Fast copula variational inference
文献类型：期刊论文
| 作者 | J. Chi; J. Ouyang; A. Zhang; X. Wang and X. Li |
| 刊名 | Journal of Experimental and Theoretical Artificial Intelligence
 |
| 出版日期 | 2022 |
| 卷号 | 34期号:2页码:295-310 |
| ISSN号 | 0952813X |
| DOI | 10.1080/0952813X.2021.1871970 |
| 英文摘要 | Mean-field variational inference, built on fully factorisations, can be efficiently solved; however, it ignores the dependencies between latent variables, resulting in lower performance. To address this, the copula variational inference (CVI) method is proposed by using the well-established copulas to effectively capture posterior dependencies, leading to better approximations. However, it suffers from a computational issue, where the optimisation for big models with massive latent variables is quite time-consuming. This is mainly caused by the expensive sampling when forming noisy Monte Carlo gradients in CVI. For CVI speedup, in this paper we propose a novel fast CVI (abbr. FCVI). In FCVI, we derive the gradient of CVI objective by an expectation of the mean-field factorisation. Therefore, we can achieve a much efficient sampling from the (Formula presented.) -dimensional mean-field factorisation, enabling to reduce the sampling complexity from (Formula presented.) to (Formula presented.). To evaluate FCVI, we compare it against baseline methods on modelling performance and runtime. Experimental results demonstrate that FCVI is on a par with CVI, but runs much faster. 2021 Informa UK Limited, trading as Taylor & Francis Group. |
| URL标识 | 查看原文 |
| 源URL | [http://ir.ciomp.ac.cn/handle/181722/66663]  |
| 专题 | 中国科学院长春光学精密机械与物理研究所 |
| 推荐引用方式 GB/T 7714 | J. Chi,J. Ouyang,A. Zhang,et al. Fast copula variational inference[J]. Journal of Experimental and Theoretical Artificial Intelligence,2022,34(2):295-310. |
| APA | J. Chi,J. Ouyang,A. Zhang,&X. Wang and X. Li.(2022).Fast copula variational inference.Journal of Experimental and Theoretical Artificial Intelligence,34(2),295-310. |
| MLA | J. Chi,et al."Fast copula variational inference".Journal of Experimental and Theoretical Artificial Intelligence 34.2(2022):295-310. |
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