Zobrazeno 1 - 10
of 215
pro vyhledávání: '"Nesreen K. Ahmed"'
Autor:
Yao Xiao, Guixiang Ma, Nesreen K. Ahmed, Mihai Capotă, Theodore L. Willke, Shahin Nazarian, Paul Bogdan
Publikováno v:
Communications Engineering, Vol 2, Iss 1, Pp 1-15 (2023)
Abstract Recent technological advances have contributed to the rapid increase in algorithmic complexity of applications, ranging from signal processing to autonomous systems. To control this complexity and endow heterogeneous computing systems with a
Externí odkaz:
https://doaj.org/article/dce5624382d842e2b61228caa8395f1b
Autor:
Hoda Eldardiry, Xiangnan Kong, Ryan A. Rossi, Rong Zhou, Theodore L. Willke, Nesreen K. Ahmed, John Boaz Lee
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering. 34:2401-2415
Random walks are at the heart of many existing node embedding and network representation learning methods. However, such methods have many limitations that arise from the use of traditional random walks, e.g., the embeddings resulting from these meth
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2cea6374e97b91542f242ef4ed5910b0
https://doi.org/10.1109/tkde.2020.3006475
https://doi.org/10.1109/tkde.2020.3006475
Publikováno v:
Machine Learning and Knowledge Discovery in Databases ISBN: 9783031263897
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1de104d7f7d0fa9dcff91bcf44346fc8
https://doi.org/10.1007/978-3-031-26390-3_6
https://doi.org/10.1007/978-3-031-26390-3_6
Publikováno v:
ACM Transactions on Knowledge Discovery from Data. 15:1-27
Temporal networks representing a stream of timestamped edges are seemingly ubiquitous in the real world. However, the massive size and continuous nature of these networks make them fundamentally challenging to analyze and leverage for descriptive and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cdaed6cf0e3177b60976d0f4c7ab88c4
https://doi.org/10.1145/3442202
https://doi.org/10.1145/3442202
Autor:
Nesreen K. Ahmed, Giovanni Petri, Jonathan D. Cohen, Sebastian Musslick, Theodeore L. Willke, David C. Turner, Kayhan Ozcimder, Biswadip Dey
Publikováno v:
Nature Physics. 17:646-651
The ability to learn new tasks and generalize to others is a remarkable characteristic of both human brains and recent artificial intelligence systems. The ability to perform multiple tasks simultaneously is also a key characteristic of parallel arch
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::15a8421284c3a801a38d15751f24ee66
https://doi.org/10.1038/s41567-021-01170-x
https://doi.org/10.1038/s41567-021-01170-x
Publikováno v:
Companion Proceedings of the Web Conference 2022.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8fb2cf873c25fffe6f212d13f9944bab
https://doi.org/10.1145/3487553.3524255
https://doi.org/10.1145/3487553.3524255
Publikováno v:
ACM Transactions on Knowledge Discovery from Data. 14:1-37
Structural roles define sets of structurally similar nodes that are more similar to nodes inside the set than outside, whereas communities define sets of nodes with more connections inside the set than outside. Roles based on structural similarity an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::331efca261d5806938094f1871dbc009
https://doi.org/10.1145/3397191
https://doi.org/10.1145/3397191
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering. 32:438-452
This paper presents a general inductive graph representation learning framework called $\text{DeepGL}$ DeepGL for learning deep node and edge features that generalize across-networks. In particular, $\text{DeepGL}$ DeepGL begins by deriving a set of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::36e94f885cffcdf571d02fa83c35daa0
https://doi.org/10.1109/tkde.2018.2878247
https://doi.org/10.1109/tkde.2018.2878247
Autor:
Guixiang Ma, Yao Xiao, Mihai Capota, Theodore L. Willke, Shahin Nazarian, Paul Bogdan, Nesreen K. Ahmed
Publikováno v:
2021 IEEE International Conference on Big Data (Big Data).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7d8be70eba7c863ceed834b768fbd55d
https://doi.org/10.1109/bigdata52589.2021.9671685
https://doi.org/10.1109/bigdata52589.2021.9671685
Autor:
Alexander Heinecke, Evangelos Georganas, Nesreen K. Ahmed, Dhiraj D. Kalamkar, Vasimuddin, Sasikanth Avancha, Sanchit Misra, Guixiang Ma, Ramanarayan Mohanty
Publikováno v:
SC
Full-batch training on Graph Neural Networks (GNN) to learn the structure of large graphs is a critical problem that needs to scale to hundreds of compute nodes to be feasible. It is challenging due to large memory capacity and bandwidth requirements
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::68b9efba91450ff8075b812045822ddf
https://doi.org/10.1145/3458817.3480856
https://doi.org/10.1145/3458817.3480856