no code implementations • 23 Mar 2024 • Zhenglin Li, Yangchen Huang, Mengran Zhu, Jingyu Zhang, JingHao Chang, Houze Liu
Our method focuses on manipulating the feature map extracted from the Diffusion Model to be more semantically useful, and for this, we propose two methods: Feature Attention and FDAF.
no code implementations • 15 Mar 2024 • Ye Zhang, Mengran Zhu, Kailin Gui, Jiayue Yu, Yong Hao, Haozhan Sun
In this study, we explore the efficiency of the Monte Carlo Tree Search (MCTS), a prominent decision-making algorithm renowned for its effectiveness in complex decision environments, contingent upon the volume of simulations conducted.
no code implementations • 28 Feb 2024 • Mengran Zhu, Ye Zhang, Yulu Gong, Kaijuan Xing, Xu Yan, Jintong Song
In the realm of consumer lending, accurate credit default prediction stands as a critical element in risk mitigation and lending decision optimization.
no code implementations • 27 Feb 2024 • Mengran Zhu, Ye Zhang, Yulu Gong, Changxin Xu, Yafei Xiang
Credit card fraud detection is a critical challenge in the financial sector, demanding sophisticated approaches to accurately identify fraudulent transactions.
no code implementations • 25 Feb 2024 • Hanyi Yu, Shuning Huo, Mengran Zhu, Yulu Gong, Yafei Xiang
This paper introduces a deep learning-based prediction method for autonomous driving lane change behavior, aiming to facilitate safe lane changes and thereby improve road safety.
no code implementations • 25 Feb 2024 • Shuning Huo, Yafei Xiang, Hanyi Yu, Mengran Zhu, Yulu Gong
In recent years, advancements in natural language processing (NLP) have been fueled by deep learning techniques, particularly through the utilization of powerful computing resources like GPUs and TPUs.
no code implementations • 25 Feb 2024 • Yafei Xiang, Hanyi Yu, Yulu Gong, Shuning Huo, Mengran Zhu
With the rapid development of artificial intelligence technology, Transformer structural pre-training model has become an important tool for large language model (LLM) tasks.
no code implementations • 15 Feb 2024 • Mengran Zhu, Yulu Gong, Yafei Xiang, Hanyi Yu, Shuning Huo
Anomaly detection is a critical challenge across various research domains, aiming to identify instances that deviate from normal data distributions.
no code implementations • 15 Feb 2024 • Yulu Gong, Mengran Zhu, Shuning Huo, Yafei Xiang, Hanyi Yu
In the age of the Internet, people's lives are increasingly dependent on today's network technology.