Self-supervised Graph Learning for Recommendation(SGL) - 代码天地

Self-supervised Graph Learning for Recommendation(SGL)

企业开发 2023-07-29 13:22:07 阅读次数: 0

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转载自blog.csdn.net/qq_42018521/article/details/131615731

Self-supervised Graph Learning for Recommendation(SGL)

Multi-Modal Self-Supervised Learning for Recommendation

self-supervised learning

阅读理解《Better and Faster: Knowledge Transfer from Multiple Self-supervised Learning Tasks via Graph D》

论文阅读 —— Graph Self-Supervised Learning: A Survey （自监督图学习综述）

论文阅读 - Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection

【论文下饭】GATE: Graph CCA for Temporal Self-Supervised Learning for Label-Efficient fMRI Analysis

Contrastive Self-Supervised Learning

Self-supervised learning and computer vision

Self-Supervised Learning for Contextualized Extractive Summarization

Self-Supervised Learning 入门介绍

NTU 课程笔记：self-supervised learning

SelFlow: Self-Supervised Learning of Optical Flow

《graph self- supervised learning：a survey》论文阅读

自监督学习 | (1) Self-supervised Learning入门

自监督学习 | (1) Self-supervised Learning入门

Self-Supervised Learning of Pretext-Invariant Representation

自监督学习（self-supervised learning）（20201124）

Distilling Visual Priors from Self-Supervised Learning

CutPaste:Self-Supervised Learning for Anomaly Detection and Localization

【WACV 2021】Self-Supervised Learning for Domain Adaptation on Point Clouds

自监督学习（SSL）Self-Supervised Learning

机器学习：Self-supervised Learning for Speech and image

Jointly Learning Explainable Rules for Recommendation with Knowledge Graph

图像分割"LIP: Self-supervised Structure-sensitive Learning and A New Benchmark for Human Parsing"

论文阅读--Reinforced Cross-Modal Matching and Self-Supervised Imatation Learning for VLN Navigation

【论文笔记】SelfORE: Self-supervised Relational Feature Learning for Open Relation

人体姿态2020（三）Self-supervised Learning of Interpretable Keypoints from Unlabelled Videos

wav2vec2.0: A Framework for Self-Supervised Learning of Speech Representations

【论文笔记】Steering Self-Supervised Feature Learning Beyond Local Pixel Statistics

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