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Diffusion models have achieved state-of-the-art performance in generative modeling tasks across various domains.
Bayesian quantile regression
Keming Yu and Rana A Moyeed · 2001
Earlier work this paper cites.
Strictly proper scoring rules, prediction, and estimation
Tilmann Gneiting and Adrian E Raftery · 2007
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Automatic time series forecasting: the forecast package for r
Rob J Hyndman and Yeasmin Khandakar · 2008
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Free energy computations: A mathematical perspective
Gabriel Stoltz, Mathias Rousset, et al · 2010
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Mcmc using hamiltonian dynamics
Radford M Neal et al · 2011
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Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Uber tlc foil response, 2016
FiveThirtyEight · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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Uci machine learning repository
Dua Dheeru and E Karra Taniskidou · 2017
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Real-valued (medical) time series generation with recurrent conditional gans
Cristóbal Esteban, Stephanie L Hyland, and Gunnar Rätsch · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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A multi-horizon quantile recurrent forecaster
Ruofeng Wen, Kari Torkkola, Balakrishnan Narayanaswamy, and Dhruv Madeka · 2017
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Time series forecasting for healthcare diagnosis and prognostics with the focus on cardiovascular diseases
C Bui, N Pham, A Vo, A Tran, A Nguyen, and T Le · 2018
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Forecasting: principles and practice
Rob J Hyndman and George Athanasopoulos · 2018
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A classification of business forecasting problems
Stephan Kolassa and Tim Januschowski · 2018
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Modeling long-and short-term temporal patterns with deep neural networks
Guokun Lai, Wei-Cheng Chang, Yiming Yang, and Hanxiao Liu · 2018
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Deep state space models for time series forecasting
Syama Sundar Rangapuram, Matthias W Seeger, Jan Gasthaus, Lorenzo Stella, Yuyang Wang, and Tim Januschowski · 2018
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Probabilistic forecasting with spline quantile function rnns
Jan Gasthaus, Konstantinos Benidis, Yuyang Wang, Syama Sundar Rangapuram, David Salinas, Valentin Flunkert, and Tim Januschowski · 2019
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Time-series generative adversarial networks
Jinsung Yoon, Daniel Jarrett, and Mihaela Van der Schaar · 2019
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GluonTS: Probabilistic and Neural Time Series Modeling in Python
Alexander Alexandrov, Konstantinos Benidis, Michael Bohlke-Schneider, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Danielle C. Maddix, Syama Rangapuram, David Salinas, Jasper Schulz, Lorenzo Stella, Ali Caner Türkmen, and Yuyang Wang · 2020
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Normalizing kalman filters for multivariate time series analysis
Emmanuel de Bézenac, Syama Sundar Rangapuram, Konstantinos Benidis, Michael Bohlke-Schneider, Richard Kurle, Lorenzo Stella, Hilaf Hasson, Patrick Gallinari, and Tim Januschowski · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Diffwave: A versatile diffusion model for audio synthesis
Zhifeng Kong, Wei Ping, Jiaji Huang, Kexin Zhao, and Bryan Catanzaro · 2020
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Glide: Towards photorealistic image generation and editing with text-guided diffusion models
Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Autoregressive denoising diffusion models for multivariate probabilistic time series forecasting
Kashif Rasul, Calvin Seward, Ingmar Schuster, and Roland Vollgraf · 2021
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Csdi: Conditional score-based diffusion models for probabilistic time series imputation
Yusuke Tashiro, Jiaming Song, Yang Song, and Stefano Ermon · 2021
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Protein structure and sequence generation with equivariant denoising diffusion probabilistic models
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Time-series forecasting with deep learning: a survey
Bryan Lim and Stefan Zohren · 2020
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The m4 competition: 100,000 time series and 61 forecasting methods
Spyros Makridakis, Evangelos Spiliotis, and Vassilios Assimakopoulos · 2020
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Multivariate probabilistic time series forecasting via conditioned normalizing flows
Kashif Rasul, Abdul-Saboor Sheikh, Ingmar Schuster, Urs Bergmann, and Roland Vollgraf · 2020
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Deepar: Probabilistic forecasting with autoregressive recurrent networks
David Salinas, Valentin Flunkert, Jan Gasthaus, and Tim Januschowski · 2020
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Financial time series forecasting with deep learning: A systematic literature review: 2005–2019
Omer Berat Sezer, Mehmet Ugur Gudelek, and Ahmet Murat Ozbayoglu · 2020
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2020
Cited alongside, same era.
Deep learning for time series forecasting: Tutorial and literature survey
Konstantinos Benidis, Syama Sundar Rangapuram, Valentin Flunkert, Yuyang (Bernie) Wang, Danielle Maddix Robinson, Caner Turkmen, Jan Gasthaus, Michael Bohlke-Schneider, David Salinas, Lorenzo Stella, Francois-Xavier Aubet, Laurent Callot, and Tim Januschowski · 2021
Cited alongside, same era.
Namrata Anand and Tudor Achim · 2022
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Blended diffusion for text-driven editing of natural images
Omri Avrahami, Dani Lischinski, and Ohad Fried · 2022
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Modeling temporal data as continuous functions with process diffusion
Marin Biloš, Kashif Rasul, Anderson Schneider, Yuriy Nevmyvaka, and Stephan Günnemann · 2022
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Diffusion models as plug-and-play priors
Alexandros Graikos, Nikolay Malkin, Nebojsa Jojic, and Dimitris Samaras · 2022
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Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2022
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Score-based generative modeling of graphs via the system of stochastic differential equations
Jaehyeong Jo, Seul Lee, and Sung Ju Hwang · 2022
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Denoising diffusion restoration models
Bahjat Kawar, Michael Elad, Stefano Ermon, and Jiaming Song · 2022
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Repaint: Inpainting using denoising diffusion probabilistic models
Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, and Luc Van Gool · 2022
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Palette: Image-to-image diffusion models
Chitwan Saharia, William Chan, Huiwen Chang, Chris Lee, Jonathan Ho, Tim Salimans, David Fleet, and Mohammad Norouzi · 2022
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Deep learning for renewable energy forecasting: A taxonomy, and systematic literature review
Changtian Ying, Weiqing Wang, Jiong Yu, Qi Li, Donghua Yu, and Jianhua Liu · 2022
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Diffusion-based time series imputation and forecasting with structured state space models
Juan Lopez Alcaraz and Nils Strodthoff · 2023
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Generative diffusion for 3d turbulent flows
Marten Lienen, Jan Hansen-Palmus, David Lüdke, and Stephan Günnemann · 2023
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Add and thin: Diffusion for temporal point processes
David Lüdke, Marin Biloš, Oleksandr Shchur, Marten Lienen, and Stephan Günnemann · 2023
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Accelerating guided diffusion sampling with splitting numerical methods
Suttisak Wizadwongsa and Supasorn Suwajanakorn · 2023
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