Research map / Probabilistic and scientific modeling / Variational and generative sampling
Manifold GPLVMs for discovering non-Euclidean latent structure in neural data
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use a T² latent manifold instead of a Euclidean latent space | mGPLVM · Synthetic T² data | cross-validated prediction error | Helped |
| Use an SO(3) latent manifold instead of a Euclidean latent space | mGPLVM · Synthetic SO(3) data | prediction performance | Helped |
| Compare candidate manifolds using cross-validated log likelihood | mGPLVM · Synthetic T², SO(3), and S³ data | cross-validated log likelihood | Helped |
| Use a T¹ latent manifold instead of a Euclidean latent space | mGPLVM · Drosophila ellipsoid body | cross-validated prediction error | Helped |
| Use a T¹ latent manifold instead of a Euclidean latent space | mGPLVM · Mouse ADn, session 140313 | cross-validated log likelihood ratio: 127 ± 30 | Helped |
| Add separate length scales for each T² dimension (ablation) | T²-mGPLVM · Synthetic T¹ data | latent dimensionality selection | Helped |
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