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Статті в журналах з теми "Neural-symbolic"
Zhang, Jing, Bo Chen, Lingxi Zhang, Xirui Ke, and Haipeng Ding. "Neural, symbolic and neural-symbolic reasoning on knowledge graphs." AI Open 2 (2021): 14–35. http://dx.doi.org/10.1016/j.aiopen.2021.03.001.
Повний текст джерелаVenkatraman, Vinod, Daniel Ansari, and Michael W. L. Chee. "Neural correlates of symbolic and non-symbolic arithmetic." Neuropsychologia 43, no. 5 (January 2005): 744–53. http://dx.doi.org/10.1016/j.neuropsychologia.2004.08.005.
Повний текст джерелаNeto, João Pedro, Hava T. Siegelmann, and J. Félix Costa. "Symbolic processing in neural networks." Journal of the Brazilian Computer Society 8, no. 3 (April 2003): 58–70. http://dx.doi.org/10.1590/s0104-65002003000100005.
Повний текст джерелаDo, Quan, and Michael E. Hasselmo. "Neural circuits and symbolic processing." Neurobiology of Learning and Memory 186 (December 2021): 107552. http://dx.doi.org/10.1016/j.nlm.2021.107552.
Повний текст джерелаSmolensky, Paul. "Symbolic functions from neural computation." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 370, no. 1971 (July 28, 2012): 3543–69. http://dx.doi.org/10.1098/rsta.2011.0334.
Повний текст джерелаShavlik, Jude W. "Combining symbolic and neural learning." Machine Learning 14, no. 3 (March 1994): 321–31. http://dx.doi.org/10.1007/bf00993982.
Повний текст джерелаSetiono, R., and Huan Liu. "Symbolic representation of neural networks." Computer 29, no. 3 (March 1996): 71–77. http://dx.doi.org/10.1109/2.485895.
Повний текст джерелаTsamoura, Efthymia, Timothy Hospedales, and Loizos Michael. "Neural-Symbolic Integration: A Compositional Perspective." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 6 (May 18, 2021): 5051–60. http://dx.doi.org/10.1609/aaai.v35i6.16639.
Повний текст джерелаTaha, I. A., and J. Ghosh. "Symbolic interpretation of artificial neural networks." IEEE Transactions on Knowledge and Data Engineering 11, no. 3 (1999): 448–63. http://dx.doi.org/10.1109/69.774103.
Повний текст джерелаBOOKMAN, LAWRENCE A., and RON SUN. "EDITORIAL Integrating Neural and Symbolic Processes." Connection Science 5, no. 3-4 (January 1993): 203–4. http://dx.doi.org/10.1080/09540099308915699.
Повний текст джерелаДисертації з теми "Neural-symbolic"
Bader, Sebastian. "Neural-Symbolic Integration." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2009. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-25468.
Повний текст джерелаTownsend, Joseph Paul. "Artificial development of neural-symbolic networks." Thesis, University of Exeter, 2014. http://hdl.handle.net/10871/15162.
Повний текст джерелаXiao, Chunyang. "Neural-Symbolic Learning for Semantic Parsing." Thesis, Université de Lorraine, 2017. http://www.theses.fr/2017LORR0268/document.
Повний текст джерелаOur goal in this thesis is to build a system that answers a natural language question (NL) by representing its semantics as a logical form (LF) and then computing the answer by executing the LF over a knowledge base. The core part of such a system is the semantic parser that maps questions to logical forms. Our focus is how to build high-performance semantic parsers by learning from (NL, LF) pairs. We propose to combine recurrent neural networks (RNNs) with symbolic prior knowledge expressed through context-free grammars (CFGs) and automata. By integrating CFGs over LFs into the RNN training and inference processes, we guarantee that the generated logical forms are well-formed; by integrating, through weighted automata, prior knowledge over the presence of certain entities in the LF, we further enhance the performance of our models. Experimentally, we show that our approach achieves better performance than previous semantic parsers not using neural networks as well as RNNs not informed by such prior knowledge
Mann, Jordyn(Jordyn L. ). "Neural Bayesian goal inference for symbolic planning domains." Thesis, Massachusetts Institute of Technology, 2021. https://hdl.handle.net/1721.1/130701.
Повний текст джерелаCataloged from the official PDF of thesis.
Includes bibliographical references (pages 51-52).
There are several reasons for which one may aim to infer the short- and long-term goals of agents in diverse physical domains. As increasingly powerful autonomous systems come into development, it is conceivable that they may eventually need to accurately infer the goals of humans. There are also more immediate reasons for which this sort of inference may be desirable, such as in the use case of intelligent personal assistants. This thesis introduces a neural Bayesian approach to goal inference in multiple symbolic planning domains and compares the results of this approach to the results of a recently developed Monte Carlo Bayesian inference method known as Sequential Inverse Plan Search (SIPS). SIPS is based on sequential Monte Carlo inference for Bayesian inversion of probabilistic plan search in Planning Domain Definition Language (PDDL) domains. In addition to the neural architectures, the thesis also introduces approaches for converting PDDL predicate state representations to numerical arrays and vectors suitable for input to the neural networks. The experimental results presented indicate that for the domains investigated, in cases where the training set is representative of the test set, the neural approach provides similar accuracy results to SIPS in the later portions of the observation sequences with a far shorter amortized time cost. However, in earlier timesteps of those observation sequences and in cases where the training set is less similar to the testing set, SIPS outperforms the neural approach in terms of accuracy. These results indicate that a model-based inference method where SIPS uses a neural proposal based on the neural networks designed in this thesis could have the potential to combine the advantages of both goal inference approaches by improving the speed of SIPS inference while maintaining generalizability and high accuracy throughout the timesteps of the observation sequences.
by Jordyn Mann.
M. Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
Noda, Itsuki. "Neural Networks that Learn Symbolic and Structred Representation of Information." Kyoto University, 1995. http://hdl.handle.net/2433/154663.
Повний текст джерелаKyoto University (京都大学)
0048
新制・課程博士
博士(工学)
甲第5860号
工博第1404号
新制||工||978(附属図書館)
UT51-95-B205
京都大学大学院工学研究科電気工学専攻
(主査)教授 長尾 真, 教授 池田 克夫, 教授 矢島 脩三
学位規則第4条第1項該当
Chichlowski, Kazimierz O. "Modelling and recognition of continuous and symbolic data using artificial neural networks." Thesis, University of East Anglia, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.320829.
Повний текст джерелаChen, Hsinchun, P. Buntin, Linlin She, S. Sutjahjo, C. Sommer, and D. Neely. "Expert Prediction, Symbolic Learning, and Neural Networks: An Experiment on Greyhound Racing." IEEE, 1994. http://hdl.handle.net/10150/105472.
Повний текст джерелаFor our research, we investigated a different problem-solving scenario called game playing, which is unstructured, complex, and seldom-studied. We considered several real-life game-playing scenarios and decided on greyhound racing. The large amount of historical information involved in the search poses a challenge for both human experts and machine-learning algorithms. The questions then become: Can machine-learning techniques reduce the uncertainty in a complex game-playing scenario? Can these methods outperform human experts in prediction? Our research sought to answer these questions.
Chen, Hsinchun. "Machine Learning for Information Retrieval: Neural Networks, Symbolic Learning, and Genetic Algorithms." Wiley Periodicals, Inc, 1995. http://hdl.handle.net/10150/106427.
Повний текст джерелаInformation retrieval using probabilistic techniques has attracted significant attention on the part of researchers in information and computer science over the past few decades. In the 1980s, knowledge-based techniques also made an impressive contribution to “intelligent” information retrieval and indexing. More recently, information science researchers have turned to other newer artificial-intelligence- based inductive learning techniques including neural networks, symbolic learning, and genetic algorithms. These newer techniques, which are grounded on diverse paradigms, have provided great opportunities for researchers to enhance the information processing and retrieval capabilities of current information storage and retrieval systems. In this article, we first provide an overview of these newer techniques and their use in information science research. To familiarize readers with these techniques, we present three popular methods: the connectionist Hopfield network; the symbolic ID3/ID5R; and evolution- based genetic algorithms. We discuss their knowledge representations and algorithms in the context of information retrieval. Sample implementation and testing results from our own research are also provided for each technique. We believe these techniques are promising in their ability to analyze user queries, identify users’ information needs, and suggest alternatives for search. With proper user-system interactions, these methods can greatly complement the prevailing full-text, keywordbased, probabilistic, and knowledge-based techniques.
Tang, Zibin. "A new design approach for numeric-to-symbolic conversion using neural networks." PDXScholar, 1991. https://pdxscholar.library.pdx.edu/open_access_etds/4242.
Повний текст джерелаCarmantini, Giovanni Sirio. "Dynamical systems theory for transparent symbolic computation in neuronal networks." Thesis, University of Plymouth, 2017. http://hdl.handle.net/10026.1/8647.
Повний текст джерелаКниги з теми "Neural-symbolic"
d’Avila Garcez, Artur S., Krysia B. Broda, and Dov M. Gabbay. Neural-Symbolic Learning Systems. London: Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0211-3.
Повний текст джерелаC, Lamb Luís, and Gabbay Dov M. 1945-, eds. Neural-symbolic cognitive reasoning. Berlin: Springer, 2009.
Знайти повний текст джерелаPascal, Hitzler, and SpringerLink (Online service), eds. Perspectives of Neural-Symbolic Integration. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2007.
Знайти повний текст джерелаHammer, Barbara, and Pascal Hitzler, eds. Perspectives of Neural-Symbolic Integration. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73954-8.
Повний текст джерелаSun, Ron, and Lawrence A. Bookman, eds. Computational Architectures Integrating Neural And Symbolic Processes. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/b102608.
Повний текст джерелаGarcez, Artur S. D'Avila. Neural-Symbolic Learning Systems: Foundations and Applications. London: Springer London, 2002.
Знайти повний текст джерелаInternational School on Neural Nets "E.R. Caianiello" Fifth Course: From Synapses to Rules: Discovering Symbolic Rules From Neural Processed Data (2002 Erice, Italy). From synapses to rules: Discovering symbolic rules from neural processed data. New York: Kluwer Academic/Plenum Pub., 2002.
Знайти повний текст джерелаApolloni, Bruno. From Synapses to Rules: Discovering Symbolic Rules from Neural Processed Data. Boston, MA: Springer US, 2002.
Знайти повний текст джерелаDong, Tiansi. A Geometric Approach to the Unification of Symbolic Structures and Neural Networks. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-56275-5.
Повний текст джерелаRuan, Da. Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks, and Genetic Algorithms. Boston, MA: Springer US, 1997.
Знайти повний текст джерелаЧастини книг з теми "Neural-symbolic"
d’Avila Garcez, Artur S., Krysia B. Broda, and Dov M. Gabbay. "Introduction and Overview." In Neural-Symbolic Learning Systems, 1–12. London: Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0211-3_1.
Повний текст джерелаd’Avila Garcez, Artur S., Krysia B. Broda, and Dov M. Gabbay. "Background." In Neural-Symbolic Learning Systems, 13–40. London: Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0211-3_2.
Повний текст джерелаd’Avila Garcez, Artur S., Krysia B. Broda, and Dov M. Gabbay. "Theory Refinement in Neural Networks." In Neural-Symbolic Learning Systems, 43–85. London: Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0211-3_3.
Повний текст джерелаd’Avila Garcez, Artur S., Krysia B. Broda, and Dov M. Gabbay. "Experiments on Theory Refinement." In Neural-Symbolic Learning Systems, 87–110. London: Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0211-3_4.
Повний текст джерелаd’Avila Garcez, Artur S., Krysia B. Broda, and Dov M. Gabbay. "Knowledge Extraction from Trained Networks." In Neural-Symbolic Learning Systems, 113–58. London: Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0211-3_5.
Повний текст джерелаd’Avila Garcez, Artur S., Krysia B. Broda, and Dov M. Gabbay. "Experiments on Knowledge Extraction." In Neural-Symbolic Learning Systems, 159–79. London: Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0211-3_6.
Повний текст джерелаd’Avila Garcez, Artur S., Krysia B. Broda, and Dov M. Gabbay. "Handling Inconsistencies in Neural Networks." In Neural-Symbolic Learning Systems, 183–208. London: Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0211-3_7.
Повний текст джерелаd’Avila Garcez, Artur S., Krysia B. Broda, and Dov M. Gabbay. "Experiments on Handling Inconsistencies." In Neural-Symbolic Learning Systems, 209–33. London: Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0211-3_8.
Повний текст джерелаd’Avila Garcez, Artur S., Krysia B. Broda, and Dov M. Gabbay. "Neural-Symbolic Integration: The Road Ahead." In Neural-Symbolic Learning Systems, 235–52. London: Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-0211-3_9.
Повний текст джерелаLee, Matthew K. O. "Neural Networks and Symbolic A.I." In Neurocomputing, 117–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-76153-9_14.
Повний текст джерелаТези доповідей конференцій з теми "Neural-symbolic"
Lamb, Luís C., Artur d’Avila Garcez, Marco Gori, Marcelo O. R. Prates, Pedro H. C. Avelar, and Moshe Y. Vardi. "Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/679.
Повний текст джерелаShiqi, Shen, Shweta Shinde, Soundarya Ramesh, Abhik Roychoudhury, and Prateek Saxena. "Neuro-Symbolic Execution: Augmenting Symbolic Execution with Neural Constraints." In Network and Distributed System Security Symposium. Reston, VA: Internet Society, 2019. http://dx.doi.org/10.14722/ndss.2019.23530.
Повний текст джерелаPerotti, Alan, Artur d'Avila Garcez, and Guido Boella. "Neural-symbolic monitoring and adaptation." In 2015 International Joint Conference on Neural Networks (IJCNN). IEEE, 2015. http://dx.doi.org/10.1109/ijcnn.2015.7280713.
Повний текст джерелаTran, Son N. "Compositional Neural Logic Programming." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/421.
Повний текст джерелаYang, Zhun, Adam Ishay, and Joohyung Lee. "NeurASP: Embracing Neural Networks into Answer Set Programming." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/243.
Повний текст джерелаConverse, Hayes, Antonio Filieri, Divya Gopinath, and Corina S. Pasareanu. "Probabilistic Symbolic Analysis of Neural Networks." In 2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE). IEEE, 2020. http://dx.doi.org/10.1109/issre5003.2020.00023.
Повний текст джерелаManhaeve, Robin, Giuseppe Marra, and Luc De Raedt. "Approximate Inference for Neural Probabilistic Logic Programming." In 18th International Conference on Principles of Knowledge Representation and Reasoning {KR-2021}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/kr.2021/45.
Повний текст джерелаAasted, Christopher M., Sunwook Lim, and Rahmat A. Shoureshi. "Vehicle Health Inferencing Using Feature-Based Neural-Symbolic Networks." In ASME 2013 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/dscc2013-3831.
Повний текст джерелаChrupała, Grzegorz, and Afra Alishahi. "Correlating Neural and Symbolic Representations of Language." In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/p19-1283.
Повний текст джерелаKumar, Arjun, and Tim Oates. "Connecting deep neural networks with symbolic knowledge." In 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, 2017. http://dx.doi.org/10.1109/ijcnn.2017.7966309.
Повний текст джерелаЗвіти організацій з теми "Neural-symbolic"
Gardner, Daniel. Symbolic Processor Based Models of Neural Networks. Fort Belvoir, VA: Defense Technical Information Center, May 1988. http://dx.doi.org/10.21236/ada200200.
Повний текст джерелаBurton, Robert M., and Jr. Topics in Stochastics, Symbolic Dynamics and Neural Networks. Fort Belvoir, VA: Defense Technical Information Center, December 1996. http://dx.doi.org/10.21236/ada336426.
Повний текст джерелаTang, Zibin. A new design approach for numeric-to-symbolic conversion using neural networks. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.6126.
Повний текст джерела