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AI Bibliography

AI Bibliography

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Arabshahi, F., Lee, J., Gawarecki, M., Mazaitis, K., Azaria, A., & Mitchell, T. (2020). Conversational neuro-symbolic commonsense reasoning. arXiv preprint arXiv:2006.10022.  
11/26/20, 2:54 AM
Brown, L. (2019). Augmented reality in international pilot training to meet training demands. Univ of Michigan.  
10/15/20, 5:33 PM
Du, K. L., & Swamy, M. N. S. (2019). Neural networks and statistical learning. Springer London.  
7/9/20, 10:26 AM
Friston, K. J., Lin, M., Frith, C. D., Pezzulo, G., Hobson, J. A., & Ondobaka, S. (2017). Active inference, curiosity and insight. Neural computation, 29(10), 2633–2683.  
1/3/23, 10:41 AM
Gottlieb, J., & Oudeyer, P.-Y. (2018). Towards a neuroscience of active sampling and curiosity. Nature Reviews Neuroscience, 19(12), 758–770.  
1/3/23, 10:24 AM
Habib, N. (2019). Hands-on q-learning with python: Practical q-learning with openai gym, keras, and tensorflow. Packt Publishing.  
7/9/20, 10:32 AM
Hammerstrom, D. 2015, Darpa neurocomputing. Paper presented at 2015 IEEE International Electron Devices Meeting (IEDM).  
2/4/21, 10:34 AM
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.  
7/9/20, 10:13 AM
Jin, C., Allen-Zhu, Z., Bubeck, S., & Jordan, M. I. 2018, Is q-learning provably efficient?. Paper presented at Advances in Neural Information Processing Systems.  
7/9/20, 10:20 AM
Kauvar, I., Doyle, C., Zhou, L., & Haber, N. (2023). Curious replay for model-based adaptation. International Conference on Machine Learning.  
7/24/23, 12:46 PM
Lecun, Y. (2022). A path towards autonomous machine intelligence version 0.9. 2, 2022-06-27. Open Review, 62.  
1/18/23, 12:42 PM
Li, H. (2018). Deep learning for natural language processing: Advantages and challenges. National Science Review, 5(1), 24–26.  
4/6/23, 2:59 PM
Nguyen, T. N., McDonald, C., & Gonzalez, C. (2023). Credit assignment: Challenges and opportunities in developing human-like ai agents. arXiv preprint arXiv:2307.08171.  
1/10/24, 4:30 PM
Oudeyer, P.-Y. (2018). Computational theories of curiosity-driven learning. arXiv preprint arXiv:1802.10546.  
1/3/23, 10:17 AM
Parsons, S. D., Gymtrasiewicz, P., & Wooldridge, M. (2012). Game theory and decision theory in agent-based systems. Springer US.  
7/9/20, 10:46 AM
Pennington, E., Hafer, R., Nistler, E., Seech, T., & Tossell, C. 2019, Integration of advanced technology in initial flight training. Paper presented at 2019 Systems and Information Engineering Design Symposium (SIEDS).  
10/15/20, 5:21 PM
Schwartz, H. M. (2014). Multi-agent machine learning: A reinforcement approach. Wiley.  
7/9/20, 10:52 AM
Sun, C., Qian, H., & Miao, C. (2022). From psychological curiosity to artificial curiosity: Curiosity-driven learning in artificial intelligence tasks. arXiv preprint arXiv:2201.08300.  
1/3/23, 10:12 AM
Van Hasselt, H., Guez, A., & Silver, D. 2016, Deep reinforcement learning with double q-learning. Paper presented at Thirtieth AAAI conference on artificial intelligence.  
7/9/20, 10:07 AM
Wang, Y.-X., Ramanan, D., & Hebert, M. (2017). Learning to model the tail. Advances in Neural Information Processing Systems, 30.  
3/30/23, 2:12 PM
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WIKINDX 6.7.0 | Total resources: 1621 | Username: -- | Bibliography: WIKINDX Master Bibliography | Style: American Psychological Association (APA)