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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.07387 |
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| _version_ | 1866916243585368064 |
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| author | Ahmed, Kareem Teso, Stefano Morettin, Paolo Di Liello, Luca Ardino, Pierfrancesco Gobbi, Jacopo Liang, Yitao Wang, Eric Chang, Kai-Wei Passerini, Andrea Broeck, Guy Van den |
| author_facet | Ahmed, Kareem Teso, Stefano Morettin, Paolo Di Liello, Luca Ardino, Pierfrancesco Gobbi, Jacopo Liang, Yitao Wang, Eric Chang, Kai-Wei Passerini, Andrea Broeck, Guy Van den |
| contents | 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 network towards predicting distributions satisfying the underlying structure. At the same time, it is agnostic to the arrangement of the symbols, and depends only on the semantics expressed thereby, while also enabling efficient end-to-end training and inference. We also discuss key improvements and applications of the semantic loss. One limitations of the semantic loss is that it does not exploit the association of every data point with certain features certifying its membership in a target class. We should therefore prefer minimum-entropy distributions over valid structures, which we obtain by additionally minimizing the neuro-symbolic entropy. We empirically demonstrate the benefits of this more refined formulation. Moreover, the semantic loss is designed to be modular and can be combined with both discriminative and generative neural models. This is illustrated by integrating it into generative adversarial networks, yielding constrained adversarial networks, a novel class of deep generative models able to efficiently synthesize complex objects obeying the structure of the underlying domain. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_07387 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Semantic Loss Functions for Neuro-Symbolic Structured Prediction Ahmed, Kareem Teso, Stefano Morettin, Paolo Di Liello, Luca Ardino, Pierfrancesco Gobbi, Jacopo Liang, Yitao Wang, Eric Chang, Kai-Wei Passerini, Andrea Broeck, Guy Van den Machine Learning 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 network towards predicting distributions satisfying the underlying structure. At the same time, it is agnostic to the arrangement of the symbols, and depends only on the semantics expressed thereby, while also enabling efficient end-to-end training and inference. We also discuss key improvements and applications of the semantic loss. One limitations of the semantic loss is that it does not exploit the association of every data point with certain features certifying its membership in a target class. We should therefore prefer minimum-entropy distributions over valid structures, which we obtain by additionally minimizing the neuro-symbolic entropy. We empirically demonstrate the benefits of this more refined formulation. Moreover, the semantic loss is designed to be modular and can be combined with both discriminative and generative neural models. This is illustrated by integrating it into generative adversarial networks, yielding constrained adversarial networks, a novel class of deep generative models able to efficiently synthesize complex objects obeying the structure of the underlying domain. |
| title | Semantic Loss Functions for Neuro-Symbolic Structured Prediction |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2405.07387 |