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Found 6 papers, 2 papers with code
Neurosymbolic artificial intelligence via large language models and coherence-driven inference
We devise an algorithm to generate sets of propositions that objectively instantiate graphs that support coherence-driven inference.
Prospects for inconsistency detection using large language models and sheaves
We demonstrate that large language models can produce reasonable numerical ratings of the logical consistency of claims.
Quality-diversity in dissimilarity spaces
The theory of magnitude provides a mathematical framework for quantifying and maximizing diversity.
Diversity Enhancement via Magnitude
Promoting and maintaining diversity of candidate solutions is a key requirement of evolutionary algorithms in general and multi-objective evolutionary algorithms in particular.
Path homologies of deep feedforward networks
These results provide a foundation for investigating homological differences between neural network architectures and their realized structure as implied by their parameters.
Topological mixture estimation
Density functions that represent sample data are often multimodal, i. e. they exhibit more than one maximum.
Methodology Information Theory Algebraic Topology Information Theory Statistics Theory Statistics Theory
