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Found 5 papers, 3 papers with code
Simple and Effective Transfer Learning for Neuro-Symbolic Integration
Then, a NeSy model is trained on the same task via transfer learning, where the weights of the perceptual part are injected from the pretrained network.
Deep Symbolic Learning: Discovering Symbols and Rules from Perceptions
In this paper, we propose Deep Symbolic Learning (DSL), a NeSy system that learns NeSy-functions, i. e., the composition of a (set of) perception functions which map continuous data to discrete symbols, and a symbolic function over the set of symbols.
Refining neural network predictions using background knowledge
Using a new algorithm called Iterative Local Refinement (ILR), we combine refinement functions to find refined predictions for logical formulas of any complexity.
Knowledge Enhanced Neural Networks for relational domains
In the recent past, there has been a growing interest in Neural-Symbolic Integration frameworks, i. e., hybrid systems that integrate connectionist and symbolic approaches to obtain the best of both worlds.
Neural Networks Enhancement with Logical Knowledge
In the recent past, there has been a growing interest in Neural-Symbolic Integration frameworks, i. e., hybrid systems that integrate connectionist and symbolic approaches to obtain the best of both worlds.
