Research map / Probabilistic and scientific modeling / Variational and generative sampling
A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Encode permutation invariance explicitly rather than using a nonexchangeable 2D convolutional architecture (ablation) | Exchangeable neural network · Recombination hotspot testing | accuracy | Helped |
| Train with fresh simulated examples instead of a fixed training set (ablation) | Exchangeable neural network · Recombination hotspot testing | test cross entropy | Helped |
| Train with fresh simulated examples instead of a fixed training set (ablation) | Exchangeable neural network · Recombination hotspot testing | posterior calibration | Helped |
| Add dropout regularization (ablation) | Exchangeable neural network with simulation-on-the-fly · Recombination hotspot testing | accuracy | Hurt |
| Use unphased diploid data rather than accurately phased haploid data (ablation) | Exchangeable neural network · Recombination hotspot testing | accuracy | Hurt |
| Use a Gaussian mixture model with 10 components instead of a log-normal approximate posterior (ablation) | Exchangeable neural network · Hotspot intensity estimation | Spearman correlation: 0.697 -> 0.782 | Helped |
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