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Items where City Author is "Garcez, A."

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Number of items: 47.

Mota, E., Howe, J. M. ORCID: 0000-0001-8013-6941, Schramm, A. and d'Avila Garcez, A. S. (2019). Efficient Predicate Invention using Shared NeMuS. Paper presented at the 14th International Workshop on Neural-Symbolic Learning and Reasoning, 10 - 16 August 2019, Macau, China.

Tran, S.N. and d'Avila Garcez, A. S. (2018). Deep Logic Networks: Inserting and Extracting Knowledge from Deep Belief Networks. IEEE Transactions on Neural Networks and Learning Systems, 29(2), pp. 246-258. doi: 10.1109/TNNLS.2016.2603784

Howe, J. M., Mota, E.D. and Garcez, A. (2017). Inductive learning in Shared Neural Multi-Spaces. Paper presented at the Twelfth International Workshop on Neural-Symbolic Learning and Reasoning 2017, July 17-18, 2017, London, UK.

Donadello, I., Serafini, L. and d'Avila Garcez, A. S. (2017). Logic tensor networks for semantic image interpretation. Paper presented at the IJCAI 2017, 19-25 Aug 2017, Melbourne, Australia.

Russell, A. J., Benetos, E. and 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.

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

Ali, H., Tran, S. N., Benetos, E. and d'Avila Garcez, A. S. (2016). Speaker recognition with hybrid features from a deep belief network. Neural Computing and Applications, doi: 10.1007/s00521-016-2501-7

Cherla, S., Tran, S.N., Weyde, T. and Garcez, A. (2016). Generalising the Discriminative Restricted Boltzmann Machine.

Forechi, A., De Souza, A.F., Neto, J.D.O., de Aguiar, E., Badue, C., Garcez, A. and Oliveira-Santos, T. (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. and Weyde, T. (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

Sigtia, S., Benetos, E., Boulanger-Lewandowski, N., Weyde, T., Garcez, A. and Dixon, S. (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.

Perotti, A., d'Avila Garcez, A. S. and Boella, G. (2015). Neural-Symbolic Monitoring and Adaptation. In: Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN 2015). (pp. 1-8). IEEE.

Besold, T. R., Kuehnberger, K-U., Garcez, A., Saffiotti, A., Fischer, M. H. and Bundy, A. (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

França, M. V. M., Zaverucha, G. and 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. and Silver, D. L. (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.

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

Ali, H., D'Avila Garcez, A.S., Tran, S.N., Zhou, X. and Iqbal, K. (2014). Unimodal late fusion for NIST i-vector challenge on speaker detection. Electronics Letters, 50(15), pp. 1098-1100. doi: 10.1049/el.2014.1207

França, M. V. M., Zaverucha, G. and 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. and Dixon, S. (2014). A Hybrid Recurrent Neural Network For Music Transcription. CoRR, 14(11), p. 1623.

Tran, S., Benetos, E. and 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.

Tran, S. and 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

Sigtia, S., Benetos, E., Cherla, S., Weyde, T., Garcez, A. and Dixon, S. (2014). An RNN-based Music Language Model for Improving Automatic Music Transcription. Paper presented at the 15th International Society for Music Information Retrieval Conference (ISMIR), 27-10-2014 - 31-10-2014, Taipei, Taiwan.

d'Avila Garcez, A. S., Gabbay, D. M. and Lamb, L. C. (2014). A neural cognitive model of argumentation with application to legal inference and decision making. Journal of Applied Logic, 12(2), pp. 109-127. doi: 10.1016/j.jal.2013.08.004

Cherla, S., Weyde, T., Garcez, A. and Pearce, M. (2013). A Distributed Model For Multiple-Viewpoint Melodic Prediction. In: de Souza Britto Jr, A., Gouyon, F. and Dixon, S. (Eds.), Proceedings of the 14th International Society for Music Information Retrieval Conference. (pp. 15-20). International Society for Music Information Retrieval. ISBN 978-0-615-90065-0

Cherla, S., Weyde, T., Garcez, A. and Pearce, M. (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.

Borges, Rafael, Garcez, A. and 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

de Penning, L., Garcez, A., Lamb, L. C. and Meyer, J-J. C. (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. ISBN 978-1-57735-514-4

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.

Guillame-Bert, M., Broda, K. and 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

Komendantskaya, E., Broda, K. and 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.

Renou, L. and d'Avila Garcez, A. S. (2008). Rule Extraction from Support Vector Machines: A Geometric Approach. Technical Report (TR/2008/DOC/01). Department of Computing, City University London: .

Garcez, A., Lamb, L. C. and 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. and Woods, J. (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

Child, C. H. T., Stathis, K. and 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.

d'Avila Garcez, A. S., Hitzler, P. and Tamburrini, G. (2006). Proceedings of ECAI International Workshop on Neural-Symbolic Learning and reasoning NeSy 2006 (TR/2006/DOC/02). .

Garcez, A., Gabbay, D. M. and 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

Broda, K., Garcez, A. and 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.

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

Dafas, P. and d'Avila Garcez, A. S. (2005). Applied temporal Rule Mining to Time Series (TR/2006/DOC/01). .

Hitzler, P., Bader, S. and 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.

d'Avila Garcez, A. S. (2005). Proceedings of IJCAI International Workshop on Neural-Symbolic Learning and Reasoning NeSy 2005 (TR/2005/DOC/01). .

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

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

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

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

Tran, S. N. and Garcez, A. Adaptive Feature Ranking for Unsupervised Transfer Learning. .

This list was generated on Sat Aug 17 04:34:51 2019 UTC.