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Found 6 papers, 3 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.
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 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.
