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Influence Approximation
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Estimating the influence of training triples on the behavior of a machine learning model.
논문
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Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data
Deeper Understanding of Black-box Predictions via Generalized Influence Functions
DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models
LIA: Privacy-Preserving Data Quality Evaluation in Federated Learning Using a Lazy Influence Approximation
Explaining Neural Matrix Factorization with Gradient Rollback
On the Accuracy of Influence Functions for Measuring Group Effects
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.