To generate negative training examples for the query to include in the training data, the online system determines measures of similarity between items with which the specific interaction was not performed and the query. The online system may weight a loss function for the embedding-based model by...
generative, contrastive, 和 generative-contrastive (adversarial)三个部分各自的发展,以及最近生成模型向contrastive的转变过程。 提供了自监督学习方法的理论可靠性,并展示了它如何有益于下游的监督学习任务。理论可靠性还是很make sense的 提出了目前自监督学习还存在的一些问题以及未来可能的发展方向 文章的组织大概是这...
The easiest way to understand generative learning is to treat it as a destruction/reconstruction effort, with the most common form being generative adversarial networks (GAN). The model crops or distorts an intact image\(X\)to\(Z\)and restores it to its original state (\(G(Z)=X\)). A...
the corresponding false-negative rates, ultimately establishing the final threshold values based on the cost function. Building on the aforementioned online monitoring system, we utilized Chinese-specific highway and intersection datasets to verify the feasibility of the system. The results indicated that ...
Second, we introduce a parameter-free negative sampling technique -- adaptive self-adversarial (ASA) negative sampling. ASA reduces the false-negative rate by leveraging positive relationships to effectively guide the identification of true negative samples. Our experimental evaluation demonstrates that Rel...
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[98] 提出了一种通用但有效的硬负采样策略,以使潜在的借口任务更难以解决。InfoGraph [97] 与 DGI [13] 类似,直接将图embedding与节点embedding进行对比,以区分节点是否属于给定图。基于InfoGraph,[98] 提出了hard negative sampling策略,以增强预训练任务的难度,提升学习性能。
The easiest way to understand generative learning is to treat it as a destruction/reconstruction effort, with the most common form being generative adversarial networks (GAN). The model crops or distorts an intact imageXtoZand restores it to its original state (G(Z)=X). As the model is tr...
Graph Contrastive Learning With Negative Propagation for Recommendation (Graph + CL) TCSS 2024, [PDF] General Debiasing for Graph-based Collaborative Filtering via Adversarial Graph Dropout (Graph + CL) WWW 2024, [PDF], [Code] Breaking the Barrier: Utilizing Large Language Models for Industrial Rec...
[arXiv 2023] Adversarial Hard Negative Generation for Complementary Graph Contrastive Learning [paper] [INS 2023] INS-GNN: Improving Graph Imbalance Learning with Self-Supervision [paper] [TNNLS 2023] Dual Contrastive Learning Network for Graph Clustering [paper] [arXiv 2023] RARE: Robust Masked Gr...