Optimization✓ Mathematical
◆ The PatternPruning, distillation, and the lottery ticket hypothesis
Pruning removes unimportant weights (structured or unstructured). Knowledge distillation trains a small "student" model to mimic a large "teacher." The lottery ticket hypothesis suggests sparse subnetworks within large models can match full performance — if you find the right ticket.
Compression Ratio = Original Size / Compressed Size
A 4× compression ratio means your model is 75% smaller — potentially 4× faster with minimal accuracy loss.
// Interactive — pruning vs accuracy tradeoff
Sparsity %50%
Accuracy—
Pattern bridge: Model compression is feature selection applied to weights — remove what's redundant to keep what matters. In markets, signal-to-noise filtering does the same: strip the noise, keep the signal.