TY - GEN
T1 - An Adaptive Transcursive Algorithm for Depth Estimation in Deep Learning Networks
AU - Thikshaja, Uthra Kunathur
AU - Paul, Anand
AU - Rho, Seungmin
AU - Bhattacharjee, Deblina
PY - 2016/4/21
Y1 - 2016/4/21
N2 - Estimation of depth in a Neural Network (NN) or Artificial Neural Network (ANN) is an integral as well as complicated process. In this article, we propose a way of using the transformation of functions combined with recursive nature to have an adaptive, transcursive algorithm to represent the backpropagation concept used in deep learning for a Multilayer Perceptron Network. Each function can be used to represent a hidden layer used in the neural network and they can be made to handle a complex part of the processing. Whenever an undesirable output occurs, we transform (modify) the functions until a desirable output is obtained. We have an algorithm that uses the transcursive model to create an interpretation of the concept of deep learning using multilayer perceptron network (MPN).
AB - Estimation of depth in a Neural Network (NN) or Artificial Neural Network (ANN) is an integral as well as complicated process. In this article, we propose a way of using the transformation of functions combined with recursive nature to have an adaptive, transcursive algorithm to represent the backpropagation concept used in deep learning for a Multilayer Perceptron Network. Each function can be used to represent a hidden layer used in the neural network and they can be made to handle a complex part of the processing. Whenever an undesirable output occurs, we transform (modify) the functions until a desirable output is obtained. We have an algorithm that uses the transcursive model to create an interpretation of the concept of deep learning using multilayer perceptron network (MPN).
U2 - 10.1109/platcon.2016.7456783
DO - 10.1109/platcon.2016.7456783
M3 - Chapter in a published conference proceeding
BT - 2016 International Conference on Platform Technology and Service (PlatCon)
PB - IEEE
ER -