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03/14/2023
AutoTransfer: AutoML with Knowledge Transfer – An Application to Graph Neural Networks
AutoML has demonstrated remarkable success in finding an effective neura...
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03/14/2023
Relational Multi-Task Learning: Modeling Relations between Data and Tasks
A key assumption in multi-task learning is that at the inference time th...
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10/21/2022
Efficient Automatic Machine Learning via Design Graphs
Despite the success of automated machine learning (AutoML), which aims t...
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08/15/2022
ROLAND: Graph Learning Framework for Dynamic Graphs
Graph Neural Networks (GNNs) have been successfully applied to many real...
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03/30/2022
AdaGrid: Adaptive Grid Search for Link Prediction Training Objective
One of the most important factors that contribute to the success of a ma...
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01/25/2021
Identity-aware Graph Neural Networks
Message passing Graph Neural Networks (GNNs) provide a powerful modeling...
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11/17/2020
Design Space for Graph Neural Networks
The rapid evolution of Graph Neural Networks (GNNs) has led to a growing...
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10/30/2020
Handling Missing Data with Graph Representation Learning
Machine learning with missing data has been approached in two different ...
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09/19/2020
Inductive Learning on Commonsense Knowledge Graph Completion
Commonsense knowledge graph (CKG) is a special type of knowledge graph (...
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07/13/2020
Graph Structure of Neural Networks
Neural networks are often represented as graphs of connections between n...
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07/06/2020
Neural Subgraph Matching
Subgraph matching is the problem of determining the presence and locatio...
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10/29/2019
G2SAT: Learning to Generate SAT Formulas
The Boolean Satisfiability (SAT) problem is the canonical NP-complete pr...
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06/11/2019
Position-aware Graph Neural Networks
Learning node embeddings that capture a node's position within the broad...
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06/09/2019
Redundancy-Free Computation Graphs for Graph Neural Networks
Graph Neural Networks (GNNs) are based on repeated aggregations of infor...
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04/08/2019
Hierarchical Temporal Convolutional Networks for Dynamic Recommender Systems
Recommender systems that can learn from cross-session data to dynamicall...
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03/10/2019
GNN Explainer: A Tool for Post-hoc Explanation of Graph Neural Networks
Graph Neural Networks (GNNs) are a powerful tool for machine learning on...
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06/22/2018
Hierarchical Graph Representation Learning with Differentiable Pooling
Recently, graph neural networks (GNNs) have revolutionized the field of ...
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06/22/2018
Hierarchical Graph Representation Learning withDifferentiable Pooling
Recently, graph neural networks (GNNs) have revolutionized the field of ...
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06/07/2018
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
Generating novel graph structures that optimize given objectives while o...
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02/24/2018
