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Attention-based Interpretability with Concept Transformers
In particular, we design the Concept Transformer, a deep learning module that exposes explanations of the output of a model in which it is embedded in terms of attention over user-defined high-level concepts.
Knowledge Graph Embedding using Graph Convolutional Networks with Relation-Aware Attention
Knowledge graph embedding methods learn embeddings of entities and relations in a low dimensional space which can be used for various downstream machine learning tasks such as link prediction and entity matching.
Fast Record Linkage for Company Entities
These insights guide further analytic approaches to the data and support data visualization.
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