Paper Abstract and Keywords |
Presentation |
2021-10-08 10:00
[Tutorial Lecture]
The Past and The Future of Explainable AI Techniques Yoshitaka Kameya (Meijo Univ.) |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Machine learning models of high predictive performance, such as deep neural networks and ensemble models, now play a central role in the current artificial intelligence technologies, and have started to be applied to the problems related to our health or properties. However, one of the primary obstacles here is the opacity of such high-performance models. So far, dozens of techniques for reducing the opacity have been explored, and form a research field called ``explainable aritificial intelligence (XAI).'' In this paper, I review the past literature on XAI, organize key concepts and techniques in the current XAI research, and discuss the future direction of XAI. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
explainable AI / XAI / machine learning / deep learning / / / / |
Reference Info. |
ITE Tech. Rep. |
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Conference Information |
Committee |
BCT IEICE-SIS |
Conference Date |
2021-10-07 - 2021-10-08 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
online |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
System Implementation Technology, Short Range Wireless Systems, Smart Multimedia Systems, Broadcasting Technology, etc. |
Paper Information |
Registration To |
IEICE-SIS |
Conference Code |
2021-10-SIS-BCT |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
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(See Japanese page) |
Title (in English) |
The Past and The Future of Explainable AI Techniques |
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explainable AI |
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XAI |
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machine learning |
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deep learning |
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1st Author's Name |
Yoshitaka Kameya |
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Meijo University (Meijo Univ.) |
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Date Time |
2021-10-08 10:00:00 |
Presentation Time |
40 minutes |
Registration for |
IEICE-SIS |
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Volume (vol) |
vol.45 |
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