Zobrazeno 1 - 10
of 27
pro vyhledávání: '"Vassilis N, Ioannidis"'
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-14 (2022)
Abstract Effective and successful clinical trials are essential in developing new drugs and advancing new treatments. However, clinical trials are very expensive and easy to fail. The high cost and low success rate of clinical trials motivate researc
Externí odkaz:
https://doaj.org/article/51c962f2116b4b5997bc71e72288db26
Autor:
Zichen Wang, Vassilis N. Ioannidis, Huzefa Rangwala, Tatsuya Arai, Ryan Brand, Mufei Li, Yohei Nakayama
Publikováno v:
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a70e562da22b969f00772c5c91095e48
https://doi.org/10.1145/3534678.3542628
https://doi.org/10.1145/3534678.3542628
Publikováno v:
IEEE Transactions on Signal Processing. 68:2586-2597
A plethora of network-science related applications call for inference of spatio-temporal graph processes. Such an inference task can be aided by the underlying graph topology that might jump over discrete modes. For example, the connectivity in dynam
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::eba4d8fb6d26b8f92b164019a2e17f7a
https://doi.org/10.1109/tsp.2020.2984889
https://doi.org/10.1109/tsp.2020.2984889
Effective and successful clinical trials are essential in developing new drugs and advancing new treatments. However, clinical trials are very expensive and easy to fail. The high cost and low success rate of clinical trials motivate research on infe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::59bcf4043ea8f6b83cd756d8933a4088
https://doi.org/10.1101/2021.11.04.21265952
https://doi.org/10.1101/2021.11.04.21265952
Publikováno v:
Scientific reports. 12(1)
Effective and successful clinical trials are essential in developing new drugs and advancing new treatments. However, clinical trials are very expensive and easy to fail. The high cost and low success rate of clinical trials motivate research on infe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=pmid________::7c9e5ff0dce5990dc89196e26754e1b7
https://pubmed.ncbi.nlm.nih.gov/35304504
https://pubmed.ncbi.nlm.nih.gov/35304504
Publikováno v:
IEEE Transactions on Signal Processing. 67:2263-2274
A task of major practical importance in network science is inferring the graph structure from noisy observations at a subset of nodes. Available methods for topology inference typically assume that the process over the network is observed at all node
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::33b11ea500105e9b140234021cbc5f9b
https://doi.org/10.1109/tsp.2019.2903025
https://doi.org/10.1109/tsp.2019.2903025
Publikováno v:
ACSSC
Uncovering anomalies in attributed networks has recently gained popularity due to its importance in unveiling outliers and flagging adversarial behavior in a gamut of data and network science applications including {the Internet of Things (IoT)}, fin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::29be44dbce31bef1694a28b7c8de62cf
http://arxiv.org/abs/2104.08637
http://arxiv.org/abs/2104.08637
Publikováno v:
ICASSP
Semi-supervised learning (SSL) of dynamic processes over graphs is encountered in several applications of network science. Most of the existing approaches are unable to handle graphs with multiple relations, which arise in various real-world networks
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f1ef7d84c5227ade40e5453b6742f34e
https://doi.org/10.1109/icassp40776.2020.9053438
https://doi.org/10.1109/icassp40776.2020.9053438
Publikováno v:
ICASSP
The interconnection of social, email, and media platforms enables adversaries to manipulate networked data and promote their malicious intents. This paper introduces graph neural network architectures that are robust to perturbed networked data. The
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c22a7235c546d901561bad5672d08286
https://doi.org/10.1109/icassp40776.2020.9054325
https://doi.org/10.1109/icassp40776.2020.9054325
The era of "data deluge" has sparked renewed interest in graph-based learning methods and their widespread applications ranging from sociology and biology to transportation and communications. In this context of graph-aware methods, the present paper
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::831898345091fa775b7f59598669730e
http://arxiv.org/abs/2003.07729
http://arxiv.org/abs/2003.07729