Self-Supervised Learning in GNNs 具体来说,这些SSL方法构造了一个预定义的代理任务,为未标记的节点/图分配伪标签,然后在设计的代理任务上训练模型来学习表示。以对比学习为例,基于同质性假设,相似性越高认为标签一致(算是伪标签),通过最大化互信息下界的损失函数,来指导模型训练; Multi-Task Self-Supervised ...
[ICLR 2022] A PyTorch implementation of paper "Automated Self-Supervised Learning for Graphs". Abstract We observe that different pretext tasks affect downstream tasks differently cross datasets, which suggests that searching pretext tasks is crucial for graph self-supervised learning. Different from exis...
Relative molecule self-attention transformer Łukasz Maziarka Dawid Majchrowski Stanisław Jastrzębski Journal of Cheminformatics (2024) GraphsformerCPI: Graph Transformer for Compound–Protein Interaction Prediction Jun Ma Zhili Zhao Ruisheng Zhang Interdisciplinary Sciences: Computational Life Scienc...
& Shi, X. Deep semantic role labeling with self-attention. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence 16725 (AAAI, 2018). He, K., Zhang, X., Ren, S. & Sun, J. J. Deep residual learning for image recognition. In Proceedings of the IEEE Conference ...
Function MRI Representation Learning via Self-supervised Transformer for Automated Brain Disorder AnalysisMajor depressive disorder (MDD) is a prevalent mental health disorder whose neuropathophysiology remains unclear. Resting-state functional magnetic resonance imaging (rs-fMRI) has been used to capture ...
Figure below shows a NLP framework for automated fact-checking (AFC) with text consisting of three stages: Claim detection to identify claims that require verification; Evidence retrievalto find sources supporting or refuting the claim; Claim verification to assess the veracity of the claim based on...
performance. Furthermore, the different domains for using self-supervised learning (SSL) as an annotation technique for cyberbullying detection are explored. 1. Introduction Social media has seen exponential changes in how we network and share ideas, feelings, and information. However, ease of ...
Function MRI Representation Learning via Self-supervised Transformer for Automated Brain Disorder Analysis Besides, based on our learned fully connected graphs, we can detect discriminative functional connectivities in MDD vs. HC classification, providing potential fMRI biomarkers for MDD analysis.doi:10.1007...
Fully automated machine learning (AutoML) for predictive modeling is becoming a reality, giving rise to a whole new field. We present the basic ideas and principles of Just Add Data Bio (JADBio), an AutoML platform applicable to the low-sample, high-dime
Veracity Classification: classify the veracity of textual claims given retrieved evidence. Datasets Claim Detection and Extraction Dataset Verdict Prediction Relevant Surveys Automated Fact-Checking Deep learning for misinformation detection on online social networks: a survey and new perspectives (Islam et al...