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Diffusion models, a family of generative models based on deep learning, have become increasingly prominent in cutting-edge machine learning research.
Reverse-time diffusion equation models
Anderson BD, 1982 · 1982
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Long short-term memory
Hochreiter S, Schmidhuber J, 1997 · 1997
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Estimation of non-normalized statistical models by score matching
Hyvärinen A, Dayan P, 2005 · 2005
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A connection between score matching and denoising autoencoders
Vincent P, 2011 · 2011
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Integrating outlier filtering in large margin training
Zhou X, Shen H, Ye J, 2011 · 2011
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Empirical evaluation of gated recurrent neural networks on sequence modeling
Chung J, Gulcehre C, Cho K, et al., 2014 · 2014
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U-net: Convolutional networks for biomedical image segmentation
Ronneberger O, Fischer P, Brox T, 2015 · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein J, Weiss E, Maheswaranathan N, et al., 2015 · 2015
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C-rnn-gan: Continuous recurrent neural networks with adversarial training
Mogren O, 2016 · 2016
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Wavenet: A generative model for raw audio
van den Oord A, Dieleman S, Zen H, et al., 2016 · 2016
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St-mvl: filling missing values in geo-sensory time series data
Yi X, Zheng Y, Zhang J, et al., 2016 · 2016
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Real-valued (medical) time series generation with recurrent conditional gans
Esteban C, Hyland SL, Rätsch G, 2017 · 2017
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Attention is all you need
Vaswani A, Shazeer N, Parmar N, et al., 2017 · 2017
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Brits: Bidirectional recurrent imputation for time series
Cao W, Wang D, Li J, et al., 2018 · 2018
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Recurrent neural networks for multivariate time series with missing values
Che Z, Purushotham S, Cho K, et al., 2018 · 2018
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Adversarial audio synthesis
Donahue C, McAuley J, Puckette M, 2018 · 2018
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Diffusion convolutional recurrent neural network: Data-driven traffic forecasting
Li Y, Yu R, Shahabi C, et al., 2018 · 2018
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Multivariate time series imputation with generative adversarial networks
Luo Y, Cai X, Zhang Y, et al., 2018 · 2018
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A survey on data imputation techniques: Water distribution system as a use case
Osman MS, Abu-Mahfouz AM, Page PR, 2018 · 2018
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Estimating missing data in temporal data streams using multi-directional recurrent neural networks
Yoon J, Zame WR, van der Schaar M, 2018 · 2018
Cited alongside, same era.
Neural empirical Bayes
Saremi S, Hyvarinen A, 2019 · 2019
Cited alongside, same era.
Generative modeling by estimating gradients of the data distribution
Song Y, Ermon S, 2019 · 2019
Cited alongside, same era.
Smoothness and stability in gans
Chu C, Minami K, Fukumizu K, 2020 · 2020
Cited alongside, same era.
Gp-vae: Deep probabilistic time series imputation
Fortuin V, Baranchuk D, Rätsch G, et al., 2020 · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Ho J, Jain A, Abbeel P, 2020 · 2020
Cited alongside, same era.
Tackling the generative learning trilemma with denoising diffusion gans
Xiao Z, Kreis K, Vahdat A, 2021 · 2021
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Scoregrad: Multivariate probabilistic time series forecasting with continuous energy-based generative models
Yan T, Zhang H, Zhou T, et al., 2021 · 2021
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Modeling temporal data as continuous functions with process diffusion
Biloš M, Rasul K, Schneider A, et al., 2022 · 2022
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Graph neural controlled differential equations for traffic forecasting
Choi J, Choi H, Hwang J, et al., 2022 · 2022
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It’s raw! audio generation with state-space models
Goel K, Gu A, Donahue C, et al., 2022 · 2022
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Efficiently modeling long sequences with structured state spaces
Gu A, Goel K, Re C, 2022 · 2022
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Sliced score matching: A scalable approach to density and score estimation
Song Y, Garg S, Shi J, et al., 2020 · 2020
Cited alongside, same era.
NVAE: A deep hierarchical variational autoencoder
Vahdat A, Kautz J, 2020 · 2020
Cited alongside, same era.
Cot-gan: Generating sequential data via causal optimal transport
Xu T, Wenliang LK, Munn M, et al., 2020 · 2020
Cited alongside, same era.
Structured denoising diffusion models in discrete state-spaces
Austin J, Johnson DD, Ho J, et al., 2021 · 2021
Cited alongside, same era.
Timevae: A variational auto-encoder for multivariate time series generation
Desai A, Freeman C, Wang Z, et al., 2021 · 2021
Cited alongside, same era.
Diffusion models beat GANs on image synthesis
Dhariwal P, Nichol A, 2021 · 2021
Cited alongside, same era.
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Flexible diffusion modeling of long videos
Harvey W, Naderiparizi S, Masrani V, et al., 2022 · 2022
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Diffusion-LM improves controllable text generation
Li X, Thickstun J, Gulrajani I, et al., 2022 · 2022
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Generative time series forecasting with diffusion, denoise, and disentanglement
Li Y, Lu X, Wang Y, et al., 2022 · 2022
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Understanding diffusion models: A unified perspective
Luo C, 2022 · 2022
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Step-unrolled denoising autoencoders for text generation
Nikolay S, Junyoung C, Mikolaj B, et al., 2022 · 2022
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Latent diffusion energy-based model for interpretable text modelling
Yu P, Xie S, Ma X, et al., 2022 · 2022
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Diffusion-based time series imputation and forecasting with structured state space models
Alcaraz JL, Strodthoff N, 2023 · 2023
Closest in time.
Diffusion models in vision: A survey
Croitoru FA, Hondru V, Ionescu RT, et al., 2023 · 2023
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Graph convolution recurrent denoising diffusion model for multivariate probabilistic temporal forecasting
Li R, Li X, Gao S, et al., 2023 · 2023
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Regular time-series generation using sgm
Lim H, Kim M, Park S, et al., 2023 · 2023
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PriSTI: A conditional diffusion framework for spatiotemporal imputation
Liu M, Huang H, Feng H, et al., 2023 · 2023
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Diffstg: Probabilistic spatio-temporal graph forecasting with denoising diffusion models
Wen H, Lin Y, Xia Y, et al., 2023 · 2023
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