Faithful approaches to rule learning
Rule learning involves developing machine learning models that can be applied to a set of logical facts to predict additional facts, as well as providing methods for extracting from the learned model a set of logical rules that explain symbolically the model’s predictions. Existing such approaches,...
Những tác giả chính: | Tena Cucala, DJ, Cuenca Grau, B, Motik, B |
---|---|
Định dạng: | Conference item |
Ngôn ngữ: | English |
Được phát hành: |
IJCAI Organization
2022
|
Những quyển sách tương tự
-
Faithful rule extraction for differentiable rule learning models
Bằng: Wang, X, et al.
Được phát hành: (2024) -
Relational graph convolutional networks do not learn sound rules
Bằng: Morris, M, et al.
Được phát hành: (2024) -
Explainable GNN-based models over knowledge graphs
Bằng: Tena Cucala, DJ, et al.
Được phát hành: (2022) -
On the correspondence between monotonic max-sum GNNs and datalog
Bằng: Tena Cucala, D, et al.
Được phát hành: (2023) -
On the correspondence between monotonic max-sum GNNs and Datalog
Bằng: Tena Cucala, D, et al.
Được phát hành: (2023)