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- Gong, Taesik
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| DC Field | Value | Language |
|---|---|---|
| dc.citation.conferencePlace | KO | - |
| dc.citation.endPage | 555 | - |
| dc.citation.startPage | 554 | - |
| dc.citation.title | ACM International Conference on Mobile Systems, Applications, and Services | - |
| dc.contributor.author | Gong, Taesik | - |
| dc.contributor.author | Kim, Yeonsu | - |
| dc.contributor.author | Shin, Jinwoo | - |
| dc.contributor.author | Lee, Sung-Ju | - |
| dc.date.accessioned | 2024-12-30T14:05:05Z | - |
| dc.date.available | 2024-12-30T14:05:05Z | - |
| dc.date.created | 2024-12-28 | - |
| dc.date.issued | 2019-06-17 | - |
| dc.description.abstract | Deep mobile sensing applications are suffering from various individual conditions in the wild. We propose a meta-learned adaptation technique to adapt to a target condition with a few labeled data. We evaluate our system on a public dataset and it outperforms baselines. | - |
| dc.identifier.bibliographicCitation | ACM International Conference on Mobile Systems, Applications, and Services, pp.554 - 555 | - |
| dc.identifier.doi | 10.1145/3307334.3328622 | - |
| dc.identifier.uri | https://scholarworks.unist.ac.kr/handle/201301/85362 | - |
| dc.language | 영어 | - |
| dc.publisher | Association for Computing Machinery, Inc | - |
| dc.title | Poster: Towards condition-independent deep mobile sensing | - |
| dc.type | Conference Paper | - |
| dc.date.conferenceDate | 2019-06-17 | - |
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