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Logic tensor networks
Attempts at combining logic and neural networks into neurosymbolic approaches have been
on the increase in recent years. In a neurosymbolic system, symbolic knowledge assists …
on the increase in recent years. In a neurosymbolic system, symbolic knowledge assists …
Logic-induced diagnostic reasoning for semi-supervised semantic segmentation
Recent advances in semi-supervised semantic segmentation have been heavily reliant on
pseudo labeling to compensate for limited labeled data, disregarding the valuable relational …
pseudo labeling to compensate for limited labeled data, disregarding the valuable relational …
[HTML][HTML] Analyzing differentiable fuzzy logic operators
The AI community is increasingly putting its attention towards combining symbolic and
neural approaches, as it is often argued that the strengths and weaknesses of these …
neural approaches, as it is often argued that the strengths and weaknesses of these …
EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: the MonuMAI cultural heritage …
Abstract The latest Deep Learning (DL) models for detection and classification have
achieved an unprecedented performance over classical machine learning algorithms …
achieved an unprecedented performance over classical machine learning algorithms …
Neural-symbolic integration: A compositional perspective
Despite significant progress in the development of neural-symbolic frameworks, the question
of how to integrate a neural and a symbolic system in a compositional manner remains …
of how to integrate a neural and a symbolic system in a compositional manner remains …
Transfer learning with synthetic corpora for spatial role labeling and reasoning
Recent research shows synthetic data as a source of supervision helps pretrained language
models (PLM) transfer learning to new target tasks/domains. However, this idea is less …
models (PLM) transfer learning to new target tasks/domains. However, this idea is less …
Analyzing differentiable fuzzy implications
Combining symbolic and neural approaches has gained considerable attention in the AI
community, as it is often argued that the strengths and weaknesses of these approaches are …
community, as it is often argued that the strengths and weaknesses of these approaches are …
Injecting domain knowledge in neural networks: a controlled experiment on a constrained problem
Recent research has shown how Deep Neural Networks trained on historical solution pools
can tackle CSPs to some degree, with potential applications in problems with implicit soft …
can tackle CSPs to some degree, with potential applications in problems with implicit soft …
Teaching the old dog new tricks: Supervised learning with constraints
Adding constraint support in Machine Learning has the potential to address outstanding
issues in data-driven AI systems, such as safety and fairness. Existing approaches typically …
issues in data-driven AI systems, such as safety and fairness. Existing approaches typically …
Knowledge enhanced neural networks for relational domains
In the recent past, there has been a growing interest in Neural-Symbolic Integration
frameworks, ie, hybrid systems that integrate connectionist and symbolic approaches to …
frameworks, ie, hybrid systems that integrate connectionist and symbolic approaches to …