Generative Diffusion Models for Sequential Recommendations

Autor: Zolghadr, Sharare, Winther, Ole, Jeha, Paul

Generative models such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) have shown promise in sequential recommendation tasks. However, they face challenges, including posterior collapse and limited representation capacit

Externí odkaz: http://arxiv.org/abs/2410.19429

PSA-GAN: Progressive Self Attention GANs for Synthetic Time Series

Autor: Jeha, Paul, Bohlke-Schneider, Michael, Mercado, Pedro, Kapoor, Shubham, Nirwan, Rajbir Singh, Flunkert, Valentin, Gasthaus, Jan, Januschowski, Tim

Publikováno v: Jeha, P, Bohlke-Schneider, M, Mercado, P, Kapoor, S, Nirwan, R S, Flunkert, V, Gasthaus, J & Januschowski, T 2022, ' PSA-GAN: Progressive Self Attention GANs for Synthetic Time Series ', Paper presented at The Tenth International Conference on Learning Representations, Virtual, 25/04/2022-29/04/2022 . < https://openreview.net/forum?id=Ix_mh42xq5w >

Realistic synthetic time series data of sufficient length enables practical applications in time series modeling tasks, such as forecasting, but remains a challenge. In this paper, we present PSA-GAN, a generative adversarial network (GAN) that gener

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______1202::79177f98db5b8a1356b64525060df135
https://orbit.dtu.dk/en/publications/2e5681cb-d394-4bf7-a985-9994bda18d62