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.
*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...
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In this project, we provide our implementations of CNN [Zeng et al., 2014] and PCNN [Zeng et al.,2015] and their extended version with sentence-level attention scheme [Lin et al., 2016] .
What are Convolutional Neural Networks and why are they important? Convolutional Neural Networks (ConvNets or CNNs) are a category of Neural Networks that have proven very effective in areas such as image recognition and classification. ConvNets have been successful in identifying faces, objects and traffic signs apart from powering vision in robots and self driving cars. Figure 1:…
M. Beyer, S. Gesper, A. Guntoro, G. Paya-Vaya, and H. Blume. Proceedings - 2023 IEEE 34th International Conference on Application-Specific Systems, Architectures and Processors, ASAP 2023, page 61--68. United States, Institute of Electrical and Electronics Engineers Inc., (2023)Funding Information: This work is supported by the German federal ministry of education and research (BMBF), project ZuSE-KI-AVF (grant no. 16ME0062).; 34th IEEE International Conference on Application-Specific Systems, Architectures and Processors, ASAP 2023 ; Conference date: 19-07-2023 Through 21-07-2023.
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)