然后,介绍图形机器学习,并特别关注表示学习(representation learning)。然后,我们将分析一个实际的例子,以指导您通过理解理论概念。 本章将涉及以下主题: 机器学习回顾 什么是图上的机器学习,为什么它很重要? 一个通用的分类导航图机器学习算法 1. 环境要求Technical requirements 所有实验将在Python 3.8和Jupyter Notebo...
[34] D. Xuet al.,Inductive representation learning on temporal graphs(2019) Proc. ICLR. [35] M. Noorshams, S. Verma, and A. Hofleitner,TIES: Temporal Interaction Embeddings for enhancing social media integrity at Facebook(2020) arXiv:2002.07917. [36] X. Wanget al.,APAN: Asynchronous P...
ESM2 作为掩蔽 LM 和 ESMFold 用于蛋白质结构预测。 ESM 特征用于无数应用,从预测 3D 结构(在ESMFoldhttps://github.com/facebookresearch/esm中)到蛋白质-配体结合(DiffDockhttps://arxiv.org/abs/2210.01776及其后代)到蛋白质结构生成模型...
Scalability graph可以非常大,像Facebook和Twitter这样的社交网络,它们拥有超过10亿的用户,对这么大的数据进行操作并不容易。幸运的是,大多数自然出现的graph都是“稀疏的”:它们的边数往往与顶点数成线性关系。graph的稀疏性导致可以使用特殊的方法有效计算graph中node的表示。另外,和graph的数据量相比,这些方法的参数要...
Book Description Graph-structured data is ubiquitous throughout the natural and social sciences,from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn,reason,and generalize from this kind of da...
{Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings}, author={Chen, Yu and Wu, Lingfei and Zaki, Mohammed J}, booktitle={Proceedings of the 34th Conference on Neural Information Processing Systems}, month={Dec. 6-12,}, year={2020} } @inproceedings{...
machine-learningknowledge-graphknowledge-graphs-embeddingsgraph-learningdgl UpdatedNov 6, 2023 Python codefuse-ai/codefuse-chatbot Star1.2k An intelligent assistant serving the entire software development lifecycle, powered by a Multi-Agent Framework, working with DevOps Toolkits, Code&Doc Repo RAG, etc...
Microsoft Graph surfaces intelligent insights by bringing together smart machine learning algorithms with a wealth of data and user behavior. Using Microsoft Graph, developers can access this relevant data to make applications contextual and smarter. For example: people picking controls powered by the Pe...
While machine learning has achieved a great success in many research fields, e.g., computer vision, natural language processing and time series processing. Most of these fields use Euclidean domain data, for which the feed forward neural networks, CNNs and RNNs are enough. However, for other...
与七种最先进的基准方法相比,广泛的实验证明LMExplainer在CommonsenseQA和OpenBookQA数据集上优于现有的LM+KG方法。我们将LMExplainer生成的解释与其他算法生成的解释以及人工注释的解释进行比较。结果表明,LMExplainer生成的解释更全面、更清晰。 GraphAgent: Exploiting Large Language Models for Interpretable Learning on...