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pro vyhledávání: '"Pang,Jun"'
We introduce a novel gene regulatory network (GRN) inference method that integrates optimal transport (OT) with a deep-learning structural inference model. Advances in next-generation sequencing enable detailed yet destructive gene expression assays
Externí odkaz:
http://arxiv.org/abs/2409.15080
In distributed networks, calculating the maximum element is a fundamental task in data analysis, known as the distributed maximum consensus problem. However, the sensitive nature of the data involved makes privacy protection essential. Despite its im
Externí odkaz:
http://arxiv.org/abs/2409.10226
Autor:
Bresson, Roman, Nikolentzos, Giannis, Panagopoulos, George, Chatzianastasis, Michail, Pang, Jun, Vazirgiannis, Michalis
In recent years, Graph Neural Networks (GNNs) have become the de facto tool for learning node and graph representations. Most GNNs typically consist of a sequence of neighborhood aggregation (a.k.a., message-passing) layers, within which the represen
Externí odkaz:
http://arxiv.org/abs/2406.18380
Estimating causal effects in e-commerce tends to involve costly treatment assignments which can be impractical in large-scale settings. Leveraging machine learning to predict such treatment effects without actual intervention is a standard practice t
Externí odkaz:
http://arxiv.org/abs/2403.19289
Autor:
Mu, Chunyan, Pang, Jun
In multiagent systems (MASs), agents' observation upon system behaviours may improve the overall team performance, but may also leak sensitive information to an observer. A quantified observability analysis can thus be useful to assist decision-makin
Externí odkaz:
http://arxiv.org/abs/2310.02614
Autor:
Hu, Hailong, Pang, Jun
Generative adversarial networks (GANs) have shown remarkable success in image synthesis, making GAN models themselves commercially valuable to legitimate model owners. Therefore, it is critical to technically protect the intellectual property of GANs
Externí odkaz:
http://arxiv.org/abs/2306.05233
Autor:
Hu, Hailong, Pang, Jun
Diffusion models have been remarkably successful in data synthesis. However, when these models are applied to sensitive datasets, such as banking and human face data, they might bring up severe privacy concerns. This work systematically presents the
Externí odkaz:
http://arxiv.org/abs/2306.05208
The development of clustering heuristics has demonstrated that Bitcoin is not completely anonymous. Currently, existing clustering heuristics only consider confirmed transactions recorded in the Bitcoin blockchain. However, unconfirmed transactions i
Externí odkaz:
http://arxiv.org/abs/2303.01012