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Article

Tran, S. N., Garcez, A., Weyde, T. ORCID: 0000-0001-8028-9905 , Yin, J., Zhang, Q. ORCID: 0000-0003-0982-2986 & Karunanithi, M.view all authors (2020). Sequence Classification Restricted Boltzmann Machines With Gated Units. IEEE Transactions on Neural Networks and Learning Systems, 31(11), pp. 4806-4815. doi: 10.1109/tnnls.2019.2958103

Garcez, A., Gori, M., Lamb, L. C. , Serafini, L., Spranger, M. & Tran, S. N.view all authors (2019). Neural-symbolic computing: An effective methodology for principled integration of machine learning and reasoning. Journal of Applied Logics, 6(4), pp. 611-631.

Howe, J. M., Mota, E.D. & Garcez, A. (2017). Inductive learning in Shared Neural Multi-Spaces. CEUR Workshop Proceedings, 2003,

Tran, S. N., Cherla, S., Garcez, A. & Weyde, T.view all authors (2017). The Recurrent Temporal Discriminative Restricted Boltzmann Machines. CoRR,

Sarkar, S., Weyde, T., Garcez, A. , Slabaugh, G. G., Dragicevic, S. & Percy, C.view all authors (2016). Accuracy and interpretability trade-offs in machine learning applied to safer gambling. CEUR Workshop Proceedings, 1773,

Cherla, S., Tran, S.N., Weyde, T. & Garcez, A.view all authors (2016). Generalising the Discriminative Restricted Boltzmann Machine. pp. 111-119. doi: 10.1007/978-3-319-68612-7_13

Forechi, A., De Souza, A.F., Neto, J.D.O. , de Aguiar, E., Badue, C., Garcez, A. & Oliveira-Santos, T.view all authors (2016). Fat-Fast VG-RAM WNN: A high performance approach. NEUROCOMPUTING, 183, pp. 56-69. doi: 10.1016/j.neucom.2015.06.104

Percy, C., Garcez, A., Dragicevic, S. , França, M. V. M., Slabaugh, G. G. & Weyde, T.view all authors (2016). The Need for Knowledge Extraction: Understanding Harmful Gambling Behavior with Neural Networks. Frontiers in Artificial Intelligence and Applications, 285, pp. 974-981. doi: 10.3233/978-1-61499-672-9-974

Besold, T. R., Kuehnberger, K-U., Garcez, A. , Saffiotti, A., Fischer, M. H. & Bundy, A.view all authors (2015). Anchoring Knowledge in Interaction: Towards a Harmonic Subsymbolic/Symbolic Framework and Architecture of Computational Cognition. Lecture Notes in Computer Science, 9205, pp. 35-45. doi: 10.1007/978-3-319-21365-1_4

Perotti, A., Boella, G. & Garcez, A. (2014). Runtime Verification Through Forward Chaining. Electronic Proceedings in Theoretical Computer Science, 169, pp. 68-81. doi: 10.4204/eptcs.169.8

França, M. V. M., Zaverucha, G. & Garcez, A. (2014). Fast relational learning using bottom clause propositionalization with artificial neural networks. Machine Learning, 94(1), pp. 81-104. doi: 10.1007/s10994-013-5392-1

Sigtia, S., Benetos, E., Boulanger-Lewandowski, N. , Weyde, T., Garcez, A. & Dixon, S.view all authors (2014). A Hybrid Recurrent Neural Network For Music Transcription. CoRR, 14(11), article number 1623.

Tran, S. & Garcez, A. (2014). Low-cost representation for restricted Boltzmann machines. Lecture Notes in Computer Science, 8834, pp. 69-77. doi: 10.1007/978-3-319-12637-1_9

Borges, Rafael, Garcez, A. & Lamb, L. C. (2011). Learning and Representing Temporal Knowledge in Recurrent Networks. IEEE Transactions on Neural Networks, 22(12), pp. 2409-2421. doi: 10.1109/tnn.2011.2170180

Guillame-Bert, M., Broda, K. & Garcez, A. (2010). First-order logic learning in artificial neural networks. International Joint Conference on Neural Networks (IJCNN 2010), doi: 10.1109/IJCNN.2010.5596491

Garcez, A., Lamb, L. C. & Gabbay, D. M. (2007). Connectionist modal logic: Representing modalities in neural networks. Theoretical Computer Science, 371(1-2), pp. 34-53. doi: 10.1016/j.tcs.2006.10.023

Garcez, A., Gabbay, D. M., Ray, O. & Woods, J.view all authors (2007). Abductive reasoning in neural-symbolic learning systems. Topoi: An International Review of Philosophy, 26(1), pp. 37-49. doi: 10.1007/s11245-006-9005-5

Garcez, A., Gabbay, D. M. & Lamb, L. C. (2005). Value-based argumentation frameworks as neural-symbolic learning systems. Journal of Logic and Computation, 15(6), pp. 1041-1058. doi: 10.1093/logcom/exi057

Garcez, A. (2005). Fewer epistemological challenges for connectionism. Lecture Notes in Computer Science, 3526, pp. 289-325. doi: 10.1007/11494645_18

Garcez, A., Broda, K. & Gabbay, D. M. (2001). Symbolic knowledge extraction from trained neural networks: A sound approach. Artificial Intelligence, 125(1-2), pp. 153-205. doi: 10.1016/S0004-3702(00)00077-1

Garcez, A. & Zaverucha, G. (1999). The connectionist inductive learning and logic programming system. Applied Intelligence Journal, 11(1), pp. 59-77. doi: 10.1023/a:1008328630915

Book Section

Cherla, S., Weyde, T., Garcez, A. & Pearce, M.view all authors (2013). A Distributed Model For Multiple-Viewpoint Melodic Prediction. In: de Souza Britto Jr, A., Gouyon, F. & Dixon, S. (Eds.), Proceedings of the 14th International Society for Music Information Retrieval Conference. (pp. 15-20). International Society for Music Information Retrieval.

de Penning, L., Garcez, A., Lamb, L. C. & Meyer, J-J. C.view all authors (2011). A Neural-Symbolic Cognitive Agent for Online Learning and Reasoning. In: Proceedings of the Twenty-Second international joint conference on Artificial Intelligence. (pp. 1653-1658). International Joint Conferences on Artificial Intelligence. doi: 10.5591/978-1-57735-516-8/IJCAI11-278

Conference or Workshop Item

Tran, S. N. & Garcez, A. (2023). Neurosymbolic Reasoning and Learning with Restricted Boltzmann Machines. In: Williams, B., Chen, Y. & Neville, J. (Eds.), Proceedings of the AAAI Conference on Artificial Intelligence. 37th AAAI Conference on Artificial Intelligence, 7-14 Feb 2023, Washington, D.C., USA. doi: 10.1609/aaai.v37i5.25806

Ngan, K. H., Garcez, A. & Townsend, J. (2022). Extracting Meaningful High-Fidelity Knowledge from Convolutional Neural Networks. In: 2022 International Joint Conference on Neural Networks (IJCNN). 2022 International Joint Conference on Neural Networks (IJCNN), 18-23 Jul 2022, Padua, Italy. doi: 10.1109/ijcnn55064.2022.9892194

Philps, D., Garcez, A. & Weyde, T. ORCID: 0000-0001-8028-9905 (2019). Making Good on LSTMs' Unfulfilled Promise. Paper presented at the NeurIPS 2019 Workshop on Robust AI in Financial Services: Data, Fairness, Explainability, Trustworthiness, and Privacy, 13 Dec 2019, Vancouver, Canada.

Russell, A. J., Benetos, E. & Garcez, A. (2017). On the Memory Properties of Recurrent Neural Models. Paper presented at the 2017 International Joint Conference on Neural Networks, 14-19 May 2017, Anchorage, USA.

Sigtia, S., Benetos, E., Boulanger-Lewandowski, N. , Weyde, T., Garcez, A. & Dixon, S.view all authors (2015). A Hybrid Recurrent Neural Network For Music Transcription. Paper presented at the 40th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2015, 19-04-2015 - 24-04-2015, Brisbane, Australia.

França, M. V. M., Zaverucha, G. & Garcez, A. (2015). Neural Relational Learning Through Semi-Propositionalization of Bottom Clauses. Paper presented at the 2015 AAAI Spring Symposium Series, 23-03-2015 - 25-03-2015, Stanford University, USA.

Garcez, A., Besold, T. R., Raedt, L. , Foldiak, P., Hitzler, P., Icard, T., Kuhnberger, K-U., Lamb, L. C., Miikkulainen, R. & Silver, D. L.view all authors (2015). Neural-Symbolic Learning and Reasoning: Contributions and Challenges. Paper presented at the 2015 AAAI Spring Symposium Series, 23-03-2015 - 25-03-2015, Stanford University, USA.

Tran, S., Benetos, E. & Garcez, A. (2014). Learning motion-difference features using Gaussian restricted Boltzmann machines for efficient human action recognition. Paper presented at the 2014 International Joint Conference on Neural Networks (IJCNN), 06-07-2014 - 11-07-2014, Beijing, China. doi: 10.1109/IJCNN.2014.6889945

Sigtia, S., Benetos, E., Cherla, S. , Weyde, T., Garcez, A. & Dixon, S.view all authors (2014). An RNN-based Music Language Model for Improving Automatic Music Transcription. In: Wang, H-M, Yang, Y-H & Lee, JH (Eds.), http://www.terasoft.com.tw/conf/ismir2014//proceedings%5CISMIR2014_Proceedings.pdf. 15th International Society for Music Information Retrieval Conference (ISMIR), 27-10-2014 - 31-10-2014, Taipei, Taiwan.

Cherla, S., Weyde, T., Garcez, A. & Pearce, M.view all authors (2013). Learning Distributed Representations for Multiple-Viewpoint Melodic Prediction. Paper presented at the 14th International Society for Music Information Retrieval Conference, 4 - 8 Nov 2013, Curtiba, PR, Brazil.

Garcez, A. (2010). Neurons and symbols: a manifesto. Paper presented at the Dagstuhl Seminar Proceedings 10302. Learning paradigms in dynamic environments, 25 - 30 July 2010, Dagstuhl, Germany.

Komendantskaya, E., Broda, K. & Garcez, A. (2010). Using inductive types for ensuring correctness of neuro-symbolic computations. Paper presented at the 6th Conference on Computability in Europe, CiE 2010, 30 June - 4 July 2010, Ponta Delgada, Portugal.

Child, C. H. T., Stathis, K. & Garcez, A. (2007). Learning to Act with RVRL Agents. Paper presented at the 14th RCRA Workshop, Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion, Jul 2007, Rome, Italy.

Broda, K., Garcez, A. & Gabbay, D. M. (2005). Metalevel priorities and neural networks. Paper presented at the Workshop on the Foundations of Connectionist-Symbolic Integration ECAI2000, 20 - 25 August 2005, Berlin.

Hitzler, P., Bader, S. & Garcez, A. (2005). Ontology learning as a use-case for neural-symbolic integration. Paper presented at the IJCAI Workshop on Neural-Symbolic Learning and Reasoning NeSy05, 1 August 2005, Edinburgh.

Report

Garcez, A., Gabbay, D. M. & Lamb, L. C. (2004). Argumentation Neural Networks: Value-based Argumentation Frameworks as Neural-Symbolic Learning Systems (TR/2004/DOC/01). .

Garcez, A. & Gabbay, D. M. (2003). Fibring Neural Networks (TR/2003/SEG/03). .

Garcez, A., Spanoudakis, G. & Zisman, A. (2003). Proceedings of ACM ESEC/FSE International Workshop on Intelligent Technologies for Software Engineering WITSE03 (TR/2003/DOC/01). .

Working Paper

Tran, S. N. & Garcez, A. (2014). Adaptive Feature Ranking for Unsupervised Transfer Learning. .