Chuan Qin
6 أوراق في مجموعة PaperMetrix
أوراق هذا المؤلف
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A Survey on Knowledge Graph-Based Recommender Systems
2020 · arXiv (Cornell University)
To solve the information explosion problem and enhance user experience in various online applications, recommender systems have been developed to model users preferences. Although numerous efforts have been made toward more personalized recommendations, recommender systems …
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A Survey on Knowledge Graph-Based Recommender Systems : Extended Abstract
2023
To solve the information explosion problem and enhance user experience in various online applications, recommender systems have been developed to model users’ preferences. Although numerous efforts have been made toward more personalized recommendations, recommender systems …
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Harnessing the Power of LLM to Support Binary Taint Analysis
2023 · arXiv (Cornell University)
This paper proposes LATTE, the first static binary taint analysis that is powered by a large language model (LLM). LATTE is superior to the state of the art (e.g., Emtaint, Arbiter, Karonte) in three aspects. …
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Collaboration-Aware Hybrid Learning for Knowledge Development Prediction
2024
In recent years, the rise of online Knowledge Management Systems (KMSs) has significantly improved work efficiency in enterprises. Knowledge development prediction, as a critical application within these online platforms, enables organizations to proactively address knowledge …
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Diffusion Features to Bridge Domain Gap for Semantic Segmentation
2024 · arXiv (Cornell University)
Pre-trained diffusion models have demonstrated remarkable proficiency in synthesizing images across a wide range of scenarios with customizable prompts, indicating their effective capacity to capture universal features. Motivated by this, our study delves into the …
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A Comprehensive Survey on Self-Interpretable Neural Networks
2025 · arXiv (Cornell University)
Neural networks have achieved remarkable success across various fields. However, the lack of interpretability limits their practical use, particularly in critical decision-making scenarios. Post-hoc interpretability, which provides explanations for pre-trained models, is often at risk …