May 14, 2024, 4:42 a.m. | Kareem Ahmed, Stefano Teso, Paolo Morettin, Luca Di Liello, Pierfrancesco Ardino, Jacopo Gobbi, Yitao Liang, Eric Wang, Kai-Wei Chang, Andrea Passerin

cs.LG updates on

arXiv:2405.07387v1 Announce Type: new
Abstract: Structured output prediction problems are ubiquitous in machine learning. The prominent approach leverages neural networks as powerful feature extractors, otherwise assuming the independence of the outputs. These outputs, however, jointly encode an object, e.g. a path in a graph, and are therefore related through the structure underlying the output space. We discuss the semantic loss, which injects knowledge about such structure, defined symbolically, into training by minimizing the network's violation of such dependencies, steering the …

abstract arxiv cs.lg encode feature functions graph however loss machine machine learning networks neural networks neuro object path prediction semantic through type

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