Search Results for author:
Found 8 papers, 2 papers with code
Discerning and Enhancing the Weighted Sum-Rate Maximization Algorithms in Communications
By probing the theoretical underpinnings linking the BCA and MM algorithmic frameworks, we reveal the direct correlations between the equivalent transformation techniques, essential to the development of WMMSE and WSR-FP, and the surrogate functions pivotal to WSR-MM.
ASGNN: Graph Neural Networks with Adaptive Structure
In this work, we propose a novel interpretable message passing scheme with adaptive structure (ASMP) to defend against adversarial attacks on graph structure.
Towards Understanding Graph Neural Networks: An Algorithm Unrolling Perspective
The graph neural network (GNN) has demonstrated its superior performance in various applications.
Rate Maximizations for Reconfigurable Intelligent Surface-Aided Wireless Networks: A Unified Framework via Block Minorization-Maximization
The reconfigurable intelligent surface (RIS) has arose an upsurging research interest recently due to its promising outlook in 5G-and-beyond wireless networks.
Joint Design of Transmit Waveforms and Receive Filters for MIMO Radar via Manifold Optimization
The problem of joint design of transmit waveforms and receive filters is desirable in many application scenarios of multiple-input multiple-output (MIMO) radar systems.
AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition
The Audio/Visual Emotion Challenge and Workshop (AVEC 2019) "State-of-Mind, Detecting Depression with AI, and Cross-cultural Affect Recognition" is the ninth competition event aimed at the comparison of multimedia processing and machine learning methods for automatic audiovisual health and emotion analysis, with all participants competing strictly under the same conditions.
Sparse Reduced Rank Regression With Nonconvex Regularization
Numerical simulations show that the proposed algorithm is much more efficient compared to the benchmark methods and the nonconvex function can result in a better estimation accuracy.
Robust Maximum Likelihood Estimation of Sparse Vector Error Correction Model
In econometrics and finance, the vector error correction model (VECM) is an important time series model for cointegration analysis, which is used to estimate the long-run equilibrium variable relationships.
