Fetching the paper…
Reading the bibliography…
Score-based modeling through stochastic differential equations (SDEs) has provided a new perspective on diffusion models, and demonstrated superior performance on continuous data.
Pulse code communication
Frank Gray · 1953
Earlier work this paper cites.
On the distinction between the conditional probability and the joint probability approaches in the specification of nearest-neighbour systems
D Brook · 1964
Earlier work this paper cites.
Smoothing algorithms for nonlinear finite-dimensional systems
Brian DO Anderson and Ian B Rhodes · 1983
Earlier work this paper cites.
Estimation of non-normalized statistical models by score matching
Aapo Hyvärinen and Peter Dayan · 2005
Earlier work this paper cites.
Some extensions of score matching
Aapo Hyvärinen · 2007
Earlier work this paper cites.
Filtering, stability, and robustness
Ramon Van Handel · 2007
Earlier work this paper cites.
Training restricted boltzmann machines using approximations to the likelihood gradient
Tijmen Tieleman · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
A kernel two-sample test
Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
Earlier work this paper cites.
Interpretation and generalization of score matching
Siwei Lyu · 2012
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Made: Masked autoencoder for distribution estimation
Mathieu Germain, Karol Gregor, Iain Murray, and Hugo Larochelle · 2015
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
Cited alongside, same era.
Fast sampling of diffusion models with exponential integrator
Qinsheng Zhang and Yongxin Chen · 2015
Cited alongside, same era.
Learning-based methods for comparing sequences, with applications to audio-to-midi alignment and matching
Colin Raffel · 2016
Cited alongside, same era.
Neural discrete representation learning
Aaron Van Den Oord, Oriol Vinyals, et al · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
Musegan: Multi-track sequential generative adversarial networks for symbolic music generation and accompaniment
Structured denoising diffusion models in discrete state-spaces
Jacob Austin, Daniel D Johnson, Jonathan Ho, Daniel Tarlow, and Rianne van den Berg · 2021
Later among the works it cites.
Diffusion schrödinger bridge with applications to score-based generative modeling
Valentin De Bortoli, James Thornton, Jeremy Heng, and Arnaud Doucet · 2021
Later among the works it cites.
Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
Later among the works it cites.
Taming transformers for high-resolution image synthesis
Patrick Esser, Robin Rombach, and Bjorn Ommer · 2021
Later among the works it cites.
Beyond in-place corruption: Insertion and deletion in denoising probabilistic models
Daniel D Johnson, Jacob Austin, Rianne van den Berg, and Daniel Tarlow · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Hao-Wen Dong, Wen-Yi Hsiao, Li-Chia Yang, and Yi-Hsuan Yang · 2018
Cited alongside, same era.
Neural networks with cheap differential operators
Ricky TQ Chen and David K Duvenaud · 2019
Cited alongside, same era.
Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
Cited alongside, same era.
Learning discrete energy-based models via auxiliary-variable local exploration
Hanjun Dai, Rishabh Singh, Bo Dai, Charles Sutton, and Dale Schuurmans · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2020
Cited alongside, same era.
Autoregressive diffusion models
Emiel Hoogeboom, Alexey A Gritsenko, Jasmijn Bastings, Ben Poole, Rianne van den Berg, and Tim Salimans
Cited in the paper.
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
Later among the works it cites.
A continuous time framework for discrete denoising models
Andrew Campbell, Joe Benton, Valentin De Bortoli, Tom Rainforth, George Deligiannidis, and Arnaud Doucet · 2022
Closest in time.
Maskgit: Masked generative image transformer
Huiwen Chang, Han Zhang, Lu Jiang, Ce Liu, and William T Freeman · 2022
Closest in time.
Diffusion bridges vector quantized variational autoencoders
Max Cohen, Guillaume Quispe, Sylvain Le Corff, Charles Ollion, and Eric Moulines · 2022
Closest in time.
Vector quantized diffusion model for text-to-image synthesis
Shuyang Gu, Dong Chen, Jianmin Bao, Fang Wen, Bo Zhang, Dongdong Chen, Lu Yuan, and Baining Guo · 2022
Closest in time.
Generative flow networks for discrete probabilistic modeling
Dinghuai Zhang, Nikolay Malkin, Zhen Liu, Alexandra Volokhova, Aaron Courville, and Yoshua Bengio · 2022
Closest in time.