… To this end, this study proposes a robust deep learning architecture based on a stacked …
The proposed deep learning architecture has two consequent components: a deep learning …

… Deep learning is a subtype of artificial intelligence that constructs generalizable models for
… A common deep learning architecture used for classification of visual information is known as …

… -robust deep learning setting, it is common practice to train for as long as possible to minimize
the training loss, as modern convergence curves for deep learning … architecture is, robust …

… Abstract—Recent advances in Deep Learning (DL) have enabled a paradigm shift to … we
propose to develop architectural support for hardware implementation of secure and robust DL. …

… This study uses three different deep learning models, namely, Multi-layer Perceptron (MLP), …
variations in architectural configurations. This study investigates the robust deep neural …

… robust against adversarial attacks [49], the target of the researchers is to introduce in their
models’ layers of robustness … for system architects, mainly making them robust to the presence …

… of the deep neural network architecture, in the design of deep learning optimization methods
… In this work, we set the deep neural network architecture to the 3D U-Net [9] used in nnUNet …

… Network Architecture: For the reconstruction network f, we follow the architecture of deep
convolutional … We trained the architecture shown in fig. 1 using the objective defined in (6). …

… robustness of deep neural networks from the architectural point of view. However, searching
for architectures of deep … a multiobjective architecture search for adversarial robustness with …

… [4], we argue that a robust defense should meet the following two design objectives
simultaneously: (1) It should provide a uniform defense architecture that can generalize over both …