S. Arora, S. Du, W. Hu, Z. Li, and R. Wang. /Filter /FlateDecode We try to understand the effects they have on the dynamics and identify some gotchas in building deep learning systems. Understanding Black-box Predictions via Influence Functions (2017) This In this paper, we use influence functions a classic technique from robust statistics to trace a . For this class, we'll use Python and the JAX deep learning framework. 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. A. Cook, R. D. Detection of influential observation in linear regression. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. However, in a lower Data-trained predictive models see widespread use, but for the most part they are used as black boxes which output a prediction or score. Inception-V3 vs RBF SVM(use SmoothHinge) The inception networks(DNN) picked up on the distinctive characteristics of the fish. on the final predictions is straight forward. Ribeiro, M. T., Singh, S., and Guestrin, C. "why should I trust you? On robustness properties of convex risk minimization methods for pattern recognition. We have a reproducible, executable, and Dockerized version of these scripts on Codalab. SVM , . To scale up influence functions to modern machine learning settings, we develop a simple, efficient implementation that requires only oracle access to gradients and Hessian-vector products. x\Y#7r~_}2;4,>Fvv,ZduwYTUQP }#&uD,spdv9#?Kft&e&LS 5[^od7Z5qg(]}{__+3"Bej,wofUl)u*l$m}FX6S/7?wfYwoF4{Hmf83%TF#}{c}w( kMf*bLQ?C}?J2l1jy)>$"^4Rtg+$4Ld{}Q8k|iaL_@8v Haoping Xu, Zhihuan Yu, and Jingcheng Niu. , Hessian-vector . The can take significant amounts of disk space (100s of GBs) but with a fast SSD Model-agnostic meta-learning for fast adaptation of deep networks. While influence estimates align well with leave-one-out. In this lecture, we consider the behavior of neural nets in the infinite width limit. ICML'17: Proceedings of the 34th International Conference on Machine Learning - Volume 70. . A. M. Saxe, J. L. McClelland, and S. Ganguli. Chatterjee, S. and Hadi, A. S. Influential observations, high leverage points, and outliers in linear regression. Understanding Black-box Predictions via Inuence Functions Figure 1. Rethinking the Inception architecture for computer vision. Implicit Regularization and Bayesian Inference [Slides]. On linear models and convolutional neural networks, Validations 4. ImageNet large scale visual recognition challenge. Therefore, if we bring in an idea from optimization, we need to think not just about whether it will minimize a cost function faster, but also whether it does it in a way that's conducive to generalization. Thus, you can easily find mislabeled images in your dataset, or Wojnowicz, M., Cruz, B., Zhao, X., Wallace, B., Wolff, M., Luan, J., and Crable, C. "Influence sketching": Finding influential samples in large-scale regressions. We show that even on non-convex and non-differentiable models Fast exact multiplication by the hessian. Why Use Influence Functions? Understanding Black-box Predictions via Inuence Functions 2.

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