Fetching the paper…
Reading the bibliography…
Score-based generative models are a popular class of generative modelling techniques relying on stochastic differential equations (SDE).
Die Lie-Reihen und ihre Anwendungen
Wolfgang Gröbner · 1960
Earlier work this paper cites.
An estimate for the perturbations of the solutions of ordinary differential equations
Vladimir M Alekseev · 1961
Earlier work this paper cites.
On extensions of the Brunn-Minkowski and Prékopa-Leindler theorems, including inequalities for log concave functions, and with an application to the diffusion equation
Herm Jan Brascamp and Elliott H Lieb · 1976
Earlier work this paper cites.
A Connection Between Score Matching and Denoising Autoencoders
Pascal Vincent · 2011
Earlier work this paper cites.
Thin Shell Implies Spectral Gap Up to Polylog via a Stochastic Localization Scheme
Ronen Eldan · 2013
Earlier work this paper cites.
Variational Inference with Normalizing Flows
Danilo Jimenez Rezende and Shakir Mohamed · 2015
Earlier work this paper cites.
Neural Ordinary Differential Equations
Ricky T Q Chen, Yulia Rubanova, Jesse Bettencourt, and David Duvenaud · 2018
Earlier work this paper cites.
FFJORD: Free-form Continuous Dynamics for Scalable Reverse Generative Models
Will Grathwohl, Ricky T Q Chen, Jesse Bettencourt, Ilya Sutskever, and David Duvenaud · 2019
Earlier work this paper cites.
Generative Modeling by Estimating Gradients of the Data Distribution
Yang Song and Stefano Ermon · 2019
Earlier work this paper cites.
Taming correlations through entropy-efficient measure decompositions with applications to mean-field approximation
Ronen Eldan · 2020
Earlier work this paper cites.
Denoising Diffusion Probabilistic Models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Earlier work this paper cites.
Diffusion Schrödinger Bridge with Applications to Score-Based Generative Modeling
Valentin De Bortoli, James Thornton, Jeremy Heng, and Arnaud Doucet · 2021
Earlier work this paper cites.
Diffusion Models Beat GANs on Image Synthesis
Prafulla Dhariwal and Alex Nichol · 2021
Cited alongside, same era.
Grad-TTS: A Diffusion Probabilistic Model for Text-to-Speech
Vadim Popov, Ivan Vovk, Vladimir Gogoryan, Tasnima Sadekova, and Mikhail Kudinov · 2021
Cited alongside, same era.
Moser Flow: Divergence-based Generative Modeling on Manifolds
Noam Rozen, Aditya Grover, Maximilian Nickel, and Yaron Lipman · 2021
Cited alongside, same era.
An Information-Theoretic View of Stochastic Localization
Ahmed El Alaoui and Andrea Montanari · 2022
Cited alongside, same era.
Matching Normalizing Flows and Probability Paths on Manifolds
Heli Ben-Hamu, Samuel Cohen, Joey Bose, Brandon Amos, Aditya Grover, Maximilian Nickel, Ricky T Q Chen, and Yaron Lipman · 2022
Cited alongside, same era.
Generative Modeling with Denoising Auto-Encoders and Langevin Sampling
Convergence in KL and Rényi Divergence of the Unadjusted Langevin Algorithm Using Estimated Score
Kaylee Yingxi Yang and Andre Wibisono · 2022
Later among the works it cites.
Building Normalizing Flows with Stochastic Interpolants
Michael S Albergo and Eric Vanden-Eijnden · 2023
Closest in time.
Stochastic Interpolants: A Unifying Framework for Flows and Diffusions
Michael S Albergo, Nicholas M Boffi, and Eric Vanden-Eijnden · 2023
Closest in time.
Nearly d d -Linear Convergence Bounds for Diffusion Models via Stochastic Localization
Joe Benton, Valentin De Bortoli, Arnaud Doucet, and George Deligiannidis · 2023
Closest in time.
Score diffusion models without early stopping: finite Fisher information is all you need
Giovanni Conforti, Alain Durmus, and Marta Gentiloni Silveri · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Adam Block, Youssef Mroueh, and Alexander Rakhlin · 2022
Cited alongside, same era.
Convergence of denoising diffusion models under the manifold hypothesis
Valentin De Bortoli · 2022
Cited alongside, same era.
Convergence for score-based generative modeling with polynomial complexity
Holden Lee, Jianfeng Lu, and Yixin Tan · 2022
Cited alongside, same era.
Let us Build Bridges: Understanding and Extending Diffusion Generative Models
Xingchao Liu, Lemeng Wu, Mao Ye, and Qiang Liu · 2022
Cited alongside, same era.
Score-Based Generative Models Detect Manifolds
Jakiw Pidstrigach · 2022
Cited alongside, same era.
Hierarchical Text-Conditional Image Generation with CLIP Latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
Cited alongside, same era.
Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S Sara Mahdavi, Rapha Gontijo Lopes, Tim Salimans, et al · 2022
Cited alongside, same era.
Closest in time.
Iterative α \alpha -(de)Blending: a Minimalist Deterministic Diffusion Model
Eric Heitz, Laurent Belcour, and Thomas Chambon · 2023
Closest in time.
Convergence of score-based generative modeling for general data distributions
Holden Lee, Jianfeng Lu, and Yixin Tan · 2023
Closest in time.
Towards Faster Non-Asymptotic Convergence for Diffusion-Based Generative Models
Gen Li, Yuting Wei, Yuxin Chen, and Yuejie Chi · 2023
Closest in time.
Flow matching for generative modeling
Yaron Lipman, Ricky T Q Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le · 2023
Closest in time.
Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
Xingchao Liu, Chengyue Gong, and Qiang Liu · 2023
Closest in time.
Fast Sampling of Diffusion Models with Exponential Integrator
Qinsheng Zhang and Yongxin Chen · 2023
Closest in time.