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Found 5 papers, 1 papers with code
Saliency-Aware Regularized Graph Neural Network
In this work, we propose the Saliency-Aware Regularized Graph Neural Network (SAR-GNN) for graph classification, which consists of two core modules: 1) a traditional graph neural network serving as the backbone for learning node features and 2) the Graph Neural Memory designed to distill a compact graph representation from node features of the backbone.
Cognitive-Driven Optimization of Sparse Array Transceiver for MIMO Radar Beamforming
Cognitive multiple-input multiple-output (MIMO) radar is capable of adjusting system parameters adaptively by sensing and learning in complex dynamic environment.
Adaptive Sparse Array Beamformer Design by Regularized Complementary Antenna Switching
In this work, we propose a novel strategy of adaptive sparse array beamformer design, referred to as regularized complementary antenna switching (RCAS), to swiftly adapt both array configuration and excitation weights in accordance to the dynamic environment for enhancing interference suppression.
Sparse Array Transceiver Design for Enhanced Adaptive Beamforming in MIMO Radar
The proposed approach entails an entwined design, i. e., jointly selecting the optimum transmit and receive sensor locations for accomplishing MaxSINR receive beamforming.
Memory-Attended Recurrent Network for Video Captioning
Typical techniques for video captioning follow the encoder-decoder framework, which can only focus on one source video being processed.
