Fundamental Limits of Neural Network Sparsification: Evidence from Catastrophic Interpretability Collapse
Extreme neural network sparsification (90% activation reduction) presents a critical challenge for mechanistic interpretability: understanding whether interpretable features survive aggressive compression. This work investigates feature survival under severe capacity constraints in hybrid Variational Autoencoder--Sparse Autoencoder (VAE-SAE) architectures. We introduce an adaptive sparsity scheduling framework that progressively reduces active neurons from 500 to 50 over 50 training epochs, and provide empirical evidence for fundamental limits of the sparsification-interpretability relationship. Testing across two benchmark datasets -- dSprites and Shapes3D -- with both Top-k and L1 spars...
| Authors | Dip Roy, Rajiv Misra, Sanjay Kumar Singh |
|---|---|
| Primary category | cs.LG |
| Published | 18 March 2026 |
| Last updated at source | 18 March 2026 |
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