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Recently, denoising diffusion models have led to significant breakthroughs in the generation of images, audio and text.
A learning algorithm for continually running fully recurrent neural networks
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Efficient tests for an autoregressive unit root
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Long short-term memory
Hochreiter, S. and Schmidhuber, J · 1997
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Time series prediction using support vector machines: A survey
Sapankevych, N. I. and Sankar, R · 2009
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A review of wind power forecasting models
Wang, X., Guo, P., and Huang, X · 2011
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Hybrid speech recognition with deep bidirectional LSTM
Graves, A., Jaitly, N., and Mohamed, A.-R · 2013
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Auto-encoding variational Bayes
Kingma, D. P. and Welling, M · 2014
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Neural machine translation by jointly learning to align and translate
Bahdanau, D., Cho, K., and Bengio, Y · 2015
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Scheduled sampling for sequence prediction with recurrent neural networks
Bengio, S., Vinyals, O., Jaitly, N., and Shazeer, N · 2015
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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Time series classification from scratch with deep neural networks: A strong baseline
Wang, Z., Yan, W., and Oates, T · 2017
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Sigmoid-weighted linear units for neural network function approximation in reinforcement learning
Elfwing, S., Uchibe, E., and Doya, K · 2018
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Modeling long-and short-term temporal patterns with deep neural networks
Lai, G., Chang, W.-C., Yang, Y., and Liu, H · 2018
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mixup: Beyond empirical risk minimization
Zhang, H., Cisse, M., Dauphin, Y. N., and Lopez-Paz, D · 2018
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Literature review: Machine learning techniques applied to financial market prediction
Henrique, B. M., Sobreiro, V. A., and Kimura, H · 2019
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Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting
Li, S., Jin, X., Xuan, Y., Zhou, X., Chen, W., Wang, Y.-X., and Yan, X · 2019
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N-BEATS: Neural basis expansion analysis for interpretable time series forecasting
Oreshkin, B. N., Carpov, D., Chapados, N., and Bengio, Y · 2019
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WaveGrad: Estimating gradients for waveform generation
Chen, N., Zhang, Y., Zen, H., Weiss, R. J., Norouzi, M., and Chan, W · 2020
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
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DiffWave: A versatile diffusion model for audio synthesis
Diffusion-based time series imputation and forecasting with structured state space models
Alcaraz, J. M. L. and Strodthoff, N · 2022
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Analytic-DPM: An analytic estimate of the optimal reverse variance in diffusion probabilistic models
Bao, F., Li, C., Zhu, J., and Zhang, B · 2022
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Dynamic dual-output diffusion models
Benny, Y. and Wolf, L · 2022
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DEPTS: Deep expansion learning for periodic time series forecasting
Fan, W., Zheng, S., Yi, X., Cao, W., Fu, Y., Bian, J., and Liu, T.-Y · 2022
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Guided-TTS: A diffusion model for text-to-speech via classifier guidance
Kim, H., Kim, S., and Yoon, S · 2022
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Non-stationary transformers: Rethinking the stationarity in time series forecasting
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Kong, Z., Ping, W., Huang, J., Zhao, K., and Catanzaro, B · 2020
Cited alongside, same era.
ILVR: Conditioning method for denoising diffusion probabilistic models
Choi, J., Kim, S., Jeong, Y., Gwon, Y., and Yoon, S · 2021
Cited alongside, same era.
Reversible instance normalization for accurate time-series forecasting against distribution shift
Kim, T., Kim, J., Tae, Y., Park, C., Choi, J.-H., and Choo, J · 2021
Cited alongside, same era.
Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting
Liu, S., Yu, H., Liao, C., Li, J., Lin, W., Liu, A. X., and Dustdar, S · 2021
Cited alongside, same era.
Autoregressive denoising diffusion models for multivariate probabilistic time series forecasting
Rasul, K., Seward, C., Schuster, I., and Vollgraf, R · 2021
Cited alongside, same era.
Denoising diffusion implicit models
Song, J., Meng, C., and Ermon, S · 2021
Cited alongside, same era.
CSDI: Conditional score-based diffusion models for probabilistic time series imputation
Tashiro, Y., Song, J., Song, Y., and Ermon, S · 2021
Cited alongside, same era.
Liu, Y., Wu, H., Wang, J., and Long, M · 2022
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DPM-Solver: A fast ODE solver for diffusion probabilistic model sampling in around 10 steps
Lu, C., Zhou, Y., Bao, F., Chen, J., Li, C., and Zhu, J · 2022
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Repaint: Inpainting using denoising diffusion probabilistic models
Lugmayr, A., Danelljan, M., Romero, A., Yu, F., Timofte, R., and Van Gool, L · 2022
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High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022
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Diffusion probabilistic modeling for video generation
Yang, R., Srivastava, P., and Mandt, S · 2022
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DiffuSeq: Sequence to sequence text generation with diffusion models
Gong, S., Li, M., Feng, J., Wu, Z., and Kong, L · 2023
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A time series is worth 64 words: Long-term forecasting with transformers
Nie, Y., Nguyen, N. H., Sinthong, P., and Kalagnanam, J · 2023
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Are transformers effective for time series forecasting?
Zeng, A., Chen, M., Zhang, L., and Xu, Q · 2023
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Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting
Zhang, Y. and Yan, J · 2023
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