The Elastic Weight Consolidation Penalty Is Empirically Valid Reply

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA(2018)

引用 19|浏览349
暂无评分
摘要
In our recent work on elastic weight consolidation (EWC) (1) we show that forgetting in neural networks can be alleviated by using a quadratic penalty whose derivation was inspired by Bayesian evidence accumulation. In his letter (2), Dr. Huszar provides an alternative form for this penalty by following the standard work on expectation propagation using the Laplace approximation (3). He correctly argues that in cases when more than two tasks are undertaken the two forms of the penalty are different. Dr. Huszar also shows that for a toy linear regression problem his expression appears to be better. We would like to thank Dr. Huszar for pointing out … [↵][1]1To whom correspondence should be addressed. Email: kirkpatrick@google.com. [1]: #xref-corresp-1-1
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要