Hilbert Operator for Progressive Encoding (HOPE): A Mathematical Framework for Deconstructing Learned Representations in Deep Networks
- Published
- Source
- arXiv
- Paper number
- 701
- Field
- Machine Learning
- arXiv ID
- 2607.21366
Key points
- We model neurons as rank-1 operators in a Hilbert space, unifying pruning and merging under a single mathematical criterion.
- It works without data or hyperparameters by using batch-normalization statistics.
- Macro block eviction makes it possible to evaluate the entire residual connection path.
- In DEFT fine-tuning, we propose merging redundant neurons to make room in the parameter space for new tasks.
- The entire framework is mathematically rigorous, and concept-proving experiments validate it.
Paper links
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