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Denoising diffusion probabilistic models (DDPMs) are becoming the leading paradigm for generative models.
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Stl: A seasonal-trend decomposition
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The health insurance portability and accountability act of 1996 (hipaa) privacy rule: implications for clinical research
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Forecasting monthly and quarterly time series using stl decomposition
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Forecasting time series with complex seasonal patterns using exponential smoothing
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Dazhi Yang, Vishal Sharma, Zhen Ye, Li Hong Idris Lim, Lu Zhao, and Aloysius Aryaputera · 2014
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Bayesian variable selection for nowcasting economic time series
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton · 2016
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Olof Mogren · 2016
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Variational inference with normalizing flows, 2016
Danilo Jimenez Rezende and Shakir Mohamed · 2016
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Wasserstein gan, 2017
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 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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Improved training of wasserstein gans, 2017
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron Courville · 2017
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Attention is all you need, 2017
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Multivariate bayesian structural time series model, 2018
S. Rao Jammalamadaka, Jinwen Qiu, and Ning Ning · 2018
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Robuststl: A robust seasonal-trend decomposition algorithm for long time series, 2018
Qingsong Wen, Jingkun Gao, Xiaomin Song, Liang Sun, Huan Xu, and Shenghuo Zhu · 2018
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Gluonts: Probabilistic time series models in python, 2019
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 · 2019
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David Salinas, Valentin Flunkert, and Jan Gasthaus · 2019
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Time-series generative adversarial networks
Jinsung Yoon, Daniel Jarrett, and Mihaela Van der Schaar · 2019
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Alexander Alexandrov, Konstantinos Benidis, Michael Bohlke-Schneider, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Danielle C Maddix, Syama Rangapuram, David Salinas, Jasper Schulz, et al · 2020
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Edward Choi, Zhen Xu, Yujia Li, Michael W. Dusenberry, Gerardo Flores, Yuan Xue, and Andrew M. Dai · 2020
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Generative adversarial networks
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2020
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Denoising diffusion probabilistic models, 2020
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Conditional sig-wasserstein gans for time series generation
Hao Ni, Lukasz Szpruch, Magnus Wiese, Shujian Liao, and Baoren Xiao · 2020
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A transformer-based framework for multivariate time series representation learning
George Zerveas, Srideepika Jayaraman, Dhaval Patel, Anuradha Bhamidipaty, and Carsten Eickhoff · 2021
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Diffusion-based time series imputation and forecasting with structured state space models
Juan Miguel Lopez Alcaraz and Nils Strodthoff · 2022
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Diffusion posterior sampling for general noisy inverse problems
Hyungjin Chung, Jeongsol Kim, Michael T Mccann, Marc L Klasky, and Jong Chul Ye · 2022
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Hypertime: Implicit neural representation for time series, 2022
Elizabeth Fons, Alejandro Sztrajman, Yousef El-laham, Alexandros Iosifidis, and Svitlana Vyetrenko · 2022
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Vector quantized diffusion model for text-to-image synthesis, 2022
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Some theoretical insights into wasserstein gans, 2021
Gérard Biau, Maxime Sangnier, and Ugo Tanielian · 2021
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Latent diffusion energy-based model for interpretable text modeling, 2022
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Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting, 2022
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On the constrained time-series generation problem
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Regular time-series generation using sgm
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Videofusion: Decomposed diffusion models for high-quality video generation, 2023
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