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Found 7 papers, 1 papers with code
A Pipeline For Discourse Circuits From CCG
DisCoCirc represents natural language text as a `circuit' that captures the core semantic information of the text.
From Conceptual Spaces to Quantum Concepts: Formalising and Learning Structured Conceptual Models
We show how concepts from the domains of shape, colour, size and position can be learned from images of simple shapes, where concepts are represented as Gaussians in the classical implementation, and quantum effects in the quantum one.
Formalising and Learning a Quantum Model of Concepts
Our approach builds upon Gardenfors' classical framework of conceptual spaces, in which cognition is modelled geometrically through the use of convex spaces, which in turn factorise in terms of simpler spaces called domains.
The Conceptual VAE
In this report we present a new model of concepts, based on the framework of variational autoencoders, which is designed to have attractive properties such as factored conceptual domains, and at the same time be learnable from data.
A Domain-Theoretic Framework for Robustness Analysis of Neural Networks
A domain-theoretic framework is presented for validated robustness analysis of neural networks.
Composing Conversational Negation
Negation in natural language does not follow Boolean logic and is therefore inherently difficult to model.
Conversational Negation using Worldly Context in Compositional Distributional Semantics
We propose a framework to model an operational conversational negation by applying worldly context (prior knowledge) to logical negation in compositional distributional semantics.
