A Stable Neural Statistical Dependence Estimator for Autoencoder Feature Analysis
Statistical dependence measures like mutual information is ideal for analyzing autoencoders, but it can be ill-posed for deterministic, static, noise-free networks. We adopt the variational (Gaussian) formulation that makes dependence among inputs, latents, and reconstructions measurable, and we propose a stable neural dependence estimator based on an orthonormal density-ratio decomposition. Unlike MINE, our method avoids input concatenation and product-of-marginals re-pairing, reducing computational cost and improving stability. We introduce an efficient NMF-like scalar cost and demonstrate empirically that assuming Gaussian noise to form an auxiliary variable enables meaningful dependen...
| Authors | Bo Hu, Jose C Principe |
|---|---|
| Primary category | cs.LG |
| Published | 12 March 2026 |
| Last updated at source | 22 March 2026 |
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