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Found 14 papers, 1 papers with code
Enabling Resource-efficient AIoT System with Cross-level Optimization: A survey
Accordingly, the accuracy and responsiveness of DL models are bounded by resource availability.
Channel Estimation for Underwater Visible Light Communication: A Sparse Learning Perspective
The underwater propagation environment for visible light signals is affected by complex factors such as absorption, shadowing, and reflection, making it very challengeable to achieve effective underwater visible light communication (UVLC) channel estimation.
Privacy-Preserving Cooperative Visible Light Positioning for Nonstationary Environment: A Federated Learning Perspective
To improve the positioning accuracy and generalization capability in nonstationary environments, a cooperative VLP scheme based on federated learning (FL) is proposed in this paper.
Power and Interference Control for VLC-Based UDN: A Reinforcement Learning Approach
An RL-based algorithm is proposed to dynamically optimize the policy of power and interference control, maximizing the system utility in the complicated and dynamic environment.
Sparsity-Aware Intelligent Massive Random Access Control in Open RAN: A Reinforcement Learning Based Approach
Massive random access of devices in the emerging Open Radio Access Network (O-RAN) brings great challenge to the access control and management.
Multi-Target Cooperative Visible Light Positioning: A Compressed Sensing Based Framework
Specifically, a CS-based framework is formulated exploiting the superposition of the received visible light signals at the multiple targets to be located via intertarget cooperation.
AdaEnlight: Energy-aware Low-light Video Stream Enhancement on Mobile Devices
We report extensive experiments on diverse datasets, scenarios, and platforms and demonstrate the superiority of AdaEnlight compared with state-of-the-art low-light image and video enhancement solutions.
TL-SDD: A Transfer Learning-Based Method for Surface Defect Detection with Few Samples
To solve the imbalanced distribution problem, in this paper we propose TL-SDD: a novel Transfer Learning-based method for Surface Defect Detection.
Bi-Temporal Semantic Reasoning for the Semantic Change Detection in HR Remote Sensing Images
Recent studies indicate that the SCD can be modeled through a triple-branch Convolutional Neural Network (CNN), which contains two temporal branches and a change branch.
AdaSpring: Context-adaptive and Runtime-evolutionary Deep Model Compression for Mobile Applications
There are many deep learning (e. g., DNN) powered mobile and wearable applications today continuously and unobtrusively sensing the ambient surroundings to enhance all aspects of human lives.
AdaDeep: A Usage-Driven, Automated Deep Model Compression Framework for Enabling Ubiquitous Intelligent Mobiles
Recent breakthroughs in Deep Neural Networks (DNNs) have fueled a tremendously growing demand for bringing DNN-powered intelligence into mobile platforms.
Privacy Adversarial Network: Representation Learning for Mobile Data Privacy
This work departs from prior works in methodology: we leverage adversarial learning to a better balance between privacy and utility.
Towards Diverse Paraphrase Generation Using Multi-Class Wasserstein GAN
We propose a multi-class extension to the Wasserstein GAN, which allows our generative model to learn from both positive and negative samples.
Better accuracy with quantified privacy: representations learned via reconstructive adversarial network
This work represents a methodical departure from prior works: we balance between a measure of privacy and another of utility by leveraging adversarial learning to find a sweeter tradeoff.
