The previous lecture treated stochasticity as a curse; this one treats it as a blessing. We are given training points z 1;:::;z n, where z i= (x i;y i) 2 XY . In. Springenberg, J. T., Dosovitskiy, A., Brox, T., and Riedmiller, M. Striving for simplicity: The all convolutional net. as long as you have a supervised learning problem. 2172: 2017: . 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/ You can get the default config by calling ptif.get_default_config(). Often we want to identify an influential group of training samples in a particular test prediction. You signed in with another tab or window. C. Maddison, D. Paulin, Y.-W. Teh, B. O'Donoghue, and A. Doucet. The datasets for the experiments can also be found at the Codalab link. influence-instance. We have a reproducible, executable, and Dockerized version of these scripts on Codalab. A unified analysis of extra-gradient and optimistic gradient methods for saddle point problems: Proximal point approach. , mislabel . the original paper linked here. The precision of the output can be adjusted by using more iterations and/or In Proceedings of the 34th International Conference on Machine Learning-Volume 70, pages 1885--1894. lage2019evaluationI. Model-agnostic meta-learning for fast adaptation of deep networks. Pang Wei Koh, Percy Liang; Proceedings of the 34th International Conference on Machine Learning, . To scale up influence functions to modern [] 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. We have a reproducible, executable, and Dockerized version of these scripts on Codalab. Your file of search results citations is now ready. James Tu, Yangjun Ruan, and Jonah Philion. below is divided into parameters affecting the calculation and parameters This packages offers two modes of computation to calculate the influence Here, we used CIFAR-10 as dataset. Koh P, Liang P, 2017. We'll mostly focus on minimax optimization, or zero-sum games. (b) 7 , 7 . A. S. Benjamin, D. Rolnick, and K. P. Kording. Deep learning via hessian-free optimization. Self-tuning networks: Bilevel optimization of hyperparameters using structured best-response functions. The dict structure looks similiar to this: Harmful is a list of numbers, which are the IDs of the training data samples Deep learning via Hessian-free optimization. In order to have any hope of understanding the solutions it comes up with, we need to understand the problems. Second-Order Group Influence Functions for Black-Box Predictions Inception-V3 vs RBF SVM(use SmoothHinge) The inception networks(DNN) picked up on the distinctive characteristics of the fish. Natural gradient works efficiently in learning. Therefore, this course will finish with bilevel optimziation, drawing upon everything covered up to that point in the course. Measuring the effects of data parallelism on neural network training. We'll cover first-order Taylor approximations (gradients, directional derivatives) and second-order approximations (Hessian) for neural nets. Agarwal, N., Bullins, B., and Hazan, E. Second order stochastic optimization in linear time. Debruyne, M., Hubert, M., and Suykens, J. This isn't the sort of applied class that will give you a recipe for achieving state-of-the-art performance on ImageNet. He, M. Narayanan, S. Gershman, B. Kim, and F. Doshi-Velez. Understanding Black-box Predictions via Influence Functions That can increase prediction accuracy, reduce Shrikumar, A., Greenside, P., Shcherbina, A., and Kundaje, A. The details of the assignment are here. This alert has been successfully added and will be sent to: You will be notified whenever a record that you have chosen has been cited. Copyright 2023 ACM, Inc. Understanding black-box predictions via influence functions. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. The final report is due April 7. This code replicates the experiments from the following paper: Understanding Black-box Predictions via Influence Functions. In many cases, the distance between two neural nets can be more profitably defined in terms of the distance between the functions they represent, rather than the distance between weight vectors. << Things get more complicated when there are multiple networks being trained simultaneously to different cost functions. the training dataset were the most helpful, whereas the Harmful images were the Then, it'll calculate all s_test values and save those to disk. Strack, B., DeShazo, J. P., Gennings, C., Olmo, J. L., Ventura, S., Cios, K. J., and Clore, J. N. Impact of HbA1c measurement on hospital readmission rates: analysis of 70,000 clinical database patient records. A classic result tells us that the influence of upweighting z on the parameters ^ is given by. Understanding black-box predictions via influence functions Up to now, we've assumed networks were trained to minimize a single cost function. Alex Adam, Keiran Paster, and Jenny (Jingyi) Liu, 25% Colab notebook and paper presentation. numbers above the images show the actual influence value which was calculated. Thus, we can see that different models learn more from different images. Measuring and regularizing networks in function space. prediction outcome of the processed test samples. The second mode is called calc_all_grad_then_test and PDF Understanding Black-box Predictions via Influence Functions - GitHub Pages