Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Yachen Kang"'
Publikováno v:
IEEE Access, Vol 12, Pp 65117-65127 (2024)
Imitation learning is a widely-used paradigm for decision making that learns from expert demonstrations. Existing imitation algorithms often require multiple interactions between the agent and the environment from which the demonstration is obtained.
Externí odkaz:
https://doaj.org/article/958aa6a3f3a3430e9304b237f49baf7d
Publikováno v:
IJCAI
Recently, diverse primitive skills have been learned by adopting the entropy as intrinsic reward, which further shows that new practical skills can be produced by combining a variety of primitive skills. This is essentially skill transfer, very usefu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f9fa0451278b7b7b5bfca9f07ba370fa
https://doi.org/10.24963/ijcai.2020/401
https://doi.org/10.24963/ijcai.2020/401
Publikováno v:
IJCNN
Collaborative filtering (CF) is one of the most effective approaches for recommender systems by exploiting user-item behavior interactions. However, in real applications, the rating matrix is usually sparse, causing a poor performance. Numerous CF me
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bec58e6ec4bb8a949905e4ca31fad5cb
https://doi.org/10.1109/ijcnn48605.2020.9207009
https://doi.org/10.1109/ijcnn48605.2020.9207009
The purpose of few-shot recognition is to recognize novel categories with a limited number of labeled examples in each class. To encourage learning from a supplementary view, recent approaches have introduced auxiliary semantic modalities into effect
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ca0abb220e3c20b77207ea5a69be165b
Publikováno v:
ICDM Workshops
Collaborative filtering (CF) faces two challenges for recommendations: data sparsity and cold-start issue. One solution is to incorporate the side information and the other is to utilize relevant knowledge. In this paper, a cross-domain deep collabor
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6d0f63a61af4ab7fa94a347862f2a95d
https://doi.org/10.1109/icdmw.2019.00096
https://doi.org/10.1109/icdmw.2019.00096
Publikováno v:
PRICAI 2019: Trends in Artificial Intelligence ISBN: 9783030298937
PRICAI (3)
PRICAI (3)
Collaborative filtering (CF) is among the most effective techniques for recommendations. However, it suffers from data sparsity and cold-start issue. One solution is to incorporate the side information and the other is to learn knowledge from relevan
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4a90397dc40c1e097898078a90c6fbfc
https://doi.org/10.1007/978-3-030-29894-4_42
https://doi.org/10.1007/978-3-030-29894-4_42