Poster: Towards condition-independent deep mobile sensing
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Gong, Taesik | ko |
| dc.contributor.author | Kim, Yeonsu | ko |
| dc.contributor.author | Shin, Jinwoo | ko |
| dc.contributor.author | Lee, Sung-Ju | ko |
| dc.date.accessioned | 2019-12-13T10:28:49Z | - |
| dc.date.available | 2019-12-13T10:28:49Z | - |
| dc.date.created | 2019-12-05 | - |
| dc.date.created | 2019-12-05 | - |
| dc.date.issued | 2019-06-20 | - |
| dc.identifier.citation | 17th ACM International Conference on Mobile Systems, Applications, and Services, MobiSys 2019, pp.554 - 555 | - |
| dc.identifier.uri | http://hdl.handle.net/10203/269375 | - |
| 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.language | English | - |
| dc.publisher | Association for Computing Machinery, Inc | - |
| dc.title | Poster: Towards condition-independent deep mobile sensing | - |
| dc.type | Conference | - |
| dc.identifier.scopusid | 2-s2.0-85069165037 | - |
| dc.type.rims | CONF | - |
| dc.citation.beginningpage | 554 | - |
| dc.citation.endingpage | 555 | - |
| dc.citation.publicationname | 17th ACM International Conference on Mobile Systems, Applications, and Services, MobiSys 2019 | - |
| dc.identifier.conferencecountry | KO | - |
| dc.identifier.conferencelocation | InterContinental Hotel Seoul Coex | - |
| dc.identifier.doi | 10.1145/3307334.3328622 | - |
| dc.contributor.localauthor | Lee, Sung-Ju | - |
| dc.contributor.nonIdAuthor | Gong, Taesik | - |
| dc.contributor.nonIdAuthor | Kim, Yeonsu | - |
| dc.contributor.nonIdAuthor | Shin, Jinwoo | - |
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