What is recorded here?
Accumulates gradient agreement to approximate a training point’s effect on test loss along optimization, with practical checkpoint and selected-layer variants for scalable inspection.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
First-order gradient approximations and checkpoint heuristics estimate training-example influence along an optimization trajectory. They are not exact deletion-and-retraining effects; extending the method to another optimizer requires its actual update rule.
Read the original work
- Estimating Training Data Influence by Tracking Gradient Descent
- Estimating Training Data Influence by Tracing Gradient Descent
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People and systems
Garima; Frederick Liu; Satyen Kale; Mukund Sundararajan
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
TracIn: checkpoint-gradient estimates of training-example influence
Accumulates gradient agreement to approximate a training point’s effect on test loss along optimization, with practical checkpoint and selected-layer variants for scalable inspection.
Scientific scope
First-order gradient approximations and checkpoint heuristics estimate training-example influence along an optimization trajectory. They are not exact deletion-and-retraining effects; extending the method to another optimizer requires its actual update rule.
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
Garima; Frederick Liu; Satyen Kale; Mukund Sundararajan
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.