What is recorded here?
Estimated connection sensitivity through derivatives of multiplicative masks, retaining high-sensitivity weights before training the resulting sparse network.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
The criterion approximates discrete removal effects using an infinitesimal derivative at initialization and is a heuristic, not a global solution to constrained training. It uses task data; it is not data-free. High unstructured sparsity does not itself guarantee measured runtime acceleration or preserve accuracy on every architecture.
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People and systems
Namhoon Lee; Thalaiyasingam Ajanthan; Philip H. S. Torr
Source-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.
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English catalogue narrative
SNIP: data-dependent single-shot pruning at initialization
Estimated connection sensitivity through derivatives of multiplicative masks, retaining high-sensitivity weights before training the resulting sparse network.
Scientific scope
The criterion approximates discrete removal effects using an infinitesimal derivative at initialization and is a heuristic, not a global solution to constrained training. It uses task data; it is not data-free. High unstructured sparsity does not itself guarantee measured runtime acceleration or preserve accuracy on every architecture.
Reading status
This 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.
People and systems
Namhoon Lee; Thalaiyasingam Ajanthan; Philip H. S. Torr
Source-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.