Understanding black-box predictions via influence functions. A. S. Benjamin, D. Rolnick, and K. P. Kording. Wojnowicz, M., Cruz, B., Zhao, X., Wallace, B., Wolff, M., Luan, J., and Crable, C. "Influence sketching": Finding influential samples in large-scale regressions. In this paper, we use influence functions a classic technique from robust statistics to trace a models prediction through the learning algorithm and back to its training data, thereby identifying training points most responsible for a given prediction. Lage, E. Chen, J. Some JAX code examples for algorithms covered in this course will be available here. Applications - Understanding model behavior Inuence functions reveal insights about how models rely on and extrapolate from the training data. Riemannian metrics for neural networks I: Feed-forward networks. 7 1 . Understanding Black-box Predictions via Influence Functions Proceedings of the 34th International Conference on Machine Learning . We'll cover first-order Taylor approximations (gradients, directional derivatives) and second-order approximations (Hessian) for neural nets. Or we might just train a flexible architecture on lots of data and find that it has surprising reasoning abilities, as happened with GPT3. On the Accuracy of Influence Functions for Measuring - ResearchGate When can we take advantage of parallelism to train neural nets? On linear models and convolutional neural networks, we demonstrate that influence functions are useful for multiple purposes: understanding model behavior, debugging models, detecting dataset errors, and even creating visually-indistinguishable training-set attacks.See more on this video at https://www.microsoft.com/en-us/research/video/understanding-black-box-predictions-via-influence-functions/ in terms of the dataset. ? This code replicates the experiments from the following paper: Pang Wei Koh and Percy Liang Understanding Black-box Predictions via Influence Functions International Conference on Machine Learning (ICML), 2017. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. can speed up the calculation significantly as no duplicate calculations take The details of the assignment are here. Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., and Darrell, T. Decaf: A deep convolutional activation feature for generic visual recognition. Pearlmutter, B. 10.5 Influential Instances | Interpretable Machine Learning - GitHub Pages ICML'17: Proceedings of the 34th International Conference on Machine Learning - Volume 70. (b) 7 , 7 . calculations, which could potentially be 10s of thousands. The datasets for the experiments can also be found at the Codalab link. Understanding Black-box Predictions via Influence Functions In this paper, we use influence functions a classic technique from robust statistics to trace a model's prediction through the learning algorithm and back to its training data, thereby identifying training points most responsible for a given prediction. Liu, Y., Jiang, S., and Liao, S. Efficient approximation of cross-validation for kernel methods using Bouligand influence function. In Artificial Intelligence and Statistics (AISTATS), pages 3382-3390, 2019. Visualised, the output can look like this: The test image on the top left is test image for which the influences were ImageNet large scale visual recognition challenge. On the importance of initialization and momentum in deep learning.
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