Combining data and theory for derivable scientific discovery with AI-Descartes
Scientists aim to discover meaningful formulae that accurately describe experimental data.
Mathematical models of natural phenomena can be manually created from domain …
Mathematical models of natural phenomena can be manually created from domain …
Neural-logic human-object interaction detection
The interaction decoder utilized in prevalent Transformer-based HOI detectors typically
accepts pre-composed human-object pairs as inputs. Though achieving remarkable …
accepts pre-composed human-object pairs as inputs. Though achieving remarkable …
Image translation as diffusion visual programmers
We introduce the novel Diffusion Visual Programmer (DVP), a neuro-symbolic image
translation framework. Our proposed DVP seamlessly embeds a condition-flexible diffusion …
translation framework. Our proposed DVP seamlessly embeds a condition-flexible diffusion …
On the benefits of OWL-based knowledge graphs for neural-symbolic systems
D Herron, E Jiménez-Ruiz… - Proceedings of the 17th …, 2023 - openaccess.city.ac.uk
Knowledge graphs, as understood within the Semantic Web and Knowledge Representation
communities, are more than just graph data. OWL-based knowledge graphs offer the …
communities, are more than just graph data. OWL-based knowledge graphs offer the …
Convex and Bilevel Optimization for Neuro-Symbolic Inference and Learning
We address a key challenge for neuro-symbolic (NeSy) systems by leveraging convex and
bilevel optimization techniques to develop a general gradient-based framework for end-to …
bilevel optimization techniques to develop a general gradient-based framework for end-to …
Neuro-symbolic learning yielding logical constraints
Neuro-symbolic systems combine the abilities of neural perception and logical reasoning.
However, end-to-end learning of neuro-symbolic systems is still an unsolved challenge. This …
However, end-to-end learning of neuro-symbolic systems is still an unsolved challenge. This …
A mathematical framework, a taxonomy of modeling paradigms, and a suite of learning techniques for neural-symbolic systems
The field of Neural-Symbolic (NeSy) systems is growing rapidly. Proposed approaches show
great promise in achieving symbiotic unions of neural and symbolic methods. However …
great promise in achieving symbiotic unions of neural and symbolic methods. However …
Error Detection and Constraint Recovery in Hierarchical Multi-Label Classification without Prior Knowledge
JS Kricheli, K Vo, A Datta, S Ozgur… - arXiv preprint arXiv …, 2024 - arxiv.org
Recent advances in Hierarchical Multi-label Classification (HMC), particularly
neurosymbolic-based approaches, have demonstrated improved consistency and accuracy …
neurosymbolic-based approaches, have demonstrated improved consistency and accuracy …
Metacognitive AI: Framework and the Case for a Neurosymbolic Approach
Metacognition is the concept of reasoning about an agent's own internal processes and was
originally introduced in the field of developmental psychology. In this position paper, we …
originally introduced in the field of developmental psychology. In this position paper, we …
On the Transition from Neural Representation to Symbolic Knowledge
J Cheng, P Chin - arXiv preprint arXiv:2308.02000, 2023 - arxiv.org
Bridging the huge disparity between neural and symbolic representation can potentially
enable the incorporation of symbolic thinking into neural networks from essence. Motivated …
enable the incorporation of symbolic thinking into neural networks from essence. Motivated …