Search Results for author: Yanming Fang

Found 7 papers, 2 papers with code

Explored An Effective Methodology for Fine-Grained Snake Recognition

1 code implementation24 Jul 2022 Yong Huang, Aderon Huang, Wei Zhu, Yanming Fang, Jinghua Feng

Then, in order to take full advantage of unlabeled datasets, we use self-supervised learning and supervised learning joint training to provide pre-trained model.

Fine-Grained Image Classification Self-Supervised Learning

Intelligent Credit Limit Management in Consumer Loans Based on Causal Inference

no code implementations10 Jul 2020 Hang Miao, Kui Zhao, Zhun Wang, Linbo Jiang, Quanhui Jia, Yanming Fang, Quan Yu

Based on these testing data, a response model is then built to measure the heterogeneous treatment effect of increasing credit limits (i. e. treatments) for different customers, who are depicted by several control variables (i. e. features).

Causal Inference Management

Large-scale Uncertainty Estimation and Its Application in Revenue Forecast of SMEs

no code implementations2 May 2020 Zebang Zhang, Kui Zhao, Kai Huang, Quanhui Jia, Yanming Fang, Quan Yu

If the uncertainty of an enterprise's revenue forecasting can be estimated, a more proper credit limit can be granted.

Management Uncertainty Quantification

NetDP: An Industrial-Scale Distributed Network Representation Framework for Default Prediction in Ant Credit Pay

no code implementations1 Apr 2020 Jianbin Lin, Zhiqiang Zhang, Jun Zhou, Xiaolong Li, Jingli Fang, Yanming Fang, Quan Yu, Yuan Qi

Considering the above challenges and the special scenario in Ant Financial, we try to incorporate default prediction with network information to alleviate the cold-start problem.

A Semi-supervised Graph Attentive Network for Financial Fraud Detection

1 code implementation28 Feb 2020 Daixin Wang, Jianbin Lin, Peng Cui, Quanhui Jia, Zhen Wang, Yanming Fang, Quan Yu, Jun Zhou, Shuang Yang, Yuan Qi

Additionally, among the network, only very few of the users are labelled, which also poses a great challenge for only utilizing labeled data to achieve a satisfied performance on fraud detection.

Fraud Detection

Deep Interaction Processes for Time-Evolving Graphs

no code implementations25 Sep 2019 xiaofu Chang, Jianfeng Wen, Xuqin Liu, Yanming Fang, Le Song, Yuan Qi

To model the dependency between latent dynamic representations of each node, we define a mixture of temporal cascades in which a node's neural representation depends on not only this node's previous representations but also the previous representations of related nodes that have interacted with this node.

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