In this work, we propose solving the Information bottleneck (IB) and Pri...
In this work, we generalize the information bottleneck (IB) approach to ...
Accurate disease identification and its severity estimation is an import...
Deep Neural Networks (DNNs) which are trained end-to-end have been
succe...
Gradient-based adversarial attacks on deep neural networks pose a seriou...
Automatic modulation classification can be a core component for intellig...
In this paper, we propose a framework for predicting frame errors in the...
The Information bottleneck (IB) method enables optimizing over the trade...
Interference management has become a key factor in regulating transmissi...
Turbulent premixed flames are important for power generation using gas
t...
We present a MUSIC-based Direction of Arrival (DOA) estimation strategy ...
Subsampling of received wireless signals is important for relaxing hardw...
Recent studies have shown that deep learning models are vulnerable to
sp...
Gradient-based adversarial attacks on neural networks can be crafted in ...
We investigate the potential of training time reduction for deep learnin...
A set of about 80 researchers, practitioners, and federal agency program...
Coded caching has been shown to result in significant throughput gains, ...
The reliance on deep learning algorithms has grown significantly in rece...
We propose a computationally efficient wrapper feature selection method ...
We study the problem of interference source identification, through the ...
In this work, we study a large linear interference network with an equal...
Coded caching techniques have received significant attention lately due ...
In this work, we investigate the feasibility and effectiveness of employ...
Machine Learning models are vulnerable to adversarial attacks that rely ...
We study information theoretic models of interference networks that cons...
We present an example where a distributed coordinated protocol supported...
The problem of interference management is considered in the context of a...
In this work, we investigate the value of employing deep learning for th...