Relational Deep Learning
PulseAugur coverage of Relational Deep Learning — every cluster mentioning Relational Deep Learning across labs, papers, and developer communities, ranked by signal.
1 day(s) with sentiment data
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New paradigm improves Relational Deep Learning models with incremental training
Researchers have introduced a new incremental evaluation and training paradigm for Relational Deep Learning (RDL) models. This approach addresses the limitation of current RDL practices that rely on static datasets, whi…
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New attacks probe adversarial robustness of relational deep learning pipelines
Researchers have developed new methods to test the adversarial robustness of Relational Deep Learning (RDL) pipelines, which are commonly used for machine learning on relational databases. These pipelines encode databas…
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New adversarial attacks probe Relational Deep Learning robustness
Researchers have developed new methods to test the adversarial robustness of Relational Deep Learning (RDL) models. These attacks focus on manipulating foreign-key references within a database while adhering to schema i…
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New research details optimal graph structures for relational deep learning
Researchers have identified key characteristics that make graphs suitable for relational deep learning. They found that directly converting database schemas into graphs often leads to information overload and semantic f…
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New Rel-MOSS method tackles imbalanced data in relational deep learning
Researchers have introduced Rel-MOSS, a novel approach to address class imbalance in relational deep learning on relational databases. This method aims to prevent minority entities from being overshadowed by majority on…
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FROG framework learns relational database graph structures for deep learning
Researchers have developed FROG, a novel framework for Relational Deep Learning (RDL) that addresses the limitations of fixed graph structures in modeling relational databases. FROG introduces a learnable approach to gr…