Results 31 to 40 of about 3,653 (212)
Deepfake Network Architecture Attribution
Proceedings of the AAAI Conference on Artificial Intelligence, 2022 With the rapid progress of generation technology, it has become necessary to attribute the origin of fake images. Existing works on fake image attribution perform multi-class classification on several Generative Adversarial Network (GAN) models and obtain high accuracies.
Tianyun Yang +4 more
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Constrained Consistency Modeling for Attributed Network Embedding
IEEE Access, 2019 Network embedding has emerged as a fundamental approach to network analysis tasks. Its main purpose is to learn a suitable mapping function to convert nodes in networks into a low-dimensional representations.
Xuan Zang +3 more
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Attributes for Improved Attributes: A Multi-Task Network for Attribute Classification
CoRR, 2016 Attributes, or semantic features, have gained popularity in the past few years in domains ranging from activity recognition in video to face verification. Improving the accuracy of attribute classifiers is an important first step in any application which uses these attributes.
Emily M. Hand, Rama Chellappa
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Embedding Networks with Edge Attributes [PDF]
Proceedings of the 29th on Hypertext and Social Media, 2018 Predicting links in information networks requires deep understanding and careful modeling of network structure. Network embedding, which aims to learn low-dimensional representations of nodes, has been used successfully for the task of link prediction in the past few decades.
Palash Goyal +3 more
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DANE-MDA: Predicting microRNA-disease associations via deep attributed network embedding
iScience, 2021 Summary: Predicting the microRNA-disease associations by using computational methods is conductive to the efficiency of costly and laborious traditional bio-experiments.
Bo-Ya Ji +4 more
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Deep Attributed Network Embedding [PDF]
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018 Network embedding has attracted a surge of attention in recent years. It is to learn the low-dimensional representation for nodes in a network, which benefits downstream tasks such as node classification and link prediction. Most of the existing approaches learn node representations only based on the topological structure, yet nodes are often ...
Hongchang Gao, Heng Huang 0001
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Axiomatic Attribution for Deep Networks
CoRR, 2017 We study the problem of attributing the prediction of a deep network to its input features, a problem previously studied by several other works. We identify two fundamental axioms---Sensitivity and Implementation Invariance that attribution methods ought to satisfy.
Mukund Sundararajan +2 more
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Anchor Link Prediction across Attributed Networks via Network Embedding
Entropy, 2019 Presently, many users are involved in multiple social networks. Identifying the same user in different networks, also known as anchor link prediction, becomes an important problem, which can serve numerous applications, e.g., cross-network recommendation,
Shaokai Wang +6 more
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Nane: A Node2vec Extension for Attributed Network Embedding [PDF]
Interdisciplinary Description of Complex SystemsTraditional network representation learning methods focus solely on the network’s topology, ignoring other sources of information that could improve the learning process.
Sarah Abdulkareem Ahmed Ahmed +1 more
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Neural-Brane: Neural Bayesian Personalized Ranking for Attributed Network Embedding
Data Science and Engineering, 2019 Network embedding methodologies, which learn a distributed vector representation for each vertex in a network, have attracted considerable interest in recent years.
Vachik S. Dave +3 more
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