在本文中,值得强调的是,我们提议在基于基础化学知识的微调期间提供一个化学提示缩小这一差距。受启发于prompt-tuning,一个已经在很多领域的自然语言处理任务中有出色的表现的新兴范式,为基于基础化学知识的分子图设计合适的提示去实现更可靠的预测是很重要的。 为此,我们提出了一个面向化学元素的知识图谱,它用一种有条...
Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning qwe qwer 3 人赞同了该文章 论文地址:arxiv.org/pdf/2006.1038 方法简介 本文研究了基于RL方法+知识图谱(KG)来处理交互式推荐系统(IRS)的问题,这为推荐决策提供了丰富的辅助信息。不是从头开始学习RL策略,而是利用从KG中学到的项目相...
Deep learning models can accurately predict molecular properties and help making the search for potential drug candidates faster and more efficient. Many existing methods are purely data driven, focusing on exploiting the intrinsic topology and construct
Graph embeddingPredictionLearner modelMedical fieldThe training curriculum for medical doctors requires the intensive and rapid assimilation of a lot of knowledge. To help medical students optimize their learning path, the SIDES 3.0 national French project aims to extend an existing platform with ...
知识图谱词嵌入(Knowledge Graph Embedding,KGE)模型:如图6的右侧部分,将知识图谱三元组中的前2个(电影ID和关系实体)作为输入,预测出第3个(目标实体)。 图6 MKR框架 在3个子模型中,最关键的是交叉压缩单元模型。下面就先从该模型开始一步一步地实现MKR框架。
To this end, we propose a method called Knowledge Graph-enhanced Hierarchical Reinforcement learning for interactive and explainable recommendation (KGHR) to harness the advantages of both DRL and knowledge graphs (KGs) to deal with the above two issues of DRL-based IRSs. Specifically, the ...
Firstly, we use coreference contrastive learning to capture potential commonsense knowledge. Secondly, we construct a heterogeneous graph to enhance the graph structure information according to the original document and commonsense knowledge. Lastly, CGDRE infers relations on the aggregated graph and uses ...
Representation Learning-Prof. Li Shang and Prof. Robert Dick LLM4EDA暑期学校-复旦 40 0 Large Language Model in EDA-余备副教授 LLM4EDA暑期学校-复旦 88 0 大语言模型在数据稀缺领域中的应用:以模拟电路为例-陈子豪 LLM4EDA暑期学校-复旦 60 0 通用人工智能时代的EDA-杨帆教授 LLM4EDA暑期学校-复...
21|Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation 一、Motivation 现在的推荐方法很多只考虑单一特征,1)复杂的用户行为之间的内部依赖 2) 商品之间的side information。 3)多种类型的用户交互的动态特性。提出框架:KHGT,1)捕获行为内部的信息 2)精确的判断哪些行为对于最终...
《Learning to Infer and Execute 3D Shape Programs》(ICLR 2019) GitHub:O网页链接《Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding》(NeurIPS 2018) GitHub: O网页链接《An End-to-End Deep Learning Architecture for Graph Classification》(AAAI 2018) GitHub: O网页链接...