最新的USB4 Gen 3 TP3 Embedding 文件供大家参考！

最新的USB4 Gen 2 TP3 Embedding文件， 大家参考下！

这篇论文是讲述基于协同过滤，因式分解和embedding的托攻击检测

图书分类代码不同的embedding

Learning low-dimensional embeddings of knowledge graphs is a powerful approach used to predict unobserved or missing edges between entities. However, an open challenge in this area is developing techniques that can go beyond simple edge prediction and handle more complex logical queries, which might involve multiple unobserved edges, entities, and variables. For instance, given an incomplete biological knowledge graph, we might want to predict what drugs are likely to target proteins involved with both diseases X and Y?—a query that requires reasoning about all possible proteins that might interact with diseases X and Y. Here we introduce a framework to efficiently make predictions about conjunctive logical queries—a flexible but tractable subset of first-order logic—on incomplete knowledge graphs. In our approach, we embed graph nodes in a low-dimensional space and represent logical operators as learned geometric operations (e.g., translation, rotation) in this embedding space. By performing logical operations within a low-dimensional embedding space, our approach achieves a time complexity that is linear in the number of query variables, compared to the exponential complexity required by a naive enumeration-based approach. We demonstrate the utility of this framework in two application studies on real-world datasets with millions of relations: predicting logical relationships in a network of drug-gene-disease interactions and in a graph-based representation of social interactions derived from a popular web forum.

利用Twitter短文本，在训练词向量时融合进词语考虑带有的情感，得到带有情感信息的词向量。所用模型为SSWE，压缩包内包含三个文本文档：SSWE-h.txt、SSWE-r.txt、SSWE-u.txt。另，训练得到的词向量维度为50.

中文单词向量 该项目提供了100多个中文单词向量（嵌入），它们经过不同表示（密集和稀疏），上下文特征（单词，ngram，字符等）和语料库的训练。 可以轻松获得具有不同属性的预训练向量，并将其用于下游任务。 此外，我们提供了一个中文类比推理数据集CA8和一个评估工具包，供用户评估其词向量的质量。 参考 如果使用这些嵌入和CA8数据集，请引用该论文。 沉力，赵哲，胡仁芬，李文思，刘涛，杜小勇， ，ACL 2018。 @InProceedings{P18-2023, author = "Li, Shen and Zhao, Zhe and Hu, Renfen and Li, Wensi and Liu, Tao and Du, Xiaoyong", title = "Analogical Reasoning on Chinese M

本书是学习POWER PC860的经典参考书，也是北京邮电大学嵌入式系统课程的推荐书目，已绝版。本资源是扫描版，清晰度良好。

Embedding-based News Recommendation for Millions of Users 经典embedding论文 kdd

该项目将不再维护，建议用户访问和使用新软件包 。