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Generative models have shown strong generation ability while efficient likelihood estimation is less explored. Energy-based models~(EBMs) define a flexible energy function to parameterize unnormalized densities efficiently but are notorious for being
Externí odkaz:
http://arxiv.org/abs/2403.01666
Session-based recommendation (SR) aims to dynamically recommend items to a user based on a sequence of the most recent user-item interactions. Most existing studies on SR adopt advanced deep learning methods. However, the majority only consider a spe
Externí odkaz:
http://arxiv.org/abs/2312.14957
Session-based recommendation (SR) has gained increasing attention in recent years. Quite a great amount of studies have been devoted to designing complex algorithms to improve recommendation performance, where deep learning methods account for the ma
Externí odkaz:
http://arxiv.org/abs/2212.07030
Session-based recommendation which has been witnessed a booming interest recently, focuses on predicting a user's next interested item(s) based on an anonymous session. Most existing studies adopt complex deep learning techniques (e.g., graph neural
Externí odkaz:
http://arxiv.org/abs/2201.10782
Energy-based models (EBMs) provide an elegant framework for density estimation, but they are notoriously difficult to train. Recent work has established links to generative adversarial networks, where the EBM is trained through a minimax game with a
Externí odkaz:
http://arxiv.org/abs/2111.00929
Publikováno v:
In Environmental Technology & Innovation August 2024 35
Autor:
Liang, Cheng, Du, Hong-Qiang, Geng, Cong, Yu, Xinxin, Jiang, Xiongzhuang, Huang, Shangwei, Long, Fei, Han, Liyuan, Li, Wangnan, Liang, Guijie, Li, Bin, Cheng, Yi-Bing, Peng, Yong
Publikováno v:
In Materials Today Energy August 2024 44
Publikováno v:
In International Journal of Hydrogen Energy 5 June 2024 69:1305-1318
Publikováno v:
In Journal of Energy Storage 1 February 2024 78