The Memory Perturbation Equation: Understanding Model's Sensitivity to Data
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2023
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866929210623262720 |
|---|---|
| author | Nickl, Peter Xu, Lu Tailor, Dharmesh Möllenhoff, Thomas Khan, Mohammad Emtiyaz |
| author_facet | Nickl, Peter Xu, Lu Tailor, Dharmesh Möllenhoff, Thomas Khan, Mohammad Emtiyaz |
| contents | Understanding model's sensitivity to its training data is crucial but can also be challenging and costly, especially during training. To simplify such issues, we present the Memory-Perturbation Equation (MPE) which relates model's sensitivity to perturbation in its training data. Derived using Bayesian principles, the MPE unifies existing sensitivity measures, generalizes them to a wide-variety of models and algorithms, and unravels useful properties regarding sensitivities. Our empirical results show that sensitivity estimates obtained during training can be used to faithfully predict generalization on unseen test data. The proposed equation is expected to be useful for future research on robust and adaptive learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_19273 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | The Memory Perturbation Equation: Understanding Model's Sensitivity to Data Nickl, Peter Xu, Lu Tailor, Dharmesh Möllenhoff, Thomas Khan, Mohammad Emtiyaz Machine Learning Artificial Intelligence Understanding model's sensitivity to its training data is crucial but can also be challenging and costly, especially during training. To simplify such issues, we present the Memory-Perturbation Equation (MPE) which relates model's sensitivity to perturbation in its training data. Derived using Bayesian principles, the MPE unifies existing sensitivity measures, generalizes them to a wide-variety of models and algorithms, and unravels useful properties regarding sensitivities. Our empirical results show that sensitivity estimates obtained during training can be used to faithfully predict generalization on unseen test data. The proposed equation is expected to be useful for future research on robust and adaptive learning. |
| title | The Memory Perturbation Equation: Understanding Model's Sensitivity to Data |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2310.19273 |