Leveraging Representation and Inference through Deep Relational Learning

Maria Leonor Pacheco     Ibrahim Dalal     Dan Goldwasser    
NeurIPS Workshop on Relational Representation Learning, 2018


We present DRAIL, a declarative framework for specifying deep relational models. Our framework provides an easy-to-use interface for defining complex models consisting of many interdependent variables, and experimenting with different design choices, learning algorithms and neural architectures. We demonstrate the importance of correctly modeling the interactions between learning, representation and inference by applying DRAIL to two challenging relational learning problems, combining textual and social information. Then, we introduce a relational zero-shot learning task and explore the deep component of DRAIL to work in this setting.

Bib Entry

    author = "Maria Leonor Pacheco and Ibrahim Dalal and Dan Goldwasser",
    title = "Leveraging Representation and Inference through Deep Relational Learning",
    booktitle = "NeurIPS Workshop on Relational Representation Learning",
    year = "2018"