AI Strategy and Concepts Bibliography

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Displaying 1 - 20 of 35 (Bibliography: WIKINDX Master Bibliography)
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Aggarwal, A., Chauhan, A., Kumar, D., Mittal, M., & Verma, S. (2020). Classification of fake news by fine-tuning deep bidirectional transformers based language model. EAI Endorsed Transactions on Scalable Information Systems Online First; EAI: Ghent, Belgium,  
Last edited by: SijanLibrarian 2020-10-28 13:54:29 Pop. 0%
Allen, C., Balavzevi'c, I., & Hospedales, T. (2020). A probabilistic framework for discriminative and neuro-symbolic semi-supervised learning. arXiv preprint arXiv:2006.05896,  
Last edited by: SijanLibrarian 2020-11-26 03:01:45 Pop. 0%
Balakrishnan, A. (2021). Productizing an artificial intelligence solution for intelligent detail extraction—synergy of symbolic and sub-symbolic artificial intelligence techniques. Trends of Data Science and Applications: Theory and Practices, 954, 23.  
Last edited by: SijanLibrarian 2022-01-24 12:39:04 Pop. 0%
Barnden, J. A., & Lee, M. G. (2001). Metaphor and artificial intelligence. Lawrence Erlbaum Associates.  
Last edited by: SijanLibrarian 2021-09-20 12:59:05 Pop. 0%
Bender, E. A. (1996). Mathematical methods in artificial intelligence. IEEE Computer Society Press,  
Last edited by: SijanLibrarian 2022-01-25 10:26:48 Pop. 0%
Bentley, M. J. (2017). Enabling air force satellite ground system automation through software engineering AIR FORCE INSTITUTE OF TECHNOLOGY WRIGHT-PATTERSON AFB OH WRIGHT-PATTERSON~….  
Last edited by: SijanLibrarian 2022-07-08 09:46:34 Pop. 0%
Chen, B., Wu, H., Mo, W., Chattopadhyay, I., & Lipson, H. 2018, Autostacker: A compositional evolutionary learning system. Paper presented at Proceedings of the Genetic and Evolutionary Computation Conference.  
Last edited by: SijanLibrarian 2020-11-26 02:24:50 Pop. 0%
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805,  
Last edited by: SijanLibrarian 2020-10-28 16:47:50 Pop. 0%
Du, K. L., & Swamy, M. N. S. (2019). Neural networks and statistical learning. Springer London.  
Last edited by: SijanLibrarian 2020-07-09 10:26:37 Pop. 0%
Fugate, S., & Ferguson-Walter, K. (2019). Artificial intelligence and game theory models for defending critical networks with cyber deception. AI Magazine, 40(1), 49–62.  
Last edited by: SijanLibrarian 2020-10-28 13:50:29 Pop. 0%
Habib, N. (2019). Hands-on q-learning with python: Practical q-learning with openai gym, keras, and tensorflow. Packt Publishing.  
Last edited by: SijanLibrarian 2020-07-09 10:32:23 Pop. 0%
Heilmeier, A., Thomaser, A., Graf, M., & Betz, J. (2020). Virtual strategy engineer: Using artificial neural networks for making race strategy decisions in circuit motorsport. Applied Sciences, 10(21), 7805.  
Last edited by: SijanLibrarian 2021-03-29 16:44:52 Pop. 0%
Hester, T., Vecerik, M., Pietquin, O., Lanctot, M., Schaul, T., & Piot, B., et al. 2018, Deep q-learning from demonstrations. Paper presented at Thirty-Second AAAI Conference on Artificial Intelligence.  
Last edited by: SijanLibrarian 2020-07-09 10:13:08 Pop. 0%
Inala, J. P., Yang, Y., Paulos, J., Pu, Y., Bastani, O., & Kumar, V., et al. (2020). Neurosymbolic transformers for multi-agent communication. Advances in Neural Information Processing Systems, 33,  
Last edited by: SijanLibrarian 2020-11-26 02:44:56 Pop. 0%
Jiang, J., & Ahn, S. (2020). Generative neurosymbolic machines. Advances in Neural Information Processing Systems, 33,  
Last edited by: SijanLibrarian 2020-11-26 02:41:14 Pop. 0%
Lake, B. M., Salakhutdinov, R. R., & Tenenbaum, J. 2013, One-shot learning by inverting a compositional causal process. Paper presented at Advances in Neural Information Processing Systems.  
Last edited by: SijanLibrarian 2020-11-26 02:28:39 Pop. 0%
Madni, A. M., & Madni, C. C. (2018). Architectural framework for exploring adaptive human-machine teaming options in simulated dynamic environments. Systems, 6(4), 44.  
Last edited by: SijanLibrarian 2021-05-18 15:36:49 Pop. 0%
Marra, G., Dumanvci'c, S., Manhaeve, R., & De Raedt, L. (2021). From statistical relational to neural symbolic artificial intelligence: A survey. arXiv preprint arXiv:2108.11451,  
Last edited by: SijanLibrarian 2022-01-24 12:55:35 Pop. 0%
McWhorter, T., Morys, M., Severyn, S., Stevens, S., Chan, L., & Cheng, C.-H. (2021). Machine learning aided electronic warfare system. IEEE Access, 9, 94691–94699.  
Last edited by: SijanLibrarian 2021-11-08 16:24:55 Pop. 0%
Mehdiyev, N., & Fettke, P. (2021). Explainable artificial intelligence for process mining: A general overview and application of a novel local explanation approach for predictive process monitoring. Interpretable Artificial Intelligence: A Perspective of Granular Computing, 1–28.  
Last edited by: SijanLibrarian 2022-07-07 15:30:32 Pop. 0%
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wikindx 6.2.2 ©2003-2020 | Total resources: 1411 | Username: -- | Bibliography: WIKINDX Master Bibliography | Style: American Psychological Association (APA) | Database queries: 23 | DB execution: 0.01217 secs | Script execution: 0.02550 secs