Geodesic Convolutional Neural Network for 3D Deep-Learning based Surrogate Modeling and Optimization

– Convolution layers & Pooling layers + global architecture – Training algorithm + Dropout Regularization.. • Useful

In the first part, a series of experiments were carried out to use the Vgg transfer learning features to evaluate the performance of the proposed unimodal biometric system using

 pushing forward to weakly supervised learning the problem Medical Image Segmentation of Thoracic Organs at Risk in CT-images..

This thesis studies empirical properties of deep convolutional neural net- works, and in particular the Scattering Transform.. Indeed, the theoretical anal- ysis of the latter is

For each phase in the process model, we discuss its objectives to be achieved and the methods and metrics (M&M-s) that can be used to ensure higher trustworthiness..

As seen in figure 6, we reduced the training time, and the difference of time reduced became higher when we increased the number of iteration for each ap- proach, in which we

How to construct Deep Recurrent Neural Networks Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, Yoshua Bengio.. International Conference on Learning

Inspired by Random Neural Networks, we introduce Random Neural Layer (RNL), which is compatible with other kinds of neural layers in Deep Neural Networks (DNN). The