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.
With the development of deep learning techniques, the combination of deep learning with image compression has drawn lots of attention.
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
In this paper, we propose a novel image compression framework G-VAE (Gained Variational Autoencoder), which could achieve continuously variable rate in a single model.
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.