链接: https://arxiv.org/pdf/2212.00522.pdfgithub: https://github.com/cl4ctr/cl4ctr/1.背景 现有的推荐模型的研究中,大部分主要集中于网络结构的优化,想尽可能的去设计网络结构来捕捉有效的交叉特征,如DC…
A novel unsupervised contrastive learning framework for ancient Yi script character dataset constructionThe ancient Yi books have a long history and are one of the most important cultural heritages of humanity. Currently, Yi character image recognition datasets are all constructed by manual handwriting. ...
这篇文章提出了一个名为 Contrastive Learning for CTR prediction (CL4CTR) 的新框架,它直接提高了特征表示的质量,特别是对于低频特征。在 CL4CTR 中,引入了一个对比模块,通过充分利用来自特征的自监督信号来提高特征表示的质量和泛化性。此外,在对比学习中提出了两个约束:特征对齐和域一致性,用于正则化特征表示...
带你读《2022技术人的百宝黑皮书》——A Contrastive Framework for Learning Sentence Representations from Pairwise and Triple- wise Perspective in Angular Space(6) 117 0 0 你挚爱的强哥 | JavaScript 解决vue项目build之后部署到服务器访问的时候出现报错:Uncaught SyntaxError: Unexpected token ‘<‘ chunk...
A simple framework for contrastive learning of visual representations,程序员大本营,技术文章内容聚合第一站。
A Simple Framework for Contrastive Learning of Visual Representations,程序员大本营,技术文章内容聚合第一站。
embedding generation based on graph contrastive learning. scPROTEIN can estimate the uncertainty of peptide quantification, denoise protein data, remove batch effects and encode single-cell proteomic-specific embeddings in a unified framework. We demonstrate that scPROTEIN is efficient for cell clustering,...
Multi-layer embedding contrastive learningRobustnessTemporal knowledge graph reasoning(TKGR) has attracted widespread attention due to its ability to handle dynamic temporal features. However, existing methods face three major challenges: (1) the difficulty of capturing long-distance dependencies in ...
Therefore, this paper proposes a two-stage framework (CLFNet)1 for wind power forecast based on contrastive learning, one of which is the pre-training stage and the other is the regression stage. For the pre-training stage, its components consist of feature extraction module, data construction...
谢邀,人在美国,刚下飞机。最近读到一篇很有价值的论文《A Simple Framework for Contrastive Learning of Visual Representations》,下面就为大家详细解读一下这篇论文。 一、论文背景与概述 在计算机视觉领域,学习有效的视觉表示一直是一个核心问题。传统的有监督学习方法需要大量的标注数据,而获取标注数据往往是昂贵和...