
SLAPS: SelfSupervision Improves Structure Learning for Graph Neural Networks
Graph neural networks (GNNs) work well when the graph structure is provi...
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OutofSample Representation Learning for MultiRelational Graphs
Many important problems can be formulated as reasoning in multirelation...
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Time2Vec: Learning a Vector Representation of Time
Time is an important feature in many applications involving events that ...
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Diachronic Embedding for Temporal Knowledge Graph Completion
Knowledge graphs (KGs) typically contain temporal facts indicating relat...
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Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey
Graphs arise naturally in many realworld applications including social ...
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Structure Learning for Relational Logistic Regression: An Ensemble Approach
We consider the problem of learning Relational Logistic Regression (RLR)...
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Record Linkage to Match Customer Names: A Probabilistic Approach
Consider the following problem: given a database of records indexed by n...
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SimplE Embedding for Link Prediction in Knowledge Graphs
The aim of knowledge graphs is to gather knowledge about the world and p...
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RelNN: A Deep Neural Model for Relational Learning
Statistical relational AI (StarAI) aims at reasoning and learning in noi...
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Comparing Aggregators for Relational Probabilistic Models
Relational probabilistic models have the challenge of aggregation, where...
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Domain Recursion for Lifted Inference with Existential Quantifiers
In recent work, we proved that the domain recursion inference rule makes...
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New Liftable Classes for FirstOrder Probabilistic Inference
Statistical relational models provide compact encodings of probabilistic...
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A Learning Algorithm for Relational Logistic Regression: Preliminary Results
Relational logistic regression (RLR) is a representation of conditional ...
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Why is Compiling Lifted Inference into a LowLevel Language so Effective?
Firstorder knowledge compilation techniques have proven efficient for l...
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Seyed Mehran Kazemi
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