Semantic-based regularization for learning and inference

DOI10.1016/j.artint.2015.08.011zbMath1404.68100OpenAlexW2201744460MaRDI QIDQ511774

Michelangelo Diligenti, Marco Gori, Claudio Saccà

Publication date: 22 February 2017

Published in: Artificial Intelligence (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.artint.2015.08.011

zbMATH Keywords

first-order logickernel machineslearning with constraints

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Logic in artificial intelligence (68T27)

Related Items (9)

On the Relation Between Loss Functions and T-Norms ⋮ \(\alpha\)ILP: thinking visual scenes as differentiable logic programs ⋮ Refining neural network predictions using background knowledge ⋮ Neural logic rule layers ⋮ Neural probabilistic logic programming in DeepProbLog ⋮ Analyzing differentiable fuzzy logic operators ⋮ Beyond graph neural networks with lifted relational neural networks ⋮ Multi-Label Classification Neural Networks with Hard Logical Constraints ⋮ Injecting domain knowledge in neural networks: a controlled experiment on a constrained problem

Uses Software

• kFOIL
• Tuffy

Cites Work

• Advanced concepts in fuzzy logic and systems with membership uncertainty
• Bridging logic and kernel machines
• First-order fuzzy logic
• Practical bilevel optimization. Algorithms and applications
• Metamathematics of fuzzy logic
• Logic programs and connectionist networks
• Fast learning of relational kernels
• Theory of T-norms and fuzzy inference methods
• Probabilistic inductive logic programming. Theory and applications
• Markov logic networks
• Regularization Algorithms for Learning That Are Equivalent to Multilayer Networks
• Learning Theory and Kernel Machines
• Backtrack Programming
• Fuzzy sets
• Relational learning with statistical predicate invention: Better models for hypertext
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