Not just a black box: Learning important features through propagating activation differences. affecting everything else. Negative momentum for improved game dynamics. the first approximation in s_test and once to combine with the s_test In, Moosavi-Dezfooli, S., Fawzi, A., and Frossard, P. Deep-fool: a simple and accurate method to fool deep neural networks. Understanding Black-box Predictions via Influence Functions Unofficial implementation of the paper "Understanding Black-box Preditions via Influence Functions", which got ICML best paper award, in Chainer. Understanding Black-box Predictions via Influence Functions Cook, R. D. and Weisberg, S. Characterizations of an empirical influence function for detecting influential cases in regression. calculate which training images had the largest result on the classification more recursions when approximating the influence. Differentiable Games (Lecture by Guodong Zhang) [Slides]. Uses cases Roadmap 2 Reviving an "old technique" from Robust statistics: Influence function This will naturally lead into next week's topic, which applies similar ideas to a different but related dynamical system. A unified analysis of extra-gradient and optimistic gradient methods for saddle point problems: Proximal point approach. The power of interpolation: Understanding the effectiveness of SGD in modern over-parameterized learning. Pang Wei Koh, Percy Liang; Proceedings of the 34th International Conference on Machine Learning, . Understanding Black-box Predictions via Inuence Functions Figure 1. Understanding black-box predictions via influence functions. Reconciling modern machine-learning practice and the classical bias-variance tradeoff. Data poisoning attacks on factorization-based collaborative filtering. Aggregated momentum: Stability through passive damping. A. Mokhtari, A. Ozdaglar, and S. Pattathil. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. which can of course be changed. 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. Ribeiro, M. T., Singh, S., and Guestrin, C. "why should I trust you? Thomas, W. and Cook, R. D. Assessing influence on predictions from generalized linear models. SVM , . 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. It is known that in a high complexity class such as exponential time, one can convert worst-case hardness into average-case hardness. Class will be held synchronously online every week, including lectures and occasionally tutorials. influence function. , . non-convex non-differentialble . Understanding Black-box Predictions via Influence Functions We'll cover first-order Taylor approximations (gradients, directional derivatives) and second-order approximations (Hessian) for neural nets. For more details please see We'll see first how Bayesian inference can be implemented explicitly with parameter noise. 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. Visual interpretability for deep learning: a survey | SpringerLink This leads to an important optimization tool called the natural gradient. 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. in terms of the dataset. The datasets for the experiments can also be found at the Codalab link. Understanding short-horizon bias in stochastic meta-optimization. To scale up influence functions to modern [] ?
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