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Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign Dropout
The vast majority of deep models use multiple gradient signals, typicall...
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Sparse representation for damage identification of structural systems
Identifying damage of structural systems is typically characterized as a...
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Taskology: Utilizing Task Relations at Scale
It has been recognized that the joint training of computer vision tasks ...
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Deep learning of physical laws from scarce data
Harnessing data to discover the underlying governing laws or equations t...
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DeepPerimeter: Indoor Boundary Estimation from Posed Monocular Sequences
We present DeepPerimeter, a deep learning based pipeline for inferring a...
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Decentralized Computation Offloading for Multi-User Mobile Edge Computing: A Deep Reinforcement Learning Approach
Mobile edge computing (MEC) emerges recently as a promising solution to ...
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Gradient Adversarial Training of Neural Networks
We propose gradient adversarial training, an auxiliary deep learning fra...
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A Stochastic Geometry Analysis of Energy Harvesting in Large Scale Wireless Networks
In this paper, the theoretical sustainable capacity of wireless networks...
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Beamforming Design for Max-Min Fair SWIPT in Green Cloud-RAN with Wireless Fronthaul
In this paper, a joint beamforming design for max-min fair simultaneous ...
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Estimating Depth from RGB and Sparse Sensing
We present a deep model that can accurately produce dense depth maps giv...
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A Hybrid Approach for Efficient Wireless Information and Power Transfer in Green C-RAN
In this paper, we consider a green cloud radio access network (C-RAN) wi...
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GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks
Deep multitask networks, in which one neural network produces multiple p...
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3-D Convolutional Neural Networks for Glioblastoma Segmentation
Convolutional Neural Networks (CNN) have emerged as powerful tools for l...
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