Contextual learning in humans and machines

Outputs related to Contextual learning in humans and machines

Showing 27 outputs:

Book Chapter

Brosnan K, Gregson M & Duncan S (2020) Spaces. How are we creating environments for learning?. In: Reflective Teaching in Further, Adult and Vocational Education. 5th ed. Reflective Teaching. London: Bloomsbury, pp. 179-206. https://www.bloomsbury.com/uk/reflective-teaching-in-further-adult-and-vocational-education-9781350102002/


Conference Paper (published)

Swingler K & Bath M (2020) Learning Spatial Relations with a Standard Convolutional Neural Network. In: Merelo JJ, Garibaldi J, Wagner C, Bäck T, Madani K & Warwick K (eds.) Proceedings of the 12th International Joint Conference on Computational Intelligence - Volume 1: NCTA. 12th International Conference on Neural Computation Theory and Applications, Budapest, Hungary, 02.11.2020-04.11.2020. Setubal, Portugal: SCITEPRESS - Science and Technology Publications, pp. 464-470. https://doi.org/10.5220/0010170204640470


Book Chapter

Li S (2019) Bi/Multilingual Education, Translation, and Social Mobility in Xinjiang, China. In: Shei C, McLellan Zikpi ME & Chao D (eds.) Routledge Handbook of Chinese Language Teaching. Abingdon and New York: Routledge, pp. 593-612. https://www.routledge.com/The-Routledge-Handbook-of-Chinese-Language-Teaching/Shei-Zikpi-Chao/p/book/9781138097940


Research Report

Niker F, Himmelreich J, Feldman J & Currie M (2019) Coding Caring: Human Values for an Intimate AI. Stanford University's One Hundred Year Study on Artificial Intelligence (AI100). https://ai100.stanford.edu/sites/g/files/sbiybj18871/files/media/file/coding_caring_workshop_report_1000w_0.pdf


Conference Paper (published)

Abel A, Gao C, Smith L, Watt R & Hussain A (2018) Fast Lip Feature Extraction Using Psychologically Motivated Gabor Features. In: 2018 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE Symposium Series on Computational Intelligence, SSCI 2018, Bangalore, India, 18.11.2018-21.11.2018. Piscataway, NJ, USA: IEEE, pp. 1033-1040. https://doi.org/10.1109/SSCI.2018.8628931