Variational inference based unsupervised continual learning
This research is aimed at investigating variational inference based deep learning approach for generative continual learning. Continual learning is aimed at learning a sequence of task, in scenarios where data from past tasks are unavailable. Thus, it emphasizes on learning a sequence of task, witho...
Saved in:
主要作者: | Gao, Zhaoqi |
---|---|
其他作者: | Ponnuthurai Nagaratnam Suganthan |
格式: | Thesis-Master by Coursework |
語言: | English |
出版: |
Nanyang Technological University
2022
|
主題: | |
在線閱讀: | https://hdl.handle.net/10356/155840 |
標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
機構: | Nanyang Technological University |
語言: | English |
相似書籍
-
Unsupervised generative variational continual learning
由: Liu, Guimeng
出版: (2023) -
Low-power neuromorphic circuits for unsupervised spike based learning
由: He, Tong
出版: (2016) -
Gradient inversion-based inference attack against federated learning
由: Chan, Joel Yuan Wei
出版: (2023) -
Structured sparse representations for supervised and unsupervised learning
由: Zeng, Yijie
出版: (2020) -
Learning spike time codes through supervised and unsupervised structural plasticity
由: Roy, Subhrajit
出版: (2016)