What is recorded here?
A trained neural network may contain weights that contribute little to its predictions. LeCun, Denker and Solla estimate the cost of removing a weight from how sharply the training error bends near the learned solution. They remove low-cost weights and retrain. Their experiments show why curvature can be more useful than simply keeping large weights, while also showing that a local approximation becomes less accurate when too many weights are removed at once.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
The NIPS contribution indexed by its publisher under 1989. The saliency estimate uses a locally quadratic loss, near-stationary parameters and a diagonal-Hessian approximation. Reported pruning and retraining results are finite experiments; the name does not assert globally optimal pruning. Network redesign and automatic weight deletion are separate sources of compression.
Read the original work
- Official NeurIPS record for Optimal Brain Damage
- Yann LeCun publication record for Optimal Brain Damage
- NeurIPS publisher record for the 1989 contribution
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People and systems
Yann LeCun; John S. Denker; Sara A. Solla
Source-listed authors for the exact contribution; experimental subjects are not autonomous scientific authors. Source support and predecessor work retain separate roles. Individual effort shares and unresolved identities are not inferred.
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English catalogue narrative
LeCun, Denker and Solla: Optimal Brain Damage
A trained neural network may contain weights that contribute little to its predictions. LeCun, Denker and Solla estimate the cost of removing a weight from how sharply the training error bends near the learned solution. They remove low-cost weights and retrain. Their experiments show why curvature can be more useful than simply keeping large weights, while also showing that a local approximation becomes less accurate when too many weights are removed at once.
Scientific scope
The NIPS contribution indexed by its publisher under 1989. The saliency estimate uses a locally quadratic loss, near-stationary parameters and a diagonal-Hessian approximation. Reported pruning and retraining results are finite experiments; the name does not assert globally optimal pruning. Network redesign and automatic weight deletion are separate sources of compression.
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
Yann LeCun; John S. Denker; Sara A. Solla
Source-listed authors for the exact contribution; experimental subjects are not autonomous scientific authors. Source support and predecessor work retain separate roles. Individual effort shares and unresolved identities are not inferred.