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ORCIDNeSy 2023: Siena, Italy
- Artur S. d'Avila Garcez, Tarek R. Besold, Marco Gori, Ernesto Jiménez-Ruiz:
Proceedings of the 17th International Workshop on Neural-Symbolic Learning and Reasoning, La Certosa di Pontignano, Siena, Italy, July 3-5, 2023. CEUR Workshop Proceedings 3432, CEUR-WS.org 2023
Research papers track
- Maurizio Proietti, Francesca Toni:
A Roadmap for Neuro-argumentative Learning. 1-8 - Ouns El Harzli, Samy Badreddine, Tarek R. Besold:
What's Wrong with Gradient-based Complex Query Answering? 9-18 - Kwun Ho Ngan, James Phelan, Esma Mansouri-Benssassi, Joe Townsend, Artur S. d'Avila Garcez:
Closing the Neural-Symbolic Cycle: Knowledge Extraction, User Intervention and Distillation from Convolutional Neural Networks. 19-43 - Luca Salvatore Lorello, Marco Lippi:
The Challenge of Learning Symbolic Representations. 44-61 - Moa Johansson, Nicholas Smallbone:
Exploring Mathematical Conjecturing with Large Language Models. 62-77 - Mattijs Baert, Sam Leroux, Pieter Simoens:
Learning Logic Constraints From Demonstration. 78-84 - Fernando Zhapa-Camacho, Robert Hoehndorf:
From Axioms over Graphs to Vectors, and Back Again: Evaluating the Properties of Graph-based Ontology Embeddings. 85-102 - Jingyuan Sha, Hikaru Shindo, Kristian Kersting, Devendra Singh Dhami:
Neural-Symbolic Predicate Invention: Learning Relational Concepts from Visual Scenes. 103-117 - Vitor A. C. Horta, Robin Sobczyk, Maarten C. Stol, Alessandra Mileo:
Semantic Interpretability of Convolutional Neural Networks by Taxonomy Extraction. 118-127 - Davide Beretta, Stefania Monica, Federico Bergenti:
Preliminary Results on a State-Driven Method for Rule Construction in Neural-Symbolic Reinforcement Learning. 128-138 - Thomas Eiter, Nelson Higuera Ruiz, Johannes Oetsch:
A Modular Neurosymbolic Approach for Visual Graph Question Answering. 139-149 - Jedrzej Potoniec:
Is the Proof Length a Good Indicator of Hardness for Reason-able Embeddings? 150-161 - Emanuele Marconato, Stefano Teso, Andrea Passerini:
Neuro-Symbolic Reasoning Shortcuts: Mitigation Strategies and their Limitations. 162-166 - Rory Ward, Muhammad Jaleed Khan, John G. Breslin, Edward Curry:
Knowledge-Guided Colorization: Overview, Prospects and Challenges. 167-173 - Gaia Saveri, Luca Bortolussi:
Towards Invertible Semantic-Preserving Embeddings of Logical Formulae. 174-194 - Matthew S. Brown, M. Wasil Wahi-Anwar, Youngwon Choi, Morgan Daly, Liza Shrestha, Koon-Pong Wong, Jonathan G. Goldin, Dieter R. Enzmann:
Implementing Trustworthy AI in Real-world Medical Imaging using the SimpleMind Software Environment. 195-203 - Kristian J. Hammond, David B. Leake:
Large Language Models Need Symbolic AI. 204-209 - Sofoklis Kyriakopoulos, Artur S. d'Avila Garcez:
Continual Reasoning: Non-monotonic Reasoning in Neurosymbolic AI using Continual Learning. 210-222 - Lia Morra, Alberto Azzari
, Letizia Bergamasco, Marco Braga, Luigi Capogrosso, Federico Delrio, Giuseppe Di Giacomo, Simone Eiraudo, Giorgia Ghione, Rocco Giudice, Alkis Koudounas, Luca Piano, Daniele Rege Cambrin, Matteo Risso, Marco Rondina, Alessandro Sebastian Russo, Marco Russo, Francesco Taioli, Lorenzo Vaiani, Chiara Vercellino:
Designing Logic Tensor Networks for Visual Sudoku Puzzle Classification. 223-232 - Roxana Pop, Egor V. Kostylev:
Inductive Future Time Prediction on Temporal Knowledge Graphs with Interval Time. 233-240 - Alberto Speranzon, Christian H. Debrunner, David Rosenbluth, Mauricio Castillo-Effen, Anthony R. Nowicki, Kevin Alcedo, Andrzej Banaszuk:
Challenge Problems in Developing a Neuro-Symbolic OODA Loop. 241-247 - Johanna Ott, Arthur Ledaguenel, Céline Hudelot, Mattis Hartwig:
How to Think About Benchmarking Neurosymbolic AI? 248-254 - Elena Umili, Francesco Argenziano, Aymeric Barbin, Roberto Capobianco:
Visual Reward Machines. 255-267 - Marc Otto, Octavio Arriaga, Chandandeep Singh, Jichen Guo, Frank Kirchner:
PhysWM: Physical World Models for Robot Learning. 268-278 - Michael Hersche, Zuzanna Opala, Geethan Karunaratne, Abu Sebastian, Abbas Rahimi:
Decoding Superpositions of Bound Symbols Represented by Distributed Representations. 279-288 - Flavio Petruzzellis, Alberto Testolin, Alessandro Sperduti:
A Hybrid System for Systematic Generalization in Simple Arithmetic Problems. 289-301 - Francesco S. Carzaniga, Michael Hersche, Kaspar Schindler, Abbas Rahimi:
VSA-based Positional Encoding Can Replace Recurrent Networks in Emergent Symbol Binding. 302-326 - David Herron, Ernesto Jiménez-Ruiz, Tillman Weyde:
On the Benefits of OWL-based Knowledge Graphs for Neural-Symbolic Systems. 327-335 - Katrin Schreiberhuber, Marta Sabou, Fajar J. Ekaputra, Peter Knees, Peb Ruswono Aryan, Alfred Einfalt, Ralf Mosshammer:
Causality Prediction with Neural-Symbolic Systems: A Case Study in Smart Grids. 336-347 - Manuel Eberhardinger, Johannes Maucher, Setareh Maghsudi:
Towards Explainable Decision Making with Neural Program Synthesis and Library Learning. 348-368 - Mihaela C. Stoian, Eleonora Giunchiglia, Thomas Lukasiewicz:
Exploiting T-norms for Deep Learning in Autonomous Driving. 369-380 - Mouloud Iferroudjene, Victor Charpenay, Antoine Zimmermann:
FB15k-CVT: A Challenging Dataset for Knowledge Graph Embedding Models. 381-394 - Abhinav Kumar Thakur, Filip Ilievski, Hông-Ân Sandlin, Zhivar Sourati, Luca Luceri, Riccardo Tommasini, Alain Mermoud:
Explainable Classification of Internet Memes. 395-409
Recently published papers track (extended abstracts)
- Emanuele Marconato, Andrea Passerini, Stefano Teso:
GlanceNets: Interpretable, Leak-proof Concept-based Models. 410 - Cristina Cornelio, Jan Stühmer, Shell Xu Hu, Timothy M. Hospedales:
Learning Where and When to Reason in Neuro-Symbolic Inference. 411-412 - Kareem Ahmed, Stefano Teso, Kai-Wei Chang, Guy Van den Broeck, Antonio Vergari:
Semantic Probabilistic Layers for Neuro-Symbolic Learning. 413 - Lucile Dierckx, Rosana Veroneze, Siegfried Nijssen:
RL-Net: Interpretable Rule Learning with Neural Networks. 414-415 - Alessandro Oltramari, Jonathan Francis, Filip Ilievski, Kaixin Ma, Roshanak Mirzaee:
Generalizable Neuro-Symbolic Systems for Commonsense Question Answering. 416-417 - Alessandro Daniele, Tommaso Campari, Sagar Malhotra, Luciano Serafini:
Deep Symbolic Learning: Discovering Symbols and Rules from Perceptions. 418-419 - Daniel L. Silver, Tom M. Mitchell:
The Roles of Symbols in Neural-based AI: They are Not What You Think! 420-421 - Pietro Barbiero, Gabriele Ciravegna, Francesco Giannini, Mateo Espinosa Zarlenga, Lucie Charlotte Magister, Alberto Tonda, Pietro Liò, Frédéric Precioso, Mateja Jamnik, Giuseppe Marra:
Interpretable Neural-Symbolic Concept Reasoning. 422-423 - Anna Breit, Laura Waltersdorfer, Fajar J. Ekaputra, Marta Sabou, Andreas Ekelhart, Andreea Iana, Heiko Paulheim, Jan Portisch, Artem Revenko, Frank van Harmelen, Annette ten Teije:
Combining Machine Learning and Semantic Web: A Systematic Mapping Study. 424 - Michael Hersche, Mustafa Zeqiri, Luca Benini, Abu Sebastian, Abbas Rahimi:
Solving Raven's Progressive Matrices via a Neuro-vector-symbolic Architecture. 425-426 - Michael Akintunde, Elena Botoeva, Panagiotis Kouvaros, Alessio Lomuscio:
Verifying Strategic Abilities of Neural-Symbolic Multi-agent Systems. 427 - Wen-Chi Yang, Giuseppe Marra, Gavin Rens, Luc De Raedt:
Safe Reinforcement Learning via Probabilistic Logic Shields. 428-429 - N'Dah Jean Kouagou, Stefan Heindorf, Caglar Demir, Axel-Cyrille Ngonga Ngomo:
Neural Class Expression Synthesis. 430-431 - Gabriele Ciravegna, Pietro Barbiero, Francesco Giannini, Marco Gori, Pietro Liò, Marco Maggini, Stefano Melacci:
Logic Explained Networks. 432-433 - Eleonora Misino, Giuseppe Marra, Emanuele Sansone:
VAEL: Bridging Variational Autoencoders and Probabilistic Logic Programming. 434-435
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