Graph neural networksResponsible artificial intelligenceUser modeling is a key topic in many applications, mainly social networks and information retrieval systems. To assess the effectiveness of a user modeling approach, its capability to classify personal characteristics (e.g., the gender, age, or ...
AI techniques, especially graph neural networks (GNNs), help approximate chemical simulations with significantly lower computational cost, particularly as the system size increases. This holds tremendous promise in using AI-enabled simulated techniques to replicate materials systems of greater complexity. ...
Explainable Graph Neural Networks- Blog post that provides a brief overview of XAI methods for graph neural networks (GNNs). Videos and presentations ICML 2019 session - Robust statistics and interpretability Courses Kaggle - Machine Learning Explainability- Kaggle course about the basics of XAI with ...
Prompt flow allows developers to create executable flows that link LLMs, prompts, and Python tools through a visualized graph. It also enables developers to debug, share, and iterate their flows with ease through team collaboration and historical tracking. Moreover, prompt flow allows developers to...
Connecting ai: Merging large language models and knowledge graph. Computer. 2023;56(11):103–8. 10. Han J et al. Data mining concepts and techniques third edition. 2012. 11. Motlagh F et al. Large language models in cybersecurity: state-of-the-art. arXiv preprint arXiv:2402....
Tools for graph structure recovery and dependencies are included.” captum "Model interpretability and understanding for PyTorch.” causalml "Uplift modeling and causal inference with machine learning algorithms.” cdt15, Causal Discovery Lab., Shiga University "LiNGAM is a new method for estimating ...
Molecular networks are commonly represented as graphs detailing interactions between molecules. Gene expression data can be assigned to the vertices of these graphs. In other words, gene expression data can be structured by utilizing molecular network information as prior knowledge. Graph-CNNs can be ...