Number of disentangled data features for different VAE models having different latent dimensions.</p
The red highlighting boxes show the disentangled features achieved by a model. A) Vanilla VAE could ...
Columns represent the different hyperparameters. Each bar within a column represents a specific sett...
Overall model fit indices for different number of latent classes and latent class membership probabi...
The figure shows the WSEPIN score on the y−axis, while the bars are colored after the different late...
Details of the multi-view data sets used in our experiments (feature type (dimensionality)).</p
A) Using 10 dimensional latent space, β-VAE can learn a more disentangled representation compared to...
Number of all features detected compared to the number of features within isolation windows, by mini...
Learning disentangled representations with variational autoencoders (VAEs) is often attributed to th...
<p>Number of significantly differentially expressed probes between experimental conditions.</p
Number of TP and FP found in the different simulations for the unbalanced study design dataset.</p
Number of features detected in isolation windows and identified versus minimum voxel intensity.</p
The parameter complexity of different models and the number of parameters they actually use.</p
<p>Number of (absolutely) bistable models obtained with different concentration vectors under differ...
<p>Number of discordant variants after applying different exclusion criteria for WGS and WES experim...
<p>The feature dimension of the sparse feature subsets and the full features.</p
The red highlighting boxes show the disentangled features achieved by a model. A) Vanilla VAE could ...
Columns represent the different hyperparameters. Each bar within a column represents a specific sett...
Overall model fit indices for different number of latent classes and latent class membership probabi...
The figure shows the WSEPIN score on the y−axis, while the bars are colored after the different late...
Details of the multi-view data sets used in our experiments (feature type (dimensionality)).</p
A) Using 10 dimensional latent space, β-VAE can learn a more disentangled representation compared to...
Number of all features detected compared to the number of features within isolation windows, by mini...
Learning disentangled representations with variational autoencoders (VAEs) is often attributed to th...
<p>Number of significantly differentially expressed probes between experimental conditions.</p
Number of TP and FP found in the different simulations for the unbalanced study design dataset.</p
Number of features detected in isolation windows and identified versus minimum voxel intensity.</p
The parameter complexity of different models and the number of parameters they actually use.</p
<p>Number of (absolutely) bistable models obtained with different concentration vectors under differ...
<p>Number of discordant variants after applying different exclusion criteria for WGS and WES experim...
<p>The feature dimension of the sparse feature subsets and the full features.</p
The red highlighting boxes show the disentangled features achieved by a model. A) Vanilla VAE could ...
Columns represent the different hyperparameters. Each bar within a column represents a specific sett...
Overall model fit indices for different number of latent classes and latent class membership probabi...
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