Deep Learning Theory

… under construction

  • Bibliography:
  1. Backpropagation applied to handwritten zip code recognition, LeCun et al. (1989)
  2. Gradient-based learningapplied to document recognition, LeCun et al. (1998)
  3. Large scale distributed deep networks, Dean et al. (2012)
  4. Improving neural networks by preventing co-adaptation of feature detectors, Hinton et al. (2012)
  5. Imagenet classification with deep convolutional neural networks, Krizhensky et al. (2012)
  6. Visualizing and Understanding Convolutional Networks, Zeiler M. D., and Fergus R. (2013)
  7. Some improvements on deep convolutional neural network based image classification, Howard A. G. (2013)
  8. Network in network, Lin et al. (2013)
  9. Overfeat: Integrated recognition, localization and detection using convolutional networks, Sermanet et al. (2013)
  10. On the importanceof initialization and momentum in deep learning, Sutskever et al. (2013)
  11. Human pose estimation via deep neuralnetworks, Toshev A., and Szegedy C. (2013)

  12. Going Deeper with Convolutions, Szegedy et al. (Google) (2014)

  13. Scaling up matrix computations on shared-memorymanycore systems with 1000 cpu cores, Song F., and Dongarra J. (2014)
  14. Very Deep Convolutional Networks for Large_scale Image Recognition, Simonyan K., and Zisserman A. (2015)

  15. Deep Residual Learning for Image Recognition, He et al. (2015)

  16. Deep Compession: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding, Han et al. (2016)