Logical Neural Networks
Schedule
Wednesday, October 21, 2020
17:30 UTC
Ndivhuwo Makondo
Dr
IBM Research-Africa & the University of Witwatersrand
Host: NERV
Recording
Open recording
Event Information
Recording
Available
Host
NERV
Duration
70 minutes
Abstract
The work to be presented in this talk proposes a novel framework seamlessly providing key properties of both neural nets (learning) and symbolic logic (knowledge and reasoning). Every neuron has a meaning as a component of a formula in a weighted real-valued logic, yielding a highly interpretable disentangled representation. Inference is omnidirectional rather than focused on predefined target variables, and corresponds to logical reasoning, including classical first-order logic theorem proving as a special case. The model is end-to-end differentiable, and learning minimizes a novel loss function capturing logical contradiction, yielding resilience to inconsistent knowledge. It also enables the open-world assumption by maintaining bounds on truth values which can have probabilistic semantics, yielding resilience to incomplete knowledge.
Topics
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