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Found 5 papers, 1 papers with code
基于双星型自注意力网络的搜索结果多样化方法(Search Result Diversification Framework Based on Dual Star-shaped Self-Attention Network)
“相关研究指出, 用户提交给搜索引擎的查询通常为短查询。由于自然语言本身的特点, 短查询通常具有歧义性, 同一个查询可以指代不同的事物, 或同一事物的不同方面。为了让搜索结果尽可能满足用户多样化的信息需求, 搜索引擎需要对返回的结果进行多样化排序, 搜索结果多样化技术应运而生。目前已有的基于全局交互的多样化方法通过全连接的自注意力网络捕获全体候选文档间的交互关系, 取得了较好的效果。但由于此类方法只考虑文档间的相关关系, 并没有考虑到文档是否具有跟查询相关的有效信息, 在训练数据有限的条件下效率相对较低。该文提出了一种基于双星型自注意力网络的搜索结果多样化方法, 将全连接结构改为星型拓扑结构, 并嵌入查询信息以高效率地提取文档跟查询相关的全局交互特征。相关实验结果显示, 该模型相对于基于全连接自注意力网络的多样化方法, 具备显著的性能优势。”
Enabling Large Language Models to Learn from Rules
The current knowledge learning paradigm of LLMs is mainly based on learning from examples, in which LLMs learn the internal rule implicitly from a certain number of supervised examples.
Atomic and Subgraph-aware Bilateral Aggregation for Molecular Representation Learning
In this paper, we introduce a new model for molecular representation learning called the Atomic and Subgraph-aware Bilateral Aggregation (ASBA), which addresses the limitations of previous atom-wise and subgraph-wise models by incorporating both types of information.
A Brief History of Recommender Systems
Soon after the invention of the Internet, the recommender system emerged and related technologies have been extensively studied and applied by both academia and industry.
InvisibiliTee: Angle-agnostic Cloaking from Person-Tracking Systems with a Tee
After a survey for person-tracking system-induced privacy concerns, we propose a black-box adversarial attack method on state-of-the-art human detection models called InvisibiliTee.
