Monte-Carlo Dropout based Uncertainty Analysis in Input Attributions of Multivariate Temporal Neural Networks
Cited 0 times inCited 0 times in
- Monte-Carlo Dropout based Uncertainty Analysis in Input Attributions of Multivariate Temporal Neural Networks
- Other Titles
- 다변량 시간 신경망의 입력 기여도에 대한 몬테카를로 드랍아웃 기반의 불확실성 분석
- Lee, Ginkyeng
- Kim, Kwang In
- ExplainableAI; uncertainty
- Issue Date
- Graduate School of UNIST
- As deep learning has grown fast, so did the desire to interpret deep learning black boxes. As
a result, many analysis tools have emerged to interpret it. Interpretation in deep learning has
in fact popularized the use of deep learning in many areas including research, manufacturing,
finance, and healthcare which needs relatively accurate and reliable decision making process.
However, there is something we should not overlook. It is uncertainty. Uncertainties of models
are directly reflected in the results of interpretations of model decision as explaining tools are
dependent to models. Therefore, uncertainties of interpreting output from deep learning models
should be also taken into account as quality and cost are directly impacted by measurement
uncertainty. This attempt has not been made yet.
Therefore, we suggest Bayesian input attribution rather than discrete input attribution by
approximating Bayesian inference in deep Gaussian process through dropout to input attribution
in this paper. Then we extract candidates that can sufficiently affect the output of the model,
taking into account both input attribution itself and uncertainty of it.
- Department of Computer Science and Engineering
- Go to Link;
- Appears in Collections:
- Files in This Item:
Monte-Carlo Dropout based Uncertainty Analysis in Input Attributions of Multivariate Temporal Neural Networks.pdf
can give you direct access to the published full text of this article. (UNISTARs only)
Show full item record
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.