Accelerating Hierarchical Sparse Predictive Coding with Hybrid Amortized Inference
Hierarchical predictive coding provides an interpretable framework for perception as error-driven inference in multi-layer models, while sparse coding imposes parsimonious latent representations through explicit sparsity constraints. Their combination yields hierarchical sparse predictive coding models with appealing computational and neuroscientific properties, but practical use is often limited by the cost of iterative latent inference. In such models, each input may require many recurrent refinement steps before a useful sparse representation is obtained, and this burden becomes more severe as the hierarchy deepens. We study this bottleneck by comparing training-and-inference procedure...
| Authors | Kazuhisa Fujita |
|---|---|
| Primary category | cs.LG |
| Published | 26 June 2026 |
| Last updated at source | 27 July 2026 |
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