Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Bryan Kian Hsiang Low"'
Publikováno v:
Machine Learning.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9e750452b3af3d8ac4b5cfa888526f6f
https://doi.org/10.1007/s10994-023-06304-1
https://doi.org/10.1007/s10994-023-06304-1
Publikováno v:
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence.
Data valuation in machine learning (ML) is an emerging research area that studies the worth of data in ML. Data valuation is used in collaborative ML to determine a fair compensation for every data owner and in interpretable ML to identify the most r
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f33b6738a1d2107695069973a86dab81
https://doi.org/10.24963/ijcai.2022/782
https://doi.org/10.24963/ijcai.2022/782
Publikováno v:
Artificial Intelligence. 315:103837
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9209deced34eaa595e68ddc649199987
https://doi.org/10.1016/j.artint.2022.103837
https://doi.org/10.1016/j.artint.2022.103837
Publikováno v:
WWW
This paper presents an active distillation method for a local institution (e.g., hospital) to find the best queries within its given budget to distill an on-server black-box model’s predictive knowledge into a local surrogate with transparent param
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c86d30cc5136761c6a3c011e37c6d984
https://doi.org/10.1145/3442381.3449944
https://doi.org/10.1145/3442381.3449944
This paper presents a novel collaborative generative modeling (CGM) framework that incentivizes collaboration among self-interested parties to contribute data to a pool for training a generative model (e.g., GAN), from which synthetic data are drawn
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0310a7ec577a7f979b2b49a9ac66a686
Publikováno v:
CoNEXT
Sketches have successfully provided accurate and fine-grained measurements (e.g., flow size and heavy hitters) which are imperative for network management. In particular, Count-Min (CM) sketch is widely utilized in many applications due to its simple
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::72aca15b7320c18e85a3c2fd108b3338
https://doi.org/10.1145/3386367.3432729
https://doi.org/10.1145/3386367.3432729
Publikováno v:
AAAI
This paper presents a variational Bayesian kernel selection (VBKS) algorithm for sparse Gaussian process regression (SGPR) models. In contrast to existing GP kernel selection algorithms that aim to select only one kernel with the highest model eviden
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::70362bf98a32fdd0fd558b668313d33b
http://arxiv.org/abs/1912.02641
http://arxiv.org/abs/1912.02641
Publikováno v:
arXiv
IJCNN
IJCNN
This paper presents a novel variational inference framework for deriving a family of Bayesian sparse Gaussian process regression (SGPR) models whose approximations are variationally optimal with respect to the full-rank GPR model enriched with variou
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fc412b6ce9dbbe73d8d1960d4130c4ed
https://hdl.handle.net/1721.1/129343
https://hdl.handle.net/1721.1/129343
Publikováno v:
IJCAI
This paper presents a simple yet principled approach to boosting the robustness of the residual network (ResNet) that is motivated by the dynamical system perspective. Namely, a deep neural network can be interpreted using a partial differential equa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::77561fd590b963df7729fa4d09d526f8
Publikováno v:
AI Magazine; Vol 38 No 3: Fall 2017; 102-105
Artificial Intelligence (AI) research in Singapore is focused on accelerating the country’s development into a Smart Nation. Specifically, AI has been employed extensively in either augmenting the intelligence of humans or in developing automated m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::701065a1797022183935ca37d9e633ce
http://aaai.org/ojs/index.php/aimagazine/article/view/2749
http://aaai.org/ojs/index.php/aimagazine/article/view/2749