*NOTE: These videos were recorded in Fall 2015 to update the Neural Nets portion of the class. MIT 6.034 Artificial Intelligence, Fall 2010 View the complete...
We introduce the Generative Query Network (GQN), a framework within which machines learn to perceive their surroundings by training only on data obtained by themselves as they move around scenes. Much like infants and animals, the GQN learns by trying to make sense of its observations of the world around it. In doing so, the GQN learns about plausible scenes and their geometrical properties, without any human labelling of the contents of scenes.
Y. Kim, K. Stratos, and D. Kim. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), page 643--653. Vancouver, Canada, Association for Computational Linguistics, (July 2017)
Z. Yang, D. Yang, C. Dyer, X. He, A. Smola, and E. Hovy. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, page 1480--1489. San Diego, California, Association for Computational Linguistics, (June 2016)
S. Sapkota, A. Shakya, and B. Joshi. Proceedings of the 26th International Conference of the ORIENTAL- COCOSDA (O-COCOSDA 2023), page 1-6. IEEE, (December 2023)
D. Lee, S. Yu, and H. Yu. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, page 1362–1370. New York, NY, USA, Association for Computing Machinery, (2020)