We consider the image transmission problem over a noisy wireless channel via deep learning-based joint source-channel coding (DeepJSCC) along with a denoising diffusion probabilistic model (DDPM) at the receiver.
The Blahut-Arimoto (BA) algorithm has played a fundamental role in the numerical computation of rate-distortion (RD) functions.
The trained model is then directly generalized to new unseen graphs for online CD without additional optimization, where a better trade-off between quality and efficiency can be achieved.
SIMT lays the foundation of pre-training with large-scale multi-task multi-domain datasets and is proved essential for stable training in our GPPF experiments.
In this letter, we propose a novel tensor-based modulation scheme for massive unsourced random access.
The superiority of our algorithm is proved by demonstrating the new state-of-the-art results on cross-domain federated classification and detection.
In order to improve the speed of B&B algorithms, learning techniques have been introduced in this algorithm recently.
IGP is also a generic framework that can capture the permutation invariant partitioning ground-truth of historical snapshots in the offline training and tackle the online GP on graphs with non-fixed number of nodes and clusters.
Our model can be divided into a series of subproblems, which only relate to the traffics in a certain individual time interval.
Optimization and Control
Accessing the data in the failed disk (degraded read) with low latency is crucial for an erasure-coded storage system.
Information Theory Information Theory
With the development of deep learning techniques, the combination of deep learning with image compression has drawn lots of attention.
Different from conventional techniques of temporal link prediction that ignore the potential non-linear characteristics and the informative link weights in the dynamic network, we introduce a novel non-linear model GCN-GAN to tackle the challenging temporal link prediction task of weighted dynamic networks.