Manuscript Psy01-A@1 · Psy01-A.tex · theorem · 94477fa3
Theorem: Fixed-weight nesting and fixed-mask commutation
Stability, hard-mask discontinuity, and schedule dependence › Nested pruning and a quantitative learning-induced defect · lines 1385–1412 · proof included or linked
Psy01-A@1#thm:nesting, Psy01-A@1#eq:nesting, Psy01-A@1#eq:fixedmaskcommutation
For fixed weights, consistent tie breaking, and ,
The statement holds layer by layer. Therefore early pruning followed by late pruning, with no intervening weight update, agrees with one-shot pruning to the same final integer counts.
For fixed masks , their parameter projections satisfy
Proof. The top positions in a fixed ranking are a subset of the top positions. Ranking the eligible survivors therefore selects exactly the original top . If some selected values are zero, the resulting weight vector still satisfies (eq:nesting). Equation (eq:fixedmaskcommutation) follows coordinatewise from multiplication of zeros and ones.
Displayed formulas in this passage
Psy01-A@1#eq:nesting · line 1389
Psy01-A@1#eq:fixedmaskcommutation · line 1398
Nearby in this section
- section: Nested pruning and a quantitative learning-induced defect
- theorem: Theorem: Ranking stability after an update
- example: Example: An intervening update changes the final support
- proposition: Proposition: A projection–gradient commutator bound
- section: Nested pruning and a quantitative learning-induced defect