A set of intervals valued symbolic feature vector is then used to represent the class. Each interval value (symbolic data) is computed using mean and standard deviation of the corresponding feature ...

Each vector is aggregated by the consensus sum operation into a single vector containing all training instances for that class. A symbolic representation of the class, called “Dog” in this example, as ...

This is the main project repository for the paper Symbolically Synthesized Neural Networks:. Abstract: Neural networks adapt very well to distributed and continuous representations, but struggle to ...

This stark contrast between human and LLM performance highlights a significant gap in how machines process and understand symbolic representations of visual information compared to humans. In ...

Neural Symbolic Machines is a framework to integrate neural networks and symbolic representations using reinforcement learning, with applications in program synthesis and semantic parsing. License ...