几篇论文实现代码:《Link Prediction with Non-Contrastive Learning》(ICLR 2023) GitHub: github.com/snap-research/non-contrastive-link-prediction [fig5] 《Seeing a Rose in Five Thousand Ways》(CVPR ...
In this paper, we propose a link prediction based on Contrastive Multiple Heterogeneous Graph Convolutional Networks (PL-MHGCN) for the problem of link prediction in heterogeneous networks based on graph neural networks. The PL-MHGCN algorithm decouples the multi-network into multiple sub-networks, ...
🚀 The feature, motivation and pitch I am working with heterogeneous knowledge graphs and am trying to do link prediction on them. The specific issue I am facing is that I cannot find any working implementation that would allow me to do l...
Knowledge graph embeddings models are widely used to provide scalable and efficient link prediction for knowledge graphs. They use different techniques to model embeddings interactions, where their tensor factorisation based versions are known to provide
the diversity of entity representations in different contexts. We consider that the schema of KG is beneficial for preserving the consistency of entities across contexts, and we propose a novelschema-augmentedmulti-level contrastivelearning framework (SMiLE😊) to conduct knowledge graph link prediction...
Efficient protocols for heavy hitter identification with local differential privacy 本地化差分隐私技术下主要项值识别的有效方法 Dan ZHAO, Suyun ZHAO, Hong CHEN, Ruixuan LIU, Cuiping LI, Wenjuan LIANG Interdisciplinary Towards a better prediction of subcellular location of long non-coding RNA ...
learning-based patient-specific CT organ dose estimation method namely, multimodal contrastive learning with Scout images (Scout-MCL). Our proposed Scout-MCL gives accurate and realistic dose estimates in real-time and prospectively, by learning from multi-modal information leveraging image (lateral and...
(2024). Cross-Modality Cardiac Insight Transfer: A Contrastive Learning Approach to Enrich ECG with CMR Features. In: Linguraru, M.G., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. MICCAI 2024. Lecture Notes in Computer Science, vol 15003. Springer, Cham....
Can We Predict New Facts with Open Knowledge Graph Embeddings? A Benchmark for Open Link Prediction Can You Put it All Together: Evaluating Conversational Agents’ Ability to Blend Skills [arXiv] CDL: Curriculum Dual Learning for Emotion-Controllable Response Generation [arXiv] ChartDialogs: Plottin...
Unsupervised Learning for Physical Interaction through Video Prediction Chelsea Finn*, Google, Inc.; Ian Goodfellow, ; Sergey Levine, University of Washington http://arxiv.org/abs/1605.07157 Abstract: A core challenge for an agent learning to interact with the world is to predict how its actions...