Embedding a random graph via GNN: Extended mean-field inference theory and RL applications to NP-Hard multi-robot/machine scheduling

Hyunwook Kang; James Robert Morrison

Embedding a random graph via GNN: Extended mean-field inference theory and RL applications to NP-Hard multi-robot/machine scheduling

Engineering & Technology, School Of

Research output: Contribution to conference › Poster › peer-review

Original language	English
State	Published - Jul 17 2020
Event	2020 International Conference on Machine Learning, Workshop on Graph Representation Learning and Beyond (ICML GRL+ 2020) - Duration: Jul 17 2020 → Jul 17 2020

Conference

Conference	2020 International Conference on Machine Learning, Workshop on Graph Representation Learning and Beyond (ICML GRL+ 2020)
Period	07/17/20 → 07/17/20

Cite this

@conference{cf22447506174bd8b592f24afd3967ea,

title = "Embedding a random graph via GNN: Extended mean-field inference theory and RL applications to NP-Hard multi-robot/machine scheduling",

author = "Hyunwook Kang and Morrison, {James Robert}",

year = "2020",

month = jul,

day = "17",

language = "English",

note = "null ; Conference date: 17-07-2020 Through 17-07-2020",

}

Embedding a random graph via GNN: Extended mean-field inference theory and RL applications to NP-Hard multi-robot/machine scheduling. / Kang, Hyunwook; Morrison, James Robert.

2020. Poster session presented at 2020 International Conference on Machine Learning, Workshop on Graph Representation Learning and Beyond (ICML GRL+ 2020).

Research output: Contribution to conference › Poster › peer-review

Embedding a random graph via GNN: Extended mean-field inference theory and RL applications to NP-Hard multi-robot/machine scheduling

Conference

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Cite this