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The momentum Schr\"odinger Bridge (mSB) has emerged as a leading method for accelerating generative diffusion processes and reducing transport costs.
A Stochastic Approximation Method
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Reverse-time Diffusion Equation Models
Anderson, B. D. (1982) · 1982
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On Sampling from a Log-concave Density using Kinetic Langevin Diffusions
Dalalyan, A. S. and Riou-Durand, L. (2020) · 1988
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Adaptive Algorithms and Stochastic Approximations
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A Convexity Principle for Interacting Gases
McCann, R. J. (1997) · 1997
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Geometric Numerical Integration
Hairer, E., Lubich, C., and Wanner, G. (2006) · 2006
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Forward-Backward Stochastic Differential Equations and their Applications
Ma, J. and Yong, J. (2007) · 2007
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Statistical Mechanics: Theory and Molecular Simulation
Tuckerman, M. E. (2010) · 2010
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Classical Mechanics: From Newton to Einstein: A Modern Introduction (Second Edition)
McCall, M. W. (2011) · 2011
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MCMC using Hamiltonian dynamics
Neal, R. M. (2012) · 2012
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Rational construction of stochastic numerical methods for molecular sampling
Leimkuhler, B. and Matthews, C. (2013) · 2013
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On the Convergence of Stochastic Gradient MCMC Algorithms with High-order Integrators
Chen, C., Ding, N., and Carin, L. (2015) · 2015
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Stochastic Bridges of Linear Systems
Chen, Y. and Georgiou, T. (2016) · 2016
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Underdamped Langevin MCMC: A Non-Asymptotic Analysis
Cheng, X., Chatterji, N. S., Bartlett, P. L., and Jordan, M. I. (2017) · 2017
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Neural Ordinary Differential Equations
Chen, R. T. Q., Rubanova, Y., Bettencourt, J., and Duvenaud, D. (2018) · 2018
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Dimensionally Tight Running Time Bounds for Second-order Hamiltonian Monte Carlo
Mangoubi, O. and Vishnoi, N. K. (2018) · 2018
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User-friendly Guarantees for the Langevin Monte Carlo with Inaccurate Gradient
Dalalyan, A. S. and Karagulyan, A. G. (2019) · 2019
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Applied Stochastic Differential Equations
Särkkä, S. and Solin, A. (2019) · 2019
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Discriminator Optimal Transport
Tanaka, A. (2019) · 2019
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Refining Deep Generative Models via Discriminator Gradient Flow
Ansari, A. F., Ang, M. L., and Soh, H. (2020) · 2020
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Denoising Diffusion Probabilistic Models
Ho, J., Jain, A., and Abbeel, P. (2020) · 2020
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Stochastic Control Liaisons: Richard Sinkhorn Meets Gaspard Monge on a Schrödinger Bridge
Chen, Y., Georgiou, T. T., and Pavon, M. (2021) · 2021
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Diffusion Schrödinger Bridge with Applications to Score-Based Generative Modeling
De Bortoli, V., Thornton, J., Heng, J., and Doucet, A. (2021) · 2021
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Kingma, D. P., Salimans, T., Poole, B., and Ho, J. (2021) · 2021
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DiffWave: A Versatile Diffusion Model for Audio Synthesis
Kong, Z., Ping, W., Huang, J., Zhao, K., and Catanzaro, B. (2021) · 2021
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Is there an analog of nesterov acceleration for gradient-based mcmc?
Ma, Y.-A., Chatterji, N. S., Cheng, X., Flammarion, N., Bartlett, P. L., and Jordan, M. I. (2021) · 2021
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The Data-driven Schrödinger Bridge
Pavon, M., Tabak, E. G., and Trigila, G. (2021) · 2021
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Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecasting
Progressive Distillation for Fast Sampling of Diffusion Models
Salimans, T. and Ho, J. (2022) · 2022
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Stochastic Interpolants: A Unifying Framework for Flows and Diffusions
Albergo, M. S., Bof, N. M., and Vanden-Eijnden, E. (2023) · 2023
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Building Normalizing Flows with Stochastic Interpolants
Albergo, M. S. and Vanden-Eijnden, E. (2023) · 2023
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The Schrödinger Bridge between Gaussian Measures has a Closed Form
Bunne, C., Hsieh, Y.-P., Cuturi, m., and Krause, A. (2023) · 2023
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Entropic Neural Optimal Transport via Diffusion Processes
Gushchin, N., Kolesov, A., Korotin, A., Vetrov, D., and Burnaev, E. (2023) · 2023
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Flow Matching for Generative Modeling
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Rasul, K., Seward, C., Schuster, I., and Vollgraf, R. (2021) · 2021
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Score-Based Generative Modeling through Stochastic Differential Equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B. (2021) · 2021
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Score-based Generative Modeling in Latent Space
Vahdat, A., Kreis, K., and Kautz, J. (2021) · 2021
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Solving Schrödinger Bridges via Maximum Likelihood
Vargas, F., Thodoroff, P., Lamacraft, A., and Lawrence, N. (2021) · 2021
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Deep Generative Learning via Schrödinger Bridge
Wang, G., Jiao, Y., Xu, Q., Wang, Y., and Yang, C. (2021) · 2021
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Wasserstein Proximal Algorithms for the Schrödinger Bridge Problem: Density Control with Nonlinear Drift
Caluya, K. F. and Halder, A. (2022) · 2022
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Likelihood Training of Schrödinger Bridge using Forward-Backward SDEs Theory
Chen, T., Liu, G.-H., and Theodorou, E. A. (2022) · 2022
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Lipman, Y., Chen, R. T. Q., Ben-Hamu, H., Nickel, M., and Le, M. (2023) · 2023
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Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
Liu, X., Gong, C., and Liu, Q. (2023) · 2023
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Diffusion Bridge Mixture Transports, Schrödinger Bridge Problems and Generative Modeling
Peluchetti, S. (2023) · 2023
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Multisample Flow Matching: Straightening Flows with Minibatch Couplings
Pooladian, A.-A., Ben-Hamu, H., Domingo-Enrich, C., Amos, B., Lipman, Y., and Chen, R. T. Q. (2023) · 2023
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Diffusion Schrödinger Bridge Matching
Shi, Y., De Bortoli, V., Campbell, A., and Doucet, A. (2023) · 2023
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Where to Diffuse, How to Diffuse, and How to Get Back: Automated Learning for Multivariate Diffusions
Singhal, R., Goldstein, M., and Ranganath, R. (2023) · 2023
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Aligned Diffusion Schrödinger Bridges
Somnath, V. R., Pariset, M., Hsieh, Y.-P., Martinez, M. R., Krause, A., and Bunne, C. (2023) · 2023
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Neural Flow Diffusion Models: Learnable Forward Process for Improved Diffusion Modelling
Bartosh, G., Vetrov, D., and Naesseth, C. A. (2024) · 2024
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Generative Modeling with Phase Stochastic Bridges
Chen, T., Gu, J., Dinh, L., Theodorou, E. A., Susskind, J., and Zhai, S. (2024) · 2024
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Schrödinger Bridge Flow for Unpaired Data Translation
De Bortoli, V., Korshunova, I., Mnih, A., and Doucet, A. (2024) · 2024
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Unbalancedness in Neural Monge Maps Improves Unpaired Domain Translation
Eyring, L., Klein, D., Uscidda, T., Palla, G., Kilbertus, N., Akata, Z., and Theis, F. (2024) · 2024
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Unpaired Image-to-Image Translation via Neural Schrödinger Bridge
Kim, B., Kwon, G., Kim, K., and Ye, J. C. (2024) · 2024
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Light Schrödinger Bridge
Korotin, A., Gushchin, N., and Burnaev, E. (2024) · 2024
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A Computational Framework for Solving Wasserstein Lagrangian Flows
Neklyudov, K., Brekelmans, R., Tong, A., Atanackovic, L., Liu, Q., and Makhzani, A. (2024) · 2024
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Improving and Generalizing Flow-based Generative Models with Minibatch Optimal Transport
Tong, A., Malkin, N., Huguet, G., Zhang, Y., Rector-Brooks, J., Fatras, K., Wolf, G., and Bengio, Y. (2024) · 2024
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Mixing of Hamiltonian Monte Carlo on Strongly Log-concave Distributions: Continuous Dynamics
Mangoubi, O. and Smith, A. (2021) · 2045
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