Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Layered Approximation for Deep Neural Networks


Affiliations
1 Head of Artificial Intelligence, Yes Bank, Mumbai, Maharashtra, India
     

   Subscribe/Renew Journal


Artificial Intelligence has created immense hype in the last decade and the credit for the same goes to the groundbreaking breakthrough named “Deep Learning” or “Deep Neural Network”. Although “Artificial Neural Network”, the foundation of Deep Learning as a concept has been prevalent since 1958 but the actual implementation for solving real business use cases have only been possible over the last decade. Deep Neural Networks has demonstrated significant results in the fields of computer vision, speech recognition, and machine translation, and outperformed human brain in many instances. Artificial Neural Network, as it is inspired from human biological neural superstructure has few structural similarities but not possible in terms of how the human brain or biological neural network works because we have still limited information on its functioning. Nevertheless, Deep Neural Network paved the way for many possibilities and currently it is the most promising technology that we have in the field of Artificial Intelligence.

Keywords

Deep Neural Network, Artificial Intelligence, Artificial Neural Network, Layered Approximation.
Subscription Login to verify subscription
User
Notifications
Font Size


  • Huang, G. B., Chen, L., & Siew, C.-K. (2006). Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans. Neural Networks, 17(4), 879-892. Retrieved from http://www.ntu.edu.sg/home/egbhuang/pdf/I-ELM.pdf
  • Browniee, J. (2019, January 2). Impact of dataset size on deep learning model skill and performance estimates. better deep learning. Retrieved from https://machinelearningmastery.com/impact-of-dataset-size-on-deep-learning-model-skill-and-performance-estimates/
  • LeCun, Y., Chopra, S., Hadsell, R., Ranzato, M., & Huang, F. J. (2006). A tutorial on energy-based learning. In G. Bakir et al., Predicting Structured Data. MIT Press. Retrieved from http://yann.lecun.com/exdb/publis/
  • Nielsen, M. A. (2015). Neural networks and deep learning. Determination Press. Retrieved from http://neuralnetworksanddeeplearning.com/chap4.html
  • Sara, S., Nicholas, F., & Hinton, G. E. (2017). Dynamic routing between capsules. 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA. Retrieved from https://arxiv.org/pdf/1710.09829.pdf
  • We Know Very Little About Human Brain. (2018, August 19). Retrieved from https://mc.ai/we-know-very-little-about-human-brain-artificial-neural-network-vs/
  • Zhou, Z. H., & Feng, J. (2017). Deep forest: Towards an alternative to deep neural networks. Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17). Retrieved from https://arxiv.org/abs/1702.08835

Abstract Views: 29

PDF Views: 0




  • Layered Approximation for Deep Neural Networks

Abstract Views: 29  |  PDF Views: 0

Authors

Utpal Chakraborty
Head of Artificial Intelligence, Yes Bank, Mumbai, Maharashtra, India

Abstract


Artificial Intelligence has created immense hype in the last decade and the credit for the same goes to the groundbreaking breakthrough named “Deep Learning” or “Deep Neural Network”. Although “Artificial Neural Network”, the foundation of Deep Learning as a concept has been prevalent since 1958 but the actual implementation for solving real business use cases have only been possible over the last decade. Deep Neural Networks has demonstrated significant results in the fields of computer vision, speech recognition, and machine translation, and outperformed human brain in many instances. Artificial Neural Network, as it is inspired from human biological neural superstructure has few structural similarities but not possible in terms of how the human brain or biological neural network works because we have still limited information on its functioning. Nevertheless, Deep Neural Network paved the way for many possibilities and currently it is the most promising technology that we have in the field of Artificial Intelligence.

Keywords


Deep Neural Network, Artificial Intelligence, Artificial Neural Network, Layered Approximation.

References