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Compendium of Neurosymbolic Artificial Intelligence 2023
- Pascal Hitzler, Md. Kamruzzaman Sarker
, Aaron Eberhart:
Compendium of Neurosymbolic Artificial Intelligence. Frontiers in Artificial Intelligence and Applications 369, IOS Press 2023, ISBN 978-1-64368-406-2 - Daniel L. Silver, Tom M. Mitchell:
The Roles of Symbols in Neural-Based AI: They Are Not What You Think! 1-28 - Gunjan Singh, Sumit Bhatia, Raghava Mutharaju:
Neuro-Symbolic RDF and Description Logic Reasoners: The State-Of-The-Art and Challenges. 29-63 - Annette ten Teije
Architectural Patterns for Neuro-Symbolic AI. 64-76 - Laura Waltersdorfer, Anna Breit, Fajar J. Ekaputra
Semantic Web Machine Learning Systems: An Analysis of System Patterns. 77-99 - Miguel Ángel Méndez Lucero, Vaishak Belle:
Boolean Connectives and Deep Learning: Three Interpretations. 100-113 - Thomas Schmid:
Constructivist Machine Learning. 114-124 - Bowen Tan, Shibo Hao, Eric P. Xing, Zhiting Hu:
Neural-Symbolic Interaction and Co-Evolving. 125-152 - Bryon Aragam, Pradeep Ravikumar:
Neuro-Causal Models. 153-177 - Filip Ilievski, Kaixin Ma, Alessandro Oltramari, Peifeng Wang, Jay Pujara:
Building Robust and Explainable AI with Commonsense Knowledge Graphs and Neural Models. 178-209 - Harald Maurer:
Connectionist Neuroarchitectures in Cognition and Consciousness Theory Based on Integrative (Synchronization) Mechanisms. 210-234 - Loizos Michael:
Autodidactic and Coachable Neural Architectures. 235-248 - Frank van der Velde:
The Neural Blackboard Theory of Neuro-Symbolic Processing: Logistics of Access, Connection Paths and Intrinsic Structures. 249-271 - Axel-Cyrille Ngonga Ngomo, Caglar Demir, N'Dah Jean Kouagou
Class Expression Learning with Multiple Representations. 272-286 - Pouya Ghiasnezhad Omran
Embedding-Based First-Order Rule Learning in Large Knowledge Graphs. 287-307 - Gustav Sír, Filip Zelezný, Ondrej Kuzelka:
Lifted Relational Neural Networks: From Graphs to Deep Relational Learning. 308-336 - Joe Townsend, Esma Mansouri-Benssassi, Kwun Ho Ngan, Artur d'Avila Garcez:
Discovering Visual Concepts and Rules in Convolutional Neural Networks. 337-372 - Michael Cochez
Approximate Answering of Graph Queries. 373-386 - David Leake, Zachary Wilkerson, Xiaomeng Ye, David J. Crandall:
Enhancing Case-Based Reasoning with Neural Networks. 387-409 - Jae Hee Lee, Michael Sioutis, Kyra Ahrens, Marjan Alirezaie, Matthias Kerzel, Stefan Wermter:
Neuro-Symbolic Spatio-Temporal Reasoning. 410-429 - Yatin Nandwani
Neuro-Symbolic Architectures for Combinatorial Problems in Structured Output Spaces. 430-459 - Martin Glauer, Till Mossakowski, Fabian Neuhaus, Adel Memariani, Janna Hastings:
Neuro-Symbolic Semantic Learning for Chemistry. 460-484 - Kareem Ahmed, Stefano Teso, Paolo Morettin, Luca Di Liello, Pierfrancesco Ardino, Jacopo Gobbi, Yitao Liang, Eric Wang, Kai-Wei Chang, Andrea Passerini, Guy Van den Broeck:
Semantic Loss Functions for Neuro-Symbolic Structured Prediction. 485-505 - Shamsuddeen Hassan Muhammad, Pavel Brazdil
Combining Symbolic and Deep Learning Approaches for Sentiment Analysis. 506-521 - Geethan Karunaratne, Michael Hersche, Giovanni Cherubini, Abu Sebastian, Abbas Rahimi
Few-Shot Continual Learning Based on Vector Symbolic Architectures. 522-546 - Gabriele Ciravegna, Francesco Giannini
Learning Logic Explanations by Neural Networks. 547-558 - Anna Himmelhuber, Stephan Grimm, Mitchell Joblin, Sonja Zillner, Thomas A. Runkler:
Combining Sub-Symbolic and Symbolic Methods for Explainability. 559-576 - Vitor A. C. Horta, Alessandra Mileo:
Explaining CNNs Using Knowledge Extraction and Graph Analysis. 577-608 - Sandro Preto, Marcelo Finger:
Effective Reasoning over Neural Networks Using Łukasiewicz Logic. 609-630 - Jonathan Herzig, Jonathan Berant, Ben Bogin:
Latent Trees for Compositional Generalization. 631-664 - Xianggen Liu, Zhengdong Lu, Lili Mou:
Weakly Supervised Reasoning by Neuro-Symbolic Approaches. 665-692
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