Stop Guessing Which Weights Your Neural Network Actually Learned: Deterministic Initialization That Tracks Every Change

Short summary

A deterministic weight initialization technique enables precise tracking of which neural network weights actually learn during training, achieving 60%+ sparsity with negligible accuracy loss. Uses hash-based coordinate mapping instead of sequential RNG to maintain reproducibility. Includes working code and CLI tool for exploring weight behavior.

• •Deterministic initialization via coordinate-based hash functions enables reproducible weight change tracking
• •Achieves 60%+ sparsity with minimal accuracy impact through precision pruning
• •Includes practical implementation, workflow example, and CLI tool for weight analysis

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