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Table 5 A list of hyperparameters optimised for each architecture type, and the domains over which they were optimised

From: Building attention and edge message passing neural networks for bioactivity and physical–chemical property prediction

Hyperparameter

SELU-MPNN

AMPNN

EMNN

Learn-rate

{\(10^{ - 6} - 10^{ - 4}\)}

{\(10^{ - 6} - 10^{ - 4}\)}

{\(10^{ - 6} - 10^{ - 4}\)}

Message-size

[10,16,25,40]

[10,16,25,40]

NA

Message-passes

[1–10]

[1–10]

[1–8]

Msg-hidden-dim

[50,85,150]

[50,85,150]

[50,85,150]

Gather-width

[30,45,70,100]

[30,45,70,100]

[30,45,70,100]

Gather-emb-hidden-dim

[15,26,45,80]

[15,26,45,80]

[15, 26, 45]

Gather-att-hidden-dim

[15,26,45,80]

[15,26,45,80]

[15, 26, 45]

Out-hidden-dim

[360,450,560]

[360,450,560]

[360,450,560]

Out-dropout-p

{0.0–0.1}

{0.0–0.1}

{0.0–0.1}

Out-layer-shrinkage

{0.2–0.6}

{0.2–0.6}

{0.2–0.6}

Att-hidden-dim

NA

[50,85,150]

[50,85,150]

Edge-emb-hidden-dim

NA

NA

[60,105,180]

Edge-embedding-size

NA

NA

[30,50,80]

  1. Square brackets indicate discrete domains
  2. NA not applicable