MoNet

Reported on 5 benchmarks across 4 tasks · 1 paper · 5 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Graphs4 results

Graph RegressiononZINC-500k
MAE· 2016-11-25
0.292
best: 0.051 (ESA + rings + NodeRWSE + EdgeRWSE)
SOTA
Geometric deep learning on graphs and manifolds using mixture model CNNs arXiv:1611.08402
Graph RegressiononZINC 100k
MAE· 2016-11-25
0.407
best: 0.094 (CIN-small)
SOTA
Geometric deep learning on graphs and manifolds using mixture model CNNs arXiv:1611.08402
Graph ClassificationonCIFAR10 100k
Accuracy (%)· 2016-11-25
53.42
best: 76.468 (GRIT)
SOTA
Geometric deep learning on graphs and manifolds using mixture model CNNs arXiv:1611.08402
Node ClassificationonPATTERN 100k
Accuracy (%)· 2016-11-25
85.482
best: 86.816 (EGT)
SOTA
Geometric deep learning on graphs and manifolds using mixture model CNNs arXiv:1611.08402

Methodology1 result

ClassificationonCIFAR10 100k
Accuracy (%)· 2016-11-25
53.42
best: 76.468 (GRIT)
SOTA
Geometric deep learning on graphs and manifolds using mixture model CNNs arXiv:1611.08402