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Found 10 papers, 1 papers with code
A Tensor-BTD-based Modulation for Massive Unsourced Random Access
In this letter, we propose a novel tensor-based modulation scheme for massive unsourced random access.
PartialFed: Cross-Domain Personalized Federated Learning via Partial Initialization
The superiority of our algorithm is proved by demonstrating the new state-of-the-art results on cross-domain federated classification and detection.
Harmonic Retrieval with $L_1$-Tucker Tensor Decomposition
We also develop a novel subcarrier recovery method for the proposed model.
Branch and Bound in Mixed Integer Linear Programming Problems: A Survey of Techniques and Trends
In order to improve the speed of B&B algorithms, learning techniques have been introduced in this algorithm recently.
Trading Quality for Efficiency of Graph Partitioning: An Inductive Method across Graphs
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.
Asymmetric Gained Deep Image Compression With Continuous Rate Adaptation
With the development of deep learning techniques, the combination of deep learning with image compression has drawn lots of attention.
A Convergent Semi-Proximal Alternating Direction Method of Multipliers for Recovering Internet Traffics from Link Measurements
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
Lower Bound on the Optimal Access Bandwidth of ($K+2,K,2$)-MDS Array Code with Degraded Read Friendly
Accessing the data in the failed disk (degraded read) with low latency is crucial for an erasure-coded storage system.
Information Theory Information Theory
G-VAE: A Continuously Variable Rate Deep Image Compression Framework
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.
GCN-GAN: A Non-linear Temporal Link Prediction Model for Weighted Dynamic Networks
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.
