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        "description": "This is a data-dependent pruning heuristic. Neural-tangent-kernel discussion motivates trainability but constant-kernel/infinite-width conditions do not prove finite-network accuracy or globally optimal masks. It differs from SNIP's immediate-loss sensitivity; all reported training comparisons remain benchmark-specific.",
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      "statement": "# GraSP: pruning before training by gradient-signal preservation\n\nUsed Hessian-gradient products at initialization to score weight removals by their first-order effect on gradient norm, then trained the selected sparse subnetwork.\n\n## Scientific scope\n\nThis is a data-dependent pruning heuristic. Neural-tangent-kernel discussion motivates trainability but constant-kernel/infinite-width conditions do not prove finite-network accuracy or globally optimal masks. It differs from SNIP's immediate-loss sensitivity; all reported training comparisons remain benchmark-specific.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nChaoqi Wang; Guodong Zhang; Roger Grosse\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [Picking Winning Tickets Before Training by Preserving Gradient Flow](https://arxiv.org/abs/2002.07376v1)\n",
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