Using this equivalence as the building block, we extend our analysis to the CLIP model and rigorously characterize how similar multi-modal objects are embedded together. Motivated by our theoretical insights, we introduce the Kernel-InfoNCE loss, incorporating mixture of kernel functions that outperform...
Multi-kernel fusionContrastive learningPredicting microbe鈥揹isease associations (MDA) is crucial for proactively demystifying diseases causes and preventing them. Traditional prediction methods endure labor-intensive, time-consuming, and expensive. Therefore, this paper proposes CasMF-GCL, a novel G raph...
Learning from All Sides: Diversified Positive Augmentation via Self-distillation in Recommendation (DA) arXiv 2023, [PDF] Counterfactual Graph Augmentation for Consumer Unfairness Mitigation in Recommender Systems (Graph + DA) CIKM 2023, [PDF], [Code] Bayes-enhanced Multi-view Attention Networks for...
Briefly, we will show here that learning accuracy is reduced if the kernel has non-zero area \(I=\int_{0}^{\infty }K(t)dt\); we then show, based on prior work31,63, that reducing the area of kernels to zero requires increasingly large amounts of energy dissipation. In other words...
With GPS besides learning effective graph representations derived from GNNs, we also benefit from graph pooling to automatically generate multi-scale view augmentations. Contrastive learning on graphs has become a dominant component in self-supervised learning on graphs. Inspired by previous success in...
In current relation extraction tasks, when the input sentence structure is complex, the performance of in-context learning methods based on large language
Using the label-wise attention mechanism, CAML achieves Micro F1-score (MiF) more than 0.5, producing better performance than normal deep learning models (i.e., CNN, BiGRU, KAICD). Addressing the fixed window size weakness of CAML, MASATT-KG [13], MultiResCNN [12], LAAT [3] are ...
DAPNet is the first to apply multi-view graph contrastive learning to disease progression prediction tasks. Compared with other studies, DAPNet integrates the molecular-level disease association network, the combines disease co-occurrence network and the ICD-10 network, and fully explores the association...
Contrastive multi-view representation learning on graphs International Conference on Machine Learning, PMLR (2020), pp. 4116-4126 Google Scholar [40] Zheng Y., Zheng Y., Zhou X., Gong C., Lee V.C., Pan S. Unifying graph contrastive learning with flexible contextual scopes 2022 IEEE Internat...
InfoGraph(F.-Y. Sun et al., InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization, ICLR, 2020) [Example] MVGRL(K. Hassani et al., Contrastive Multi-View Representation Learning on Graphs, ICML, 2020) [Example1,Example2] ...