A Stable Neural Statistical Dependence Estimator for Autoencoder Feature Analysis
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866912977194582016 |
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| author | Hu, Bo Principe, Jose C |
| author_facet | Hu, Bo Principe, Jose C |
| contents | 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 dependence measurements and supports quantitative feature analysis, with a sequential convergence of singular values. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_11428 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Stable Neural Statistical Dependence Estimator for Autoencoder Feature Analysis Hu, Bo Principe, Jose C Machine Learning Artificial Intelligence 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 dependence measurements and supports quantitative feature analysis, with a sequential convergence of singular values. |
| title | A Stable Neural Statistical Dependence Estimator for Autoencoder Feature Analysis |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2603.11428 |