On the Two-fold Role of Logic Constraints in Deep Learning
Deep Learning (DL) is a branch of Artificial Intelligence (AI) that focuses on training deep neural networks. Thanks to their ability to process large amounts of data, these networks have achieved remarkable results across a variety of fields. Despite these successes, DL still faces several limitati...
I tiakina i:
| Kaituhi matua: | |
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| Hōputu: | Online |
| Reo: | Ingarihi |
| I whakaputaina: |
Firenze University Press
2025
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| Ngā marau: | |
| Urunga tuihono: | ONIX_20250801T172941_9791221506808_44 |
| Ngā Tūtohu: |
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
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| _version_ | 1869517659599536128 |
|---|---|
| author | Ciravegna, Gabriele |
| author_browse | Ciravegna, Gabriele |
| author_facet | Ciravegna, Gabriele |
| author_sort | Ciravegna, Gabriele |
| collection | Directory of Open Access Books |
| description | Deep Learning (DL) is a branch of Artificial Intelligence (AI) that focuses on training deep neural networks. Thanks to their ability to process large amounts of data, these networks have achieved remarkable results across a variety of fields. Despite these successes, DL still faces several limitations that hinder its adoption in real-world scenarios. This thesis addresses three key challenges: reducing the need for supervision, defending against adversarial attacks, and explaining neural network behavior. The first two challenges are tackled through learning from constraints, which incorporates domain knowledge to guide the learning process and enhance model robustness. The third challenge, on the other hand, is addressed using learning of constraints, which helps identify and formalize logical relationships among learned tasks, thereby providing interpretable explanations of the networks’ behavior. |
| format | Online |
| id | doab-20.500.12854ir-163658 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Firenze University Press |
| publisherStr | Firenze University Press |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1636582025-08-02T05:21:00Z On the Two-fold Role of Logic Constraints in Deep Learning Ciravegna, Gabriele Deep Learning (DL) Logic Constraints Active Learning Adversarial Defense Logic Explanations thema EDItEUR::U Computing and Information Technology Deep Learning (DL) is a branch of Artificial Intelligence (AI) that focuses on training deep neural networks. Thanks to their ability to process large amounts of data, these networks have achieved remarkable results across a variety of fields. Despite these successes, DL still faces several limitations that hinder its adoption in real-world scenarios. This thesis addresses three key challenges: reducing the need for supervision, defending against adversarial attacks, and explaining neural network behavior. The first two challenges are tackled through learning from constraints, which incorporates domain knowledge to guide the learning process and enhance model robustness. The third challenge, on the other hand, is addressed using learning of constraints, which helps identify and formalize logical relationships among learned tasks, thereby providing interpretable explanations of the networks’ behavior. 2025-08-02T05:20:59Z 2025-08-02T05:20:59Z 2025-08-01T15:36:14Z 2025 book ONIX_20250801T172941_9791221506808_44 https://library.oapen.org/handle/20.500.12657/104535 9791221506808 9791221506792 9791221506815 https://directory.doabooks.org/handle/20.500.12854/163658 eng Premio Tesi di Dottorato Città di Firenze open access image/jpeg Attribution 4.0 International https://library.oapen.org/bitstream/20.500.12657/104535/1/44798.pdf Firenze University Press 10.36253/979-12-215-0680-8 10.36253/979-12-215-0680-8 2ec4474d-93b1-4cfa-b313-9c6019b51b1a 9791221506808 9791221506792 9791221506815 126 Florence open access |
| spellingShingle | Deep Learning (DL) Logic Constraints Active Learning Adversarial Defense Logic Explanations thema EDItEUR::U Computing and Information Technology Ciravegna, Gabriele On the Two-fold Role of Logic Constraints in Deep Learning |
| title | On the Two-fold Role of Logic Constraints in Deep Learning |
| title_full | On the Two-fold Role of Logic Constraints in Deep Learning |
| title_fullStr | On the Two-fold Role of Logic Constraints in Deep Learning |
| title_full_unstemmed | On the Two-fold Role of Logic Constraints in Deep Learning |
| title_short | On the Two-fold Role of Logic Constraints in Deep Learning |
| title_sort | on the two fold role of logic constraints in deep learning |
| topic | Deep Learning (DL) Logic Constraints Active Learning Adversarial Defense Logic Explanations thema EDItEUR::U Computing and Information Technology |
| topic_facet | Deep Learning (DL) Logic Constraints Active Learning Adversarial Defense Logic Explanations thema EDItEUR::U Computing and Information Technology |
| url | ONIX_20250801T172941_9791221506808_44 |
| work_keys_str_mv | AT ciravegnagabriele onthetwofoldroleoflogicconstraintsindeeplearning |