Výsledky vyhledávání - "ZHOU Jiandong"

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DTR-Bench: An in silico Environment and Benchmark Platform for Reinforcement Learning Based Dynamic Treatment Regime

Autor: Luo, Zhiyao, Zhu, Mingcheng, Liu, Fenglin, Li, Jiali, Pan, Yangchen, Zhou, Jiandong, Zhu, Tingting

Reinforcement learning (RL) has garnered increasing recognition for its potential to optimise dynamic treatment regimes (DTRs) in personalised medicine, particularly for drug dosage prescriptions and medication recommendations. However, a significant

Externí odkaz: http://arxiv.org/abs/2405.18610

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A Brief Review of Hypernetworks in Deep Learning

Autor: Chauhan, Vinod Kumar, Zhou, Jiandong, Lu, Ping, Molaei, Soheila, Clifton, David A.

Hypernetworks, or hypernets for short, are neural networks that generate weights for another neural network, known as the target network. They have emerged as a powerful deep learning technique that allows for greater flexibility, adaptability, dynam

Externí odkaz: http://arxiv.org/abs/2306.06955

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Dynamic Inter-treatment Information Sharing for Individualized Treatment Effects Estimation

Autor: Chauhan, Vinod Kumar, Zhou, Jiandong, Ghosheh, Ghadeer, Molaei, Soheila, Clifton, David A.

Estimation of individualized treatment effects (ITE) from observational studies is a fundamental problem in causal inference and holds significant importance across domains, including healthcare. However, limited observational datasets pose challenge

Externí odkaz: http://arxiv.org/abs/2305.15984

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Motif-aware temporal GCN for fraud detection in signed cryptocurrency trust networks

Autor: Li, Song, Zhou, Jiandong, MO, Chong, LI, Jin, Tso, Geoffrey K. F., Tian, Yuxing

Graph convolutional networks (GCNs) is a class of artificial neural networks for processing data that can be represented as graphs. Since financial transactions can naturally be constructed as graphs, GCNs are widely applied in the financial industry

Externí odkaz: http://arxiv.org/abs/2211.13123

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