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Electrical and Computer Engineering
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Enhanced Supervised Descent Learning Technique For Electromagnetic Inverse Scattering Problems By The Deep Convolutional Neural Networks, He Ming Yao, Rui Guo, Maokun Li, Lijun Jiang, Michael Kwok Po Ng
Enhanced Supervised Descent Learning Technique For Electromagnetic Inverse Scattering Problems By The Deep Convolutional Neural Networks, He Ming Yao, Rui Guo, Maokun Li, Lijun Jiang, Michael Kwok Po Ng
Electrical and Computer Engineering Faculty Research & Creative Works
This work proposes a novel deep learning (DL) framework to solve the electromagnetic inverse scattering (EMIS) problems. The proposed framework integrates the complex-valued deep convolutional neural network (DConvNet) into the supervised descent method (SDM) to realize both off-line training and on-line 'imaging' prediction for EMIS. The offline training consists of two parts: 1) DConvNet training: the training dataset is created, and the proposed DConvNet is trained to realize the EM forward process and 2) SDM training: the trained DConvNet is integrated into the SDM framework, and the average descent directions between the initial prediction and the true label of SDM …
Heuristic-Based Automatic Pruning Of Deep Neural Networks, Tejalal Choudhary, Vipul Mishra, Anurag Goswami, Jagannathan Sarangapani
Heuristic-Based Automatic Pruning Of Deep Neural Networks, Tejalal Choudhary, Vipul Mishra, Anurag Goswami, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
The performance of a deep neural network (deep NN) is dependent upon a significant number of weight parameters that need to be trained which is a computational bottleneck. The growing trend of deeper architectures poses a restriction on the training and inference scheme on resource-constrained devices. Pruning is an important method for removing the deep NN's unimportant parameters and making their deployment easier on resource-constrained devices for practical applications. In this paper, we proposed a heuristics-based novel filter pruning method to automatically identify and prune the unimportant filters and make the inference process faster on devices with limited resource availability. …