We propose a joint source and channel coding (JSCC) technique for wireless image
transmission that does not rely on explicit codes for either compression or error correction; …

We consider wireless transmission of images in the presence of channel output feedback.
From a Shannon theoretic perspective feedback does not improve the asymptotic end-to …

We investigate joint source channel coding (JSCC) for wireless image transmission over
multipath fading channels. Inspired by recent works on deep learning based JSCC and …

For reliable transmission across a noisy communication channel, classical results from
information theory show that it is asymptotically optimal to separate out the source and …

Training deep neural networks on large datasets containing high-dimensional data requires
a large amount of computation. A solution to this problem is data-parallel distributed training …

We investigate how the population nonlinearities resulting from lateral inhibition and
thresholding in sparse coding networks influence neural response selectivity and …

We consider the design of low-delay joint source-channel coding (JSCC) schemes for the
transmission of discrete-time analog sources over noisy channels based on deep neural …

We introduce deep learning based communication methods for successive refinement of
images over wireless channels. We present three different strategies for progressive image …

The exponential growth of various complex images is putting tremendous pressure on
storage systems. Here, we propose a memristor-based storage system with an integrated …

We consider wireless transmission of images in the presence of channel output feedback, by
introducing an autoencoder-based deep joint source-channel coding (JSCC) scheme. We …