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Energy-based models (EBMs) are known in the Machine Learning community for decades.
Exponential convergence of langevin distributions and their discrete approximations
Gareth O Roberts and Richard L Tweedie · 1996
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Training products of experts by minimizing contrastive divergence
Geoffrey E Hinton · 2002
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A tutorial on energy-based learning
Yann LeCun, Sumit Chopra, Raia Hadsell, M Ranzato, and Fujie Huang · 2006
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Restricted boltzmann machines for collaborative filtering
Ruslan Salakhutdinov, Andriy Mnih, and Geoffrey Hinton · 2007
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Training restricted boltzmann machines using approximations to the likelihood gradient
Tijmen Tieleman · 2008
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Optimal transport: old and new , volume 338
Cédric Villani et al · 2009
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Riemann manifold langevin and hamiltonian monte carlo methods
Mark Girolami and Ben Calderhead · 2011
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Sinkhorn distances: Lightspeed computation of optimal transport
Marco Cuturi · 2013
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The no-u-turn sampler: adaptively setting path lengths in hamiltonian monte carlo
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Understanding machine learning: From theory to algorithms
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Optimal transport for applied mathematicians
Filippo Santambrogio · 2015
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Convolutional wasserstein distances: Efficient optimal transportation on geometric domains
Justin Solomon, Fernando De Goes, Gabriel Peyré, Marco Cuturi, Adrian Butscher, Andy Nguyen, Tao Du, and Leonidas Guibas · 2015
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Stochastic optimization for large-scale optimal transport
Aude Genevay, Marco Cuturi, Gabriel Peyré, and Francis Bach · 2016
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A theory of generative convnet
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Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Kantorovich duality for general transport costs and applications
Nathael Gozlan, Cyril Roberto, Paul-Marie Samson, and Prasad Tetali · 2017
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Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros · 2017
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Generative modeling using the sliced wasserstein distance
Ishan Deshpande, Ziyu Zhang, and Alexander G Schwing · 2018
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Multimodal unsupervised image-to-image translation
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Approximation by combinations of relu and squared relu ridge functions with ℓ 1 \ell^{1} and ℓ 0 \ell^{0} controls
Jason M Klusowski and Andrew R Barron · 2018
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Wasserstein distance guided representation learning for domain adaptation
Jian Shen, Yanru Qu, Weinan Zhang, and Yong Yu · 2018
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Learning deep hidden nonlinear dynamics from aggregate data
Yisen Wang, Bo Dai, Lingkai Kong, Sarah Monazam Erfani, James Bailey, and Hongyuan Zha · 2018
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Cooperative training of descriptor and generator networks
Jianwen Xie, Yang Lu, Ruiqi Gao, Song-Chun Zhu, and Ying Nian Wu · 2018
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Existence, duality, and cyclical monotonicity for weak transport costs
Julio Backhoff-Veraguas, Mathias Beiglböck, and Gudmun Pammer · 2019
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Implicit generation and modeling with energy based models
Yilun Du and Igor Mordatch · 2019
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Entropy-regularized optimal transport for machine learning
Aude Genevay · 2019
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Sample complexity of sinkhorn divergences
Aude Genevay, Lénaic Chizat, Francis Bach, Marco Cuturi, and Gabriel Peyré · 2019
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Divergence triangle for joint training of generator model, energy-based model, and inferential model
Tian Han, Erik Nijkamp, Xiaolin Fang, Mitch Hill, Song-Chun Zhu, and Ying Nian Wu · 2019
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Maximum entropy generators for energy-based models
Rithesh Kumar, Sherjil Ozair, Anirudh Goyal, Aaron Courville, and Yoshua Bengio · 2019
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Energy-inspired models: Learning with sampler-induced distributions
John Lawson, George Tucker, Bo Dai, and Rajesh Ranganath · 2019
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Sinkhorn barycenters with free support via frank-wolfe algorithm
Giulia Luise, Saverio Salzo, Massimiliano Pontil, and Carlo Ciliberto · 2019
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Statistical bounds for entropic optimal transport: sample complexity and the central limit theorem
Gonzalo Mena and Jonathan Niles-Weed · 2019
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Learning stochastic behaviour from aggregate data
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Large-scale wasserstein gradient flows
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Most: Multi-source domain adaptation via optimal transport for student-teacher learning
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Introduction to entropic optimal transport
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A FreeForm Optics Application of Entropic Optimal Transport
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How to train your energy-based models
Yang Song and Diederik P Kingma · 2021
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Computational optimal transport: With applications to data science
Gabriel Peyré, Marco Cuturi, et al · 2019
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Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming
Geoffrey Schiebinger, Jian Shu, Marcin Tabaka, Brian Cleary, Vidya Subramanian, Aryeh Solomon, Joshua Gould, Siyan Liu, Stacie Lin, Peter Berube, et al · 2019
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Amirhossein Taghvaei and Amin Jalali · 2019
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On scalable and efficient computation of large scale optimal transport
Yujia Xie, Minshuo Chen, Haoming Jiang, Tuo Zhao, and Hongyuan Zha · 2019
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Geometric dataset distances via optimal transport
David Alvarez-Melis and Nicolo Fusi · 2020
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Stargan v2: Diverse image synthesis for multiple domains
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Learning normalizing flows from entropy-kantorovich potentials
Chris Finlay, Augusto Gerolin, Adam M Oberman, and Aram-Alexandre Pooladian · 2020
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Non-asymptotic performance guarantees for neural estimation of f-divergences
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Solving schrödinger bridges via maximum likelihood
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Learning cycle-consistent cooperative networks via alternating mcmc teaching for unsupervised cross-domain translation
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Unpaired image-to-image translation via latent energy transport
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Learning energy-based generative models via coarse-to-fine expanding and sampling
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Optimizing functionals on the space of probabilities with input convex neural networks
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Likelihood training of schrödinger bridge using forward-backward SDEs theory
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An optimal transport perspective on unpaired image super-resolution
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SDEdit: Guided image synthesis and editing with stochastic differential equations
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MCMC should mix: Learning energy-based model with neural transport latent space MCMC
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On the sample complexity of entropic optimal transport
Philippe Rigollet and Austin J Stromme · 2022
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Generative modeling with optimal transport maps
Litu Rout, Alexander Korotin, and Evgeny Burnaev · 2022
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Local-global MCMC kernels: the best of both worlds
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Boundary preserving twin energy-based-models for image to image translation
Piyush Tiwary, Kinjawl Bhattacharyya, and Prathosh AP · 2022
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Wasserstein uncertainty estimation for adversarial domain matching
Rui Wang, Ruiyi Zhang, and Ricardo Henao · 2022
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Learning energy-based models with adversarial training
Xuwang Yin, Shiying Li, and Gustavo K Rohde · 2022
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Egsde: Unpaired image-to-image translation via energy-guided stochastic differential equations
Min Zhao, Fan Bao, Chongxuan Li, and Jun Zhu · 2022
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An improved central limit theorem and fast convergence rates for entropic transportation costs
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Neural monge map estimation and its applications
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Entropic neural optimal transport via diffusion processes
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