CLR, a novel contrastive learning method using graph-based sample relationships. This approach outperformed traditional ...

Reinforcement learning (RL) algorithms typically require orders of magnitude more interactions than humans to learn effective policies. Research on memory in neuroscience suggests that humans' ...

To tackle this challenge, we develop a memory-based graph reinforcement learning approach, designed to train the agent to acquire a critical load restoration strategy in a distribution network under ...

Our extensive evaluation shows that GrapHD: (1) significantly enhances learning capability by giving the notion of short/long term memorization to learning algorithms, (2) enables cognitive computing ...

Transformer-based Memory Networks for Knowledge Graph Embeddings This program provides the implementation of our KG embedding model R-MeN as described in the ACL2020 paper. R-MeN utilizes a ...