear projected subgradient methods for convex optimization
Amir Beck and Marc Teboulle · 2003
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Iterative methods for sparse linear systems
Yousef Saad · 2003
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Latent dirichlet allocation
David M Blei, Andrew Y Ng, and Michael I Jordan · 2003
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A theory of the term structure of interest rates
John C Cox, Jonathan E Ingersoll Jr, and Stephen A Ross · 2005
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Inverse kinematics for a serial chain with joints under distance constraints
Li Han and Lee Rudolph · 2006
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Bayesian estimation via sequential monte carlo sampling—constrained dynamic systems
Lixin Lang, Wen-shiang Chen, Bhavik R Bakshi, Prem K Goel, and Sridhar Ungarala · 2007
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On smoothing and inference for topic models
Arthur Asuncion, Max Welling, Padhraic Smyth, and Yee Whye Teh · 2009
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Riemann manifold langevin and hamiltonian monte carlo methods
Mark Girolami and Ben Calderhead · 2011
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Constraining the metabolic genotype–phenotype relationship using a phylogeny of in silico methods
Nathan E Lewis, Harish Nagarajan, and Bernhard O Palsson · 2012
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Stochastic gradient Riemannian Langevin dynamics on the probability simplex
Sam Patterson and Yee Whye Teh · 2013
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An introduction to stochastic differential equations with reflection , volume 1
Andrey Pilipenko · 2014
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Analysis and geometry of Markov diffusion operators , volume 103
Dominique Bakry, Ivan Gentil, and Michel Ledoux · 2014
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Bayesian optimization with unknown constraints
Michael A Gelbart, Jasper Snoek, and Ryan P Adams · 2014
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Finite-time analysis of projected langevin monte carlo
Sebastien Bubeck, Ronen Eldan, and Joseph Lehec · 2015
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U-Net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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The entropic barrier: a simple and optimal universal self-concordant barrier
Sébastien Bubeck and Ronen Eldan · 2015
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A nonlinear model of opinion formation on the sphere
Marco Caponigro, Anna Chiara Lai, and Benedetto Piccoli · 2015
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Sliced and radon wasserstein barycenters of measures
Nicolas Bonneel, Julien Rabin, Gabriel Peyré, and Hanspeter Pfister · 2015
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Sampling from a log-concave distribution with compact support with proximal Langevin Monte Carlo
Nicolas Brosse, Alain Durmus, Éric Moulines, and Marcelo Pereyra · 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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Mirrored langevin dynamics
Ya-Ping Hsieh, Ali Kavis, Paul Rolland, and Volkan Cevher · 2018
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Sigmoid-weighted linear units for neural network function approximation in reinforcement learning
Stefan Elfwing, Eiji Uchibe, and Kenji Doya · 2018
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Group normalization
Yuxin Wu and Kaiming He · 2018
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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
Riemannian diffusion models
Chin-Wei Huang, Milad Aghajohari, Joey Bose, Prakash Panangaden, and Aaron C Courville · 2022
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Kanji Sato, Akiko Takeda, Reiichiro Kawai, and Taiji Suzuki · 2022
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The mirror langevin algorithm converges with vanishing bias
Ruilin Li, Molei Tao, Santosh S Vempala, and Andre Wibisono · 2022
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Elucidating the design space of diffusion-based generative models
Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine · 2022
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Semi-discrete normalizing flows through differentiable tessellation
Ricky TQ Chen, Brandon Amos, and Maximilian Nickel · 2022
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Decoupled weight decay regularization
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Denoising diffusion probabilistic models
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HiFi-GAN: Generative adversarial networks for efficient and high fidelity speech synthesis
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Improved techniques for training score-based generative models
Yang Song and Stefano Ermon · 2020
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Riemannian continuous normalizing flows
Emile Mathieu and Maximilian Nickel · 2020
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First hitting diffusion models for generating manifold, graph and categorical data
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Torsional diffusion for molecular conformer generation
Bowen Jing, Gabriele Corso, Jeffrey Chang, Regina Barzilay, and Tommi Jaakkola · 2022
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Denoising diffusion probabilistic models on S O SO (3) for rotational alignment
Adam Leach, Sebastian M Schmon, Matteo T Degiacomi, and Chris G Willcocks · 2022
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S E SE (3)-DiffusionFields: Learning cost functions for joint grasp and motion optimization through diffusion
Julen Urain, Niklas Funk, Georgia Chalvatzaki, and Jan Peters · 2022
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Categorical SDEs with simplex diffusion
Pierre H Richemond, Sander Dieleman, and Arnaud Doucet · 2022
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Diffusevae: Efficient, controllable and high-fidelity generation from low-dimensional latents
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Maximum likelihood training of implicit nonlinear diffusion models
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Dreamfusion: Text-to-3D using 2D diffusion
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Magic3D: High-resolution text-to-3D content creation
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Diffdock: Diffusion steps, twists, and turns for molecular docking
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I 2 SB: Image-to-Image Schrödinger bridge
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