Neurosymbolic Learning for Robust and Reliable Intelligent Systems
This thesis shows that looking at intelligent systems through the lens of neurosymbolic models has several benefits over traditional deep learning approaches. Neurosymbolic models contain symbolic programmatic constructs such as loops and conditionals and continuous neural components. The symbolic p...
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Massachusetts Institute of Technology
2022
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| Online Access: | https://hdl.handle.net/1721.1/143249 https://orcid.org/0000-0003-1843-589X |