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Advanced User Credit Risk Prediction Model using LightGBM, XGBoost and Tabnet with SMOTEENN

Autor: Yu, Chang, Jin, Yixin, Xing, Qianwen, Zhang, Ye, Guo, Shaobo, Meng, Shuchen

Bank credit risk is a significant challenge in modern financial transactions, and the ability to identify qualified credit card holders among a large number of applicants is crucial for the profitability of a bank'sbank's credit card business. In the

Externí odkaz: http://arxiv.org/abs/2408.03497

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Real-Time Summarization of Twitter

Autor: Jin, Yixin, Wang, Meiqi, Li, Meng, Zhou, Wenjing, Shen, Yi, Liu, Hao

In this paper, we describe our approaches to TREC Real-Time Summarization of Twitter. We focus on real time push notification scenario, which requires a system monitors the stream of sampled tweets and returns the tweets relevant and novel to given i

Externí odkaz: http://arxiv.org/abs/2407.08125

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Online Learning of Multiple Tasks and Their Relationships : Testing on Spam Email Data and EEG Signals Recorded in Construction Fields

Autor: Jin, Yixin, Zhou, Wenjing, Wang, Meiqi, Li, Meng, Li, Xintao, Hu, Tianyu

This paper examines an online multi-task learning (OMTL) method, which processes data sequentially to predict labels across related tasks. The framework learns task weights and their relatedness concurrently. Unlike previous models that assumed stati

Externí odkaz: http://arxiv.org/abs/2406.18311

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Akademický článek

Comparative Evaluation of STEAP1 Targeting Chimeric Antigen Receptors with Different Costimulatory Domains and Spacers.

Autor: Jin, Yixin¹ (AUTHOR) yixin.jin@rr-research.no, Dunn, Claire¹ (AUTHOR) claire.dunn@ous-research.no, Persiconi, Irene¹ (AUTHOR) irene.persiconi@rr-research.no, Sike, Adam¹ (AUTHOR) adam.sike@ous-research.no, Skorstad, Gjertrud² (AUTHOR), Beck, Carole¹ (AUTHOR) c.a.beck@ous-research.no, Kyte, Jon Amund^1,2,3 (AUTHOR) jonky@ous-hf.no

Publikováno v: International Journal of Molecular Sciences. Jan2024, Vol. 25 Issue 1, p586. 18p.

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Unsupervised Discovery of Object Landmarks as Structural Representations

Autor: Zhang, Yuting, Guo, Yijie, Jin, Yixin, Luo, Yijun, He, Zhiyuan, Lee, Honglak

Publikováno v: CVPR 2018

Deep neural networks can model images with rich latent representations, but they cannot naturally conceptualize structures of object categories in a human-perceptible way. This paper addresses the problem of learning object structures in an image mod

Externí odkaz: http://arxiv.org/abs/1804.04412

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