101 research outputs found
MR-GNN: Multi-Resolution and Dual Graph Neural Network for Predicting Structured Entity Interactions
Predicting interactions between structured entities lies at the core of
numerous tasks such as drug regimen and new material design. In recent years,
graph neural networks have become attractive. They represent structured
entities as graphs and then extract features from each individual graph using
graph convolution operations. However, these methods have some limitations: i)
their networks only extract features from a fix-sized subgraph structure (i.e.,
a fix-sized receptive field) of each node, and ignore features in substructures
of different sizes, and ii) features are extracted by considering each entity
independently, which may not effectively reflect the interaction between two
entities. To resolve these problems, we present MR-GNN, an end-to-end graph
neural network with the following features: i) it uses a multi-resolution based
architecture to extract node features from different neighborhoods of each
node, and, ii) it uses dual graph-state long short-term memory networks
(L-STMs) to summarize local features of each graph and extracts the interaction
features between pairwise graphs. Experiments conducted on real-world datasets
show that MR-GNN improves the prediction of state-of-the-art methods.Comment: Accepted by IJCAI 201
Hyperparameter Learning via Distributional Transfer
Bayesian optimisation is a popular technique for hyperparameter learning but
typically requires initial exploration even in cases where similar prior tasks
have been solved. We propose to transfer information across tasks using learnt
representations of training datasets used in those tasks. This results in a
joint Gaussian process model on hyperparameters and data representations.
Representations make use of the framework of distribution embeddings into
reproducing kernel Hilbert spaces. The developed method has a faster
convergence compared to existing baselines, in some cases requiring only a few
evaluations of the target objective
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