Search Results for author:
Found 6 papers, 1 papers with code
GAN Based Near-Field Channel Estimation for Extremely Large-Scale MIMO Systems
The mixed line-of-sight (LoS) and non-line-of-sight (NLoS) XL-MIMO near-field channel model is adopted to describe the XL-MIMO near-field channel accurately.
Understanding and Improving Deep Graph Neural Networks: A Probabilistic Graphical Model Perspective
Given the fixed point equation (FPE) derived from the variational inference on the Markov random fields, the deep GNNs, including JKNet, GCNII, DGCN, and the classical GNNs, such as GCN, GAT, and APPNP, can be regarded as different approximations of the FPE.
TextRGNN: Residual Graph Neural Networks for Text Classification
Recently, text classification model based on graph neural network (GNN) has attracted more and more attention.
Auto-Encoding Score Distribution Regression for Action Quality Assessment
Traditionally, AQA is treated as a regression problem to learn the underlying mappings between videos and action scores.
Ranked #1 on
Action Quality Assessment
on JIGSAWS
Robust Graph Learning Under Wasserstein Uncertainty
To this end, we propose a graph learning framework using Wasserstein distributionally robust optimization (WDRO) which handles uncertainty in data by defining an uncertainty set on distributions of the observed data.
On Secure Degrees of Freedom of the MIMO Interference Channel with Local Output Feedback
This paper studies the problem of sum-secure degrees of freedom (SDoF) of the (M, M, N, N) multiple-input multiple-output (MIMO) interference channel with local output feedback, so as to build an information-theoretic foundation and provide practical transmission schemes for 6G-enabled vehicles-to-vehicles (V2V).
Information Theory Information Theory
