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Found 8 papers, 4 papers with code
Group-Aware Coordination Graph for Multi-Agent Reinforcement Learning
To overcome these limitations, we present a novel approach to infer the Group-Aware Coordination Graph (GACG), which is designed to capture both the cooperation between agent pairs based on current observations and group-level dependencies from behaviour patterns observed across trajectories.
Inferring Latent Temporal Sparse Coordination Graph for Multi-Agent Reinforcement Learning
The LTS-CG leverages agents' historical observations to calculate an agent-pair probability matrix, where a sparse graph is sampled from and used for knowledge exchange between agents, thereby simultaneously capturing agent dependencies and relation uncertainty.
Layer-diverse Negative Sampling for Graph Neural Networks
Experiments on various real-world graph datasets demonstrate the effectiveness of our approach in improving the diversity of negative samples and overall learning performance.
Predicting Single-cell Drug Sensitivity by Adaptive Weighted Feature for Adversarial Multi-source Domain Adaptation
The development of single-cell sequencing technology had promoted the generation of a large amount of single-cell transcriptional profiles, providing valuable opportunities to explore drug-resistant cell subpopulations in a tumor.
To Transmit or Not to Transmit: Optimal Sensor Schedule for Remote State Estimation of Discrete-Event Systems
A transmission mechanism decides whether the observable information is transmitted or not, according to an information transmission policy, such that the receiver has sufficient information to satisfy the purpose of decision-making.
Graph Convolutional Neural Networks with Diverse Negative Samples via Decomposed Determinant Point Processes
However, there are more non-neighbour nodes in the whole graph, which provide diverse and useful information for the representation update.
Learning from the Dark: Boosting Graph Convolutional Neural Networks with Diverse Negative Samples
An interesting way to understand GCNs is to think of them as a message passing mechanism where each node updates its representation by accepting information from its neighbours (also known as positive samples).
Interpretable Melody Generation from Lyrics with Discrete-Valued Adversarial Training
Generating melody from lyrics is an interesting yet challenging task in the area of artificial intelligence and music.
