Description of cohortsΒΆ

The experiments are structured in different cohort, containing one specific variation of parameters. To show the aim of the cohorts and to simplify the access of the saved artifacts using the artifact-viewer the following table offers a simple description for each cohort.

Cohort Description
cohort_1 Comparison of upper, lower and binning as different estimator. Additionally the hyperparameter of the estimators are varied. All experiments are done for relu and tanh using sgd as optimizer.
cohort_2 Comparison of training-, test- and full-dataset as base for the mi-computation. All experiments are done for relu and tanh using sgd as optimizer.
cohort_3 Comparison of upper, lower and binning as different estimator. Additionally the hyperparameter of the estimators are varied. All experiments are done for relu and tanh using adam as optimizer.
cohort_4 Comparison of training-, test- and full-dataset as base for the MI-computation. All experiments are done for relu and tanh using Adam as optimizer.
cohort_5 Comparison of different standard activation functions. All experiments are done using adam as optimizer.
cohort_6 Comparison of basic architectures. All experiments are done for relu and tanh using adam as optimizer.
cohort_7 Comparison of different hyperparameter for max-norm regularization. All experiments are done for relu and tanh using adam as optimizer.
cohort_8 Comparison of architecture with batchnorm and without batchnorm. All experiments are done for relu and tanh using adam as optimizer.
cohort_9 Comparison of architecture with batchnorm and without batchnorm. All experiments are done for relu and tanh using adam as optimizer.
cohort_10 Comparing weight norm for max_norm_weights = 0.9 and max_norm_weights = 0.6.
cohort_11  
cohort_12  
cohort_13 Effect of weight renormalization on activity patterns. Experiments for relu and tanh using adam as optimizer.
cohort_14