… deep learning architectures that utilize multimodal data are not properly designed to handle
such unexpected situations. Some deep … propose a novel deep learning architecture called “…

… 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 …

… We take a first step toward architectural support for robust deep learning. For a robustness
… This paper proposes Ptolemy, an algorithm-architecture co-design system that detects ad…

… Our goal is to find the more suitable deep-learning architecture for our task. Therefore, we …
“deep learning meta-architectures”. We combine each of these meta-architectures with “deep …

… Therefore, the robust deep learning architecture proposed in the current paper can be
trained end-to-end, and this architecture is very competitive for nuclei segmentation task. …

… deep learning model that automatically segments the complex nuclei present in histology
images by implementing an effective encoder–decoder architecture … Our deep learning model …

… -learning algorithm for reweighting training examples and training more robust deep learning
… Our method can be directly applied to any deep learning architecture and is expected to …

… of machine learning applications as the several deep learning architectures shown the
reliability in terms of speed as well as accuracy. In this paper, we have modeled a robust U-Net …

… discriminative deep learning architecture that combines concepts from unsupervised learning
for … This architecture, which we deem a Deep SpatioTemporal Inference Network, yields a …

… We apply this metric to understand the role of neural network architecture in adversarial
robustness. More specifically we are interested to find out whether the improved robustness is a …