We present a novel framework for learning system design based on neural
...
Recurrent neural networks (RNNs) are known to be universal approximators...
We study the problem of estimating the joint probability mass function (...
A deep autoencoder (DAE)-based end-to-end communication over the two-use...
We study kernel methods in machine learning from the perspective of feat...
The Expectation Maximization (EM) algorithm is widely used as an iterati...
One of the major open challenges in MIMO-OFDM receive processing is how ...
In this paper, we introduce a structure-based neural network architectur...
In this paper, we explore neural network-based strategies for performing...
In this paper, we investigate a neural network-based learning approach
t...
We consider the problem of identifying universal low-dimensional feature...
In this paper, we propose an information-theoretic approach to design th...
It is commonly believed that the hidden layers of deep neural networks (...
One primary focus in multimodal feature extraction is to find the
repres...
In this paper, we present a local information theoretic approach to
expl...