TensorFlow™ is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.
Torch is a scientific computing framework with wide support for machine learning algorithms. It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.
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:…
Engineer friends often ask me: Graph Deep Learning sounds great, but are there any big commercial success stories? Is it being deployed in practical applications? Besides the obvious ones–recommendation systems at Pinterest, Alibaba and Twitter–a slightly nuanced success story is the Transformer architecture, which has taken the NLP industry by storm. Through this post, I want to establish links between Graph Neural Networks (GNNs) and Transformers. I’ll talk about the intuitions behind model architectures in the NLP and GNN communities, make connections using equations and figures, and discuss how we could work together to drive progress.
COBOSLAB: Cognitive Bodyspaces: Learning and Behavior:
Laboratory that investigates and models the Self-organized Learning of and Behavior within Integrated Multimodal Multimodular Bodyspace Representations.
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M. Akbarzadeh-T., E. Tunstel, K. Kumbla, and M. Jamshidi. Proceedings of the 1998 IEEE World Congress on
Computational Intelligence, 2, page 1200--1205. Anchorage, Alaska, USA, IEEE Press, (5-9 May 1998)
M. Dacrema, P. Cremonesi, and D. Jannach. (2019)cite arxiv:1907.06902Comment: Source code available at: https://github.com/MaurizioFD/RecSys2019_DeepLearning_Evaluation.
M. Fukumi, S. Omatu, and Y. Nishikawa. Proceedings of the IEEE International joint Conference on Neural
Networks 1995, IJCNN'95, 4, page 1834--1838. IEEE Computer Society, (November 1995)
X. He, L. Liao, H. Zhang, L. Nie, X. Hu, and T. Chua. Proceedings of the 26th International Conference on World Wide Web, page 173–182. Republic and Canton of Geneva, CHE, International World Wide Web Conferences Steering Committee, (2017)
H. Hoffmann, and R. Möller. Artificial Neural Networks and Neural Information Processing---ICANN/ICONIP
2003, LNCS 2714, page 463-470. Springer, Berlin, (2003)
A. Hyvärinen, and E. Oja. Neural Networks: The Official Journal of the
International Neural Network Society, 13 (4-5):
411--430(June 2000)PMID: 10946390.
A. Khotanzad, and J. Lu. Computer Vision and Pattern Recognition, 1989. Proceedings CVPR '89.,
IEEE Computer Society Conference on, page 200--205. IEEE Computer Society, (1989)
Q. Le, and T. Mikolov. Proceedings of the 31st International Conference on Machine Learning, volume 32 of Proceedings of Machine Learning Research, page 1188--1196. Bejing, China, PMLR, (June 2014)
J. Lin, R. Nogueira, and A. Yates. (2020)cite arxiv:2010.06467Comment: Final preproduction version of volume in Synthesis Lectures on Human Language Technologies by Morgan & Claypool.
G. Neto. Universidade Federal do Maranhão (UFMA), Programa de Pós-Graduação em Ciência da Computação/CCET, Dissertation, (Jul 26, 2018)Departamento de Informática/CCET.