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Found 12 papers, 4 papers with code
Enhancing Depression-Diagnosis-Oriented Chat with Psychological State Tracking
Depression-diagnosis-oriented chat aims to guide patients in self-expression to collect key symptoms for depression detection.
A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges
To tackle these issues, substantial efforts have been devoted to improving the performance of GNN models in practical real-world scenarios, as well as enhancing their reliability and robustness.
GPS: Graph Contrastive Learning via Multi-scale Augmented Views from Adversarial Pooling
Self-supervised graph representation learning has recently shown considerable promise in a range of fields, including bioinformatics and social networks.
PolyCF: Towards the Optimal Spectral Graph Filters for Collaborative Filtering
Collaborative Filtering (CF) is a pivotal research area in recommender systems that capitalizes on collaborative similarities between users and items to provide personalized recommendations.
A Survey on Graph Neural Networks in Intelligent Transportation Systems
However, most of the research in this area is still concentrated on traffic forecasting, while other ITS domains, such as autonomous vehicles and urban planning, still require more attention.
Graph ODE with Factorized Prototypes for Modeling Complicated Interacting Dynamics
In this paper, we propose a new approach named Graph ODE with factorized prototypes (GOAT) to address the problem.
EduChat: A Large-Scale Language Model-based Chatbot System for Intelligent Education
Currently, EduChat is available online as an open-source project, with its code, data, and model parameters available on platforms (e. g., GitHub https://github. com/icalk-nlp/EduChat, Hugging Face https://huggingface. co/ecnu-icalk ).
A Comprehensive Survey on Deep Graph Representation Learning
Graph representation learning aims to effectively encode high-dimensional sparse graph-structured data into low-dimensional dense vectors, which is a fundamental task that has been widely studied in a range of fields, including machine learning and data mining.
GLCC: A General Framework for Graph-Level Clustering
In this paper, we propose a general graph-level clustering framework named Graph-Level Contrastive Clustering (GLCC) given multiple graphs.
Graphine: A Dataset for Graph-aware Terminology Definition Generation
Unfortunately, the lack of large-scale terminology definition dataset hinders the process toward definition generation.
Criterion-based Heterogeneous Collaborative Filtering for Multi-behavior Implicit Recommendation
Besides, CHCF integrates criterion learning and user preference learning into a unified framework, which can be trained jointly for the interaction prediction of the target behavior.
What are the most important factors that influence the changes in London Real Estate Prices? How to quantify them?
In recent years, real estate industry has captured government and public attention around the world.
Applications General Finance
