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Found 11 papers, 5 papers with code
Parallel and Limited Data Voice Conversion Using Stochastic Variational Deep Kernel Learning
On the other hand, statistical methods are effective with limited data but have difficulties in modelling complex mapping functions.
Fast Classification with Sequential Feature Selection in Test Phase
During the test phase, the proposed approach utilizes Fisher scores for feature ranking to identify the most important feature at each step.
Progressive Transmission using Recurrent Neural Networks
The transmitter aims to send the data to the receiver as fast as possible and with as few channel uses as possible (as channel conditions permit) while the receiver refines its estimate after each channel use.
Sleep Stage Scoring Using Joint Frequency-Temporal and Unsupervised Features
Detection of such sleep disorders is usually possible by analyzing a number of vital signals that have been collected from the patients.
Wi2Vi: Generating Video Frames from WiFi CSI Samples
Objects in an environment affect electromagnetic waves.
SensorDrop: A Reinforcement Learning Framework for Communication Overhead Reduction on the Edge
As such decisions are application dependent and may change over time, they should be learned during the operation of the system, for that we propose a method based on Advantage Actor-Critic (A2C) reinforcement learning which gradually learns which sensor's data is cost-effective to be sent to the central node.
Propagation Channel Modeling by Deep learning Techniques
This paper presents a novel propagation channel model which considers the time-frequency response of the channel as an image.
Deep Feature Selection using a Teacher-Student Network
In this paper, we present a novel teacher-student feature selection (TSFS) method in which a 'teacher' (a deep neural network or a complicated dimension reduction method) is first employed to learn the best representation of data in low dimension.
Deep UL2DL: Channel Knowledge Transfer from Uplink to Downlink
As such, we extracted the environment information from the UL channel response to a latent domain and then transferred the derived environment information from the latent domain to predict the DL channel.
DeepPos: Deep Supervised Autoencoder Network for CSI Based Indoor Localization
Unlike the previous fingerprinting approaches, in this work, we do not store the {CSI/RSS} of fingerprints and instead we model the environment only with a single SAE.
Deep Learning-Based Channel Estimation
This scheme considers the pilot values, altogether, as a low-resolution image and uses an SR network cascaded with a denoising IR network to estimate the channel.
