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Table 3 DeepSurv’s experimental hyper-parameters

From: DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network

Hyper-parameter Sim linear Sim nonlinear WHAS SUPPORT METABRIC Sim treatment GBSG
Optimizer sgd sgd adam adam adam adam adam
Activation SELU ReLU ReLU SELU SELU SELU SELU
# Dense layers 1 3 2 1 1 1 1
# Nodes / Layer 4 17 48 44 41 45 8
Learning rate (LR) 2.922e −4 3.194e −4 0.067 0.047 0.010 0.026 0.154
2 Reg 1.999 4.425 16.094 8.120 10.891 9.722 6.551
Dropout 0.375 0.401 0.147 0.255 0.160 0.109 0.661
LR decay 3.579e −4 3.173e −4 6.494e −4 2.573e −3 4.169e −3 1.636e −4 5.667e −3
Momentum 0.906 0.936 0.863 0.859 0.844 0.845 0.887