19 citations · 61 across the 15 of their papers we have counts for
8 papers · 1 filter
The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning
Agnese Chiatti, Michael Cochez, Cristina Cornelio +14
Neurosymbolic AI systems that integrate machine learning and symbolic reasoning are rapidly gaining attention. They complement the data-intensive statistical approaches of neural n…
ULLER: A Unified Language for Learning and Reasoning
Emile van Krieken, Samy Badreddine, Robin Manhaeve +1
The field of neuro-symbolic artificial intelligence (NeSy), which combines learning and reasoning, has recently experienced significant growth. There now are a wide variety of NeSy…
Optimisation in Neurosymbolic Learning Systems
Emile van Krieken
Neurosymbolic AI aims to integrate deep learning with symbolic AI. This integration has many promises, such as decreasing the amount of data required to train a neural network, imp…
IntelliGraphs: Datasets for Benchmarking Knowledge Graph Generation
Thiviyan Thanapalasingam, Emile van Krieken, Peter Bloem +1
Knowledge Graph Embedding (KGE) models are used to learn continuous representations of entities and relations. A key task in the literature is predicting missing links between enti…
Refining neural network predictions using background knowledge
Alessandro Daniele, Emile van Krieken, Luciano Serafini +1
Recent work has shown logical background knowledge can be used in learning systems to compensate for a lack of labeled training data. Many methods work by creating a loss function…
Analyzing Differentiable Fuzzy Implications
Emile van Krieken, Erman Acar, Frank van Harmelen
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 comp…
Analyzing Differentiable Fuzzy Logic Operators
Emile van Krieken, Erman Acar, Frank van Harmelen
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 approaches…
Semi-Supervised Learning using Differentiable Reasoning
Emile van Krieken, Erman Acar, Frank van Harmelen
We introduce Differentiable Reasoning (DR), a novel semi-supervised learning technique which uses relational background knowledge to benefit from unlabeled data. We apply it to the…