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Inductive Logic Programming: 30th International Conference, ILP 2021, Virtual Event, October 25-27, 2021, Proceedings

Inductive Logic Programming: 30th International Conference, ILP 2021, Virtual Event, October 25-27, 2021, Proceedings (Paperback)

Alexander Artikis, Nikos Katzouris (엮은이)
Springer
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Inductive Logic Programming: 30th International Conference, ILP 2021, Virtual Event, October 25-27, 2021, Proceedings
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책 정보

· 제목 : Inductive Logic Programming: 30th International Conference, ILP 2021, Virtual Event, October 25-27, 2021, Proceedings (Paperback) 
· 분류 : 외국도서 > 컴퓨터 > 인공지능(AI)
· ISBN : 9783030974534
· 쪽수 : 283쪽
· 출판일 : 2022-02-24

목차

Embedding Models for Knowledge Graphs Induced by Clusters of Relations and Background Knowledge.- Fanizzi Automatic Conjecturing of P-Recursions Using Lifted Inference.- Machine learning of microbial interactions using Abductive ILP and Hypothesis Frequency/Compression Estimation.- Answer-Set Programs for Reasoning about Counterfactual Interventions and Responsibility Scores for Classification.- Reyes Synthetic Datasets and Evaluation Tools for Inductive Neural Reasoning.- Using Domain-Knowledge to Assist Lead Discovery in Early-Stage Drug Design.- Non-Parametric Learning of Embeddings for Relational Data using Gaifman Locality Theorem.- Ontology Graph Embeddings and ILP for Financial Forecasting.- Transfer learning for boosted relational dependency networks through genetic algorithm.- Online Learning of Logic Based Neural Network Structures.- Programmatic policy extraction by iterative local search.- Mapping across relational domains for transfer learning with word embeddings-based similarity.- A First Step Towards Even More Sparse Encodings of Probability Distributions.- Feature Learning by Least Generalization.- Learning Logic Programs Using Neural Networks by Exploiting Symbolic Invariance.- Learning and revising dynamic temporal theories in the full Discrete Event Calculus.- Human-like rule learning from images using one-shot hypothesis derivation.- Generative Clausal Networks: Relational Decision Trees as Probabilistic Circuits.- A Simulated Annealing Meta-heuristic for Concept Learning in Description Logics.

 

저자소개

Alexander Artikis (엮은이)    정보 더보기
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Nikos Katzouris (엮은이)    정보 더보기
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