526互联

representation recommendation degeneration contrastive

Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation

[TOC] > [Qiu R., Huang Z., Ying H. and Wang Z. Contrastive learning for representation degeneration problem in sequential recommendation. WSDM, 2022.] ......
Representation Recommendation Degeneration Contrastive Sequential

Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation

[TOC] > [Xia X., Yin H., Yu J., Wang Q., Cui L and Zhang X. Self-supervised hypergraph convolutional networks for session-based recommendation. AAAI, ......
Self-Supervised Recommendation Convolutional Session-based Hypergraph

Self-Supervised Graph Co-Training for Session-based Recommendation

[TOC] > [Xia X., Yin H., Yu J., Shao Y. and Cui L. Self-supervised graph co-training for session-based recommendation. CIKM, 2021.](http://arxiv.org/a ......
Self-Supervised Recommendation Session-based Co-Training Supervised

Global Context Enhanced Graph Neural Networks for Session-based Recommendation

[TOC] > [Wang Z., Wei W., Cong G., Li X., Mao X. and Qiu M. Global context enhanced graph neural networks for session-based recommendation. SIGIR, 202 ......
Recommendation Session-based Enhanced Networks Context

Neural Attentive Session-based Recommendation

[TOC] >[ Li J., Ren P., Chen Z., Ren Z., Lian T. and Ma J. Neural attentive session-based recommendation. CIKM, 2017.](http://arxiv.org/abs/1711.04725 ......
Recommendation Session-based Attentive Session Neural

ARC060D - Best Representation

诈骗题。给了个模数但是答案根本达不到那个级别。 先提前给出一个引理，如果长度为 $2n$ 的 $s$ 有 $s[1,n]=s[n+1,2n]$ 并且 $s[1,m]=s[m+1,2m](mn-x$，那么就有最左边和最右边的 $n-border$ 串相等。两个拼起来，根据引理就有更小的循环节，这是不被 ......
Representation 060D Best ARC 060

[论文速览] MAGE@MAsked Generative Encoder to Unify Representation Learning and Image Synthesis

## Pre title: MAGE: MAsked Generative Encoder to Unify Representation Learning and Image Synthesis accepted: CVPR2023 paper: https://arxiv.org/abs/221 ......
Representation Generative Synthesis Learning Encoder

Uncovering the Representation of Spiking Neural Networks Trained with Surrogate Gradient

郑重声明：原文参见标题，如有侵权，请联系作者，将会撤销发布！ Published in Transactions on Machine Learning Research (04/2023) ......
Representation Uncovering Surrogate Networks Gradient

Memory Priority Model for Session-based Recommendation

[TOC] > [Liu Q., Zeng Y., Mokhosi R. and Zhang H. STAMP: Short-term attention/memory priority model for session-based recommendation. KDD, 2018.](http ......
Recommendation Session-based Priority Session Memory

Oceans on a Shoestring: Shape Representation, Meshing and Shading(低成本的海洋：形状表示、网格划分和着色)-2013年

作者：Huw Bowles 单位：Studio Gobo Introduction(简介)：Studio Gobo is a small team of talented developers based in Brighton / UK The Crew(成员)：Ben Andrews, Paul ......
网格 Representation Shoestring 形状 海洋

生成中间代码IR（intermediate representation）

完成以上步骤后就开始生成中间代码IR了，代码生成器（Code Generation）会将语法树自顶向下遍历逐步翻译成LLVM IR。OC代码在这一步会进行runtime的桥接，比如property合成、ARC处理等。 IR的基本语法 @ 全局标识 % 局部标识 alloca 开辟空间 align 内 ......
representation intermediate 代码

Do Transformers Really Perform Badly for Graph Representation

Ying C., Cai T., Luo S., Zheng S., Ke D., Shen Y. and Liu T. Do transformers really perform badly for graph representation? NIPS, 2021. 概 本文提出了一种基于图的 ......
Representation Transformers Perform Really Badly

Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding

Tang J. and Wang K. Personalized top-n sequential recommendation via convolutional sequence embedding. WSDM, 2018. 概 序列推荐的经典之作, 将卷积用在序列推荐之上. 符号说明 $\ma ......
Recommendation Convolutional Personalized Sequential Embedding

论文解读（ID-MixGCL）《ID-MixGCL: Identity Mixup for Graph Contrastive Learning》

论文信息 论文标题：ID-MixGCL: Identity Mixup for Graph Contrastive Learning论文作者：Gehang Zhang.....论文来源：2023 aRxiv论文地址：download 论文代码：download视屏讲解：click 介绍 ......
ID-MixGCL MixGCL Contrastive Identity Learning

Controllable Guarantees for Fair Outcomes via Contrastive Information Estimation

Gupta U., Ferber A. M., Dilkina B. and Steeg G. V. Controllable guarantees for fair outcomes via contrastive information estimation. AAAI, 2021. 概 本文提 ......
Controllable Contrastive Information Guarantees Estimation

Handling Information Loss of Graph Neural Networks for Session-based Recommendation

Chen T. and Wong R. C. Handling information loss of graph neural networks for session-based recommendation. KDD, 2020. 概 作者发现图用在 Session 推荐中存在: lossy ......
Recommendation Session-based Information Handling Networks

Representation Learning for Attributed Multiplex Heterogeneous Network

Cen Y., Zou X., Zhang J., Yang H., Zhou J. and Tang J. Representation learning for attributed multiplex heterogeneous network. KDD, 2019. 概 本文在 Attrib ......
Representation Heterogeneous Attributed Multiplex Learning

NEQR: novel enhanced quantum representation

Reference： Zhang, Y., Lu, K., Gao, Y. et al. NEQR: a novel enhanced quantum representation of digital images. Quantum Inf Process 12, 2833–2860 (2013)... ......
representation enhanced quantum novel NEQR

解决 c3p0报错 Establishing SSL connection without server's identity verification is not recommended

解决 c3p0报错 Establishing SSL connection without server's identity verification is not recommended ?useSSL=false <c3p0-config> <default-config> <property ......
Establishing verification recommended connection identity

2022AAAI_Semantically Contrastive Learning for Low-light Image Enhancement(SCL_LLE)

1. motivation 利用语义对比学习 2. network (1) 输入的是低光图像首先经过图像增强的网络（Zero-DCE), 再将它传入语义分割网络中 （2）语义分割网络用的是DeepLabv3+ ......
AAAI_Semantically Semantically Contrastive Enhancement Low-light

M3AE: Multimodal Representation Learning for Brain Tumor Segmentation with Missing Modalities

摘要 提出SimCLR，用于视觉表征的对比学习，简化了最近提出的对比自监督学习算法，为了理解是什么使对比预测任务能够学习有用的表示，系统研究了提出框架的主要组成部分，发现： （1）数据增强的组成在定义有效的预测任务中起着关键的作用 （2）在表示和对比损失之间引入一个可学习的非线性变换，大大提高了已学 ......
Representation Segmentation Multimodal Modalities Learning

迁移学习（CLDA）《CLDA: Contrastive Learning for Semi-Supervised Domain Adaptation》

论文信息 论文标题：CLDA: Contrastive Learning for Semi-Supervised Domain Adaptation论文作者：Ankit Singh论文来源：NeurIPS 2021论文地址：download 论文代码：download视屏讲解：click 1 简介 ......
CLDA Semi-Supervised Contrastive Adaptation Supervised

Deep graph clustering with enhanced feature representations for community detection

论文阅读03-EFR-DGC:Enhanced Feature Representations for Deep Graph Clustering 论文信息 论文地址：Deep graph clustering with enhanced feature representations for co ......
representations clustering community detection enhanced

DiffuRec: A Diffusion Model for Sequential Recommendation

Li Z., Sun A. and Li C. DiffuRec: A diffusion model for sequential recommendation. arXiv preprint arXiv:2304.00686, 2023. 概 扩散模型用于序列推荐, 性能提升很大. DiffuR ......
Recommendation Sequential Diffusion DiffuRec Model

【论文阅读笔记】iCaRL: Incremental Classifier and Representation Learning

Author: Alexander Kolesnikov Key_words: nearest-mean-of-exemplar rule, prioritized exampler selection,representation learning Create_time: September 1 ......
Representation Incremental Classifier Learning 笔记

异常检测 | 迁移学习《Anomaly Detection in IR Images of PV Modules using Supervised Contrastive Learning》

论文信息 论文标题：Anomaly Detection in IR Images of PV Modules using Supervised Contrastive Learning论文作者：Abhay Rawat, Isha Dua, Saurav Gupta, Rahul Tallamraju ......
Contrastive Supervised Detection Learning Anomaly

Sequential Recommendation via Stochastic Self-Attention

Fan Z., Liu Z., Wang A., Nazari Z., Zheng L., Peng H. and Yu P. S. Sequential recommendation via stochastic self-attention. International World Wide W ......
Recommendation Self-Attention Sequential Stochastic Attention

迁移学习（DCCL）《Domain Confused Contrastive Learning for Unsupervised Domain Adaptation》

论文信息 论文标题：Domain Confused Contrastive Learning for Unsupervised Domain Adaptation论文作者：Quanyu Long, Tianze Luo, Wenya Wang and Sinno Jialin Pan论文来源：NAA ......
Domain Unsupervised Contrastive Adaptation Confused

迁移学习（CDA）《CDA:Contrastive-adversarial Domain Adaptation 》

论文信息 论文标题：CDA:Contrastive-adversarial Domain Adaptation论文作者：Nishant Yadav, M. Alam, Ahmed K. Farahat, Dipanjan Ghosh, Chetan Gupta, A. Ganguly论文来源：202 ......
Contrastive-adversarial Contrastive adversarial Adaptation CDA

youtube点击位置纠偏论文：《Recommending What Video to Watch Next: A Multitask Ranking System》

背景 在推荐系统存在两个难题： 1. 需要同时优化点击、观看时长、点赞、打分、评论等多个目标，如何同时建模多个目标 2. 存在position bias，即同个视频放在不通位置上点击率等会不同，如何建模position bias youtube这篇论文采用了MMOE来建模多目标，并用一个shallo ......
Recommending Multitask 位置 youtube Ranking
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