Results 231 to 240 of about 1,274,940 (254)

Artificial intelligence in drug discovery from advanced molecular representation to pipeline applications. [PDF]

Front Bioinform
Zhou X, Tao W.
europepmc   +1 more source

MGANSL: multi-network representation generating with generative adversarial network for synthetic lethality prediction. [PDF]

BMC Bioinformatics
Li J, Lu X, Li Z, Liu X, Liu H, Ruan J.
europepmc   +1 more source

U-Trans: a foundation model for seismic waveform representation and enhanced downstream earthquake tasks. [PDF]

Sci Rep
Saad OM, Chen Y, Alkhalifah T.
europepmc   +1 more source

An intrusion detection model for electric vehicle supply equipment based on multi-task learning and probability transfer mechanism. [PDF]

Sci Rep
Yang K, Cai L, Wu J.
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Adaptive graph signal processing for robust multimodal fusion with dynamic semantic alignment. [PDF]

Sci Rep
Karthikeya KV, Rajasekaran AS, Das AK, K VB, Pal S.   +4 more
europepmc   +1 more source

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Contrastive representation learning on dynamic networks

Neural Networks, 2023
Representation learning for dynamic networks is designed to learn the low-dimensional embeddings of nodes that can well preserve the snapshot structure, properties and temporal evolution of dynamic networks. However, current dynamic network representation learning methods tend to focus on estimating or generating observed snapshot structures, paying ...
Pengfei Jiao, Hongjiang Chen, Huijun Tang, Qing Bao, Long Zhang, Zhidong Zhao, Huaming Wu   +6 more
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Robust Road Network Representation Learning

Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 2021
In this work, we propose a robust road network representation learning framework called Toast, which comes to be a cornerstone to boost the performance of numerous demanding transport planning tasks. Specifically, we first propose a traffic context aware skip-gram module to incorporate auxiliary tasks of predicting the traffic context of a target road ...
Yile Chen, Xiucheng Li, Gao Cong, Zhifeng Bao, Cheng Long, Yiding Liu, Arun Kumar Chandran, Richard Ellison   +7 more
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Learning Network Representation Through Reinforcement Learning

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020
Network Representation Learning embeds each node in a network into a low-dimensional real-value vector which can be used for downstream tasks such as link prediction and recommendation. Many existing approaches use unsupervised or (semi-)supervised methods to explore the network topology and learn representations from it.
Siqi Shen, Yongquan Fu, Adele Lu Jia, Huayou Su, Qinglin Wang, Chengsong Wang, Yong Dou   +6 more
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Learning IP network representations

ACM SIGCOMM Computer Communication Review, 2019
We present DIP, a deep learning based framework to learn structural properties of the Internet, such as node clustering or distance between nodes. Existing embedding-based approaches use linear algorithms on a single source of data, such as latency or hop count information, to approximate the position of a node in the Internet.
Mingda Li, Bo Zong, Cristian Lumezanu, Haifeng Chen   +3 more
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