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Tag |
Experiment wandb tag. (click here for more details) |
Any |
string |
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Seed |
Experiment seed. |
Any |
int, float |
experiment_settings |
Dataset |
The available datasets:1-UTKFace. (click here for more details) 2-Wine Quality. (click here for more details) |
1- utkf2- wine |
string |
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Normalization |
Enable dataset normalization |
True or False |
boolean |
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Dataset Training Size |
Size of the training dataset. |
Between 0 and the difference between the size of the dataset and the size of the test set |
int |
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Model Type |
The available models:1- Vanilla ANN, (click here for more details) 2-Vanilla CNN. (click here for more details)3- Resnet-18. (click here for more details) |
1- vanilla_ann2- vanilla_cnn3- resnet |
string |
model_settings |
Loss Type |
The available loss functions:1- Mean squared error. (MSE)2- MSE with Cutoff (threshold)3- Inverse variance. (IV)4- Batch inverse variance. (BIV) |
1- mse2- cutoffMSE3- iv4- biv |
string |
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1- Epsilon or:2- Threshold Value |
The value of this argument will be tailored depending on the loss type. It has two options1- Epsilon: A parameter that prevents the BIV function from having high loss values.2- Threshold Value: The cutoff or noise threshold value of the cutoffMSE loss. |
[0,+] |
float |
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. |
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Noise |
Enabling noise addition to the labels |
True or False |
boolean |
noise_settings |
Noise Type |
The available noise variance distributions:1- Uniform distribution.2- Gamma distribution. |
1- uniform2-binary_uniform3- gamma |
string |
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Params Type |
The current baseline supports the following settings for the noise distributions:1- Uniform boundaries: Where the boundaries of the uniform are provided.2- Gamma’s parameters: Where alpha and beta are provided.3- Mean and Variance: Where the mean and variance (v) of the selected distribution should be provided to estimate the its parameters indirectly. |
1- boundaries2- alphabeta3- meanvar4- meanvar_avg |
string |
parmas_settings |
Noise Distributions Ratio (p) |
Probability function over noise variance distributions. This is to study the contribution effect of low and high noise variance distributions. |
[0-1] |
float |
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Average Variance (X) |
Average over means of the noise variance distributions (two):X = p x + (1-p) x X = average mean variance.p = probability function over noise variance distributions.= mean of the first distribution. = mean of the second distribution. |
Any |
float |
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parameters |
Parameters |
Parameters of the noise variance distributions:1- Uniform 2- binary_uniform3- GammaOr: and v of the noise variance distributions.Note:1- When the “Params Type” is not boundaries, the first parameter in the list (var_scale) represents a condition to enabling maximum heteroscedasticity.1- will be the heteroscedasticity scale if var_scale equal to True.2- In this case, 0 < <= 1 |
1- (, )2- (,,,)3- (,)Or:1- (var_scale, ,)2- (var_scale, ,,,)or: 2- (var_scale, ,,,)3- (,,,) |
list |