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Electrical and Computer Engineering
Electrical and Computer Engineering Faculty Publications and Presentations
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Full-Text Articles in Engineering
Distributed Deep Learning Optimization Of Heat Equation Inverse Problem Solvers, Zhuowei Wang, Le Yang, Haoran Lin, Genping Zhao, Zixuan Liu, Xiaoyu Song
Distributed Deep Learning Optimization Of Heat Equation Inverse Problem Solvers, Zhuowei Wang, Le Yang, Haoran Lin, Genping Zhao, Zixuan Liu, Xiaoyu Song
Electrical and Computer Engineering Faculty Publications and Presentations
The inversion problem of partial differential equation plays a crucial role in cyber-physical systems applications. This paper presents a novel deep learning optimization approach to constructing a solver of heat equation inversion. To improve the computational efficiency in large-scale industrial applications, data and model parallelisms are incorporated on a platform of multiple GPUs. The advanced Ring-AllReduce architecture is harnessed to achieve an acceleration ratio of 3.46. Then a new multi-GPUs distributed optimization method GradReduce is proposed based on Ring-AllReduce architecture. This method optimizes the original data communication mechanism based on mechanical time and frequency by introducing the gradient transmission scheme …
Robust Explainability: A Tutorial On Gradient-Based Attribution Methods For Deep Neural Networks, Ian E. Nielsen, Dimah Dera, Ghulam Rasool, Nidhal Bouaynaya, Ravi P. Ramachandran
Robust Explainability: A Tutorial On Gradient-Based Attribution Methods For Deep Neural Networks, Ian E. Nielsen, Dimah Dera, Ghulam Rasool, Nidhal Bouaynaya, Ravi P. Ramachandran
Electrical and Computer Engineering Faculty Publications and Presentations
With the rise of deep neural networks, the challenge of explaining the predictions of these networks has become increasingly recognized. While many methods for explaining the decisions of deep neural networks exist, there is currently no consensus on how to evaluate them. On the other hand, robustness is a popular topic for deep learning research; however, it is hardly talked about in explainability until very recently. In this tutorial paper, we start by presenting gradient-based interpretability methods. These techniques use gradient signals to assign the burden of the decision on the input features. Later, we discuss how gradient-based methods can …
A Self Controlled Rdp Approach For Feature Extraction In Online Handwriting Recognition Using Deep Learning, Sukhdeep Singh, Vinod Kumar Chauhan, Elisa H. Barney Smith
A Self Controlled Rdp Approach For Feature Extraction In Online Handwriting Recognition Using Deep Learning, Sukhdeep Singh, Vinod Kumar Chauhan, Elisa H. Barney Smith
Electrical and Computer Engineering Faculty Publications and Presentations
The identification of accurate features is the initial task for benchmarked handwriting recognition. For handwriting recognition, the objective of feature computation is to find those characteristics of a handwritten stroke that depict the class of a stroke and make it separable from the rest of the stroke classes. The present study proposes a feature extraction technique for online handwritten strokes based on a self controlled Ramer-Douglas-Peucker (RDP) algorithm. This novel approach prepares a smaller length feature vector for different shaped online handwritten strokes without preprocessing and without any control parameter to RDP. Thus, it also overcomes the shortcomings of the …