Deep Neural Network for Constraint Acquisition through Tailored Loss Function
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866914701181452288 |
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| author | Vyhmeister, Eduardo Paez, Rocio Gonzalez, Gabriel |
| author_facet | Vyhmeister, Eduardo Paez, Rocio Gonzalez, Gabriel |
| contents | The significance of learning constraints from data is underscored by its potential applications in real-world problem-solving. While constraints are popular for modeling and solving, the approaches to learning constraints from data remain relatively scarce. Furthermore, the intricate task of modeling demands expertise and is prone to errors, thus constraint acquisition methods offer a solution by automating this process through learnt constraints from examples or behaviours of solutions and non-solutions. This work introduces a novel approach grounded in Deep Neural Network (DNN) based on Symbolic Regression that, by setting suitable loss functions, constraints can be extracted directly from datasets. Using the present approach, direct formulation of constraints was achieved. Furthermore, given the broad pre-developed architectures and functionalities of DNN, connections and extensions with other frameworks could be foreseen. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_02042 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Neural Network for Constraint Acquisition through Tailored Loss Function Vyhmeister, Eduardo Paez, Rocio Gonzalez, Gabriel Machine Learning Symbolic Computation The significance of learning constraints from data is underscored by its potential applications in real-world problem-solving. While constraints are popular for modeling and solving, the approaches to learning constraints from data remain relatively scarce. Furthermore, the intricate task of modeling demands expertise and is prone to errors, thus constraint acquisition methods offer a solution by automating this process through learnt constraints from examples or behaviours of solutions and non-solutions. This work introduces a novel approach grounded in Deep Neural Network (DNN) based on Symbolic Regression that, by setting suitable loss functions, constraints can be extracted directly from datasets. Using the present approach, direct formulation of constraints was achieved. Furthermore, given the broad pre-developed architectures and functionalities of DNN, connections and extensions with other frameworks could be foreseen. |
| title | Deep Neural Network for Constraint Acquisition through Tailored Loss Function |
| topic | Machine Learning Symbolic Computation |
| url | https://arxiv.org/abs/2403.02042 |