Tensorflow KR에서 진행하고 있는 논문읽기 모임 PR12에서 발표한 저의 네번째 발표입니다. 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, identifying the points most responsible for a given prediction. old friend extra wide slippers. International Conference on Machine . Understanding Black-box Predictions via Influence Functions. Influence functions help you to debug the results of your deep learning model in terms of the dataset. Different machine learning models have different ways of making predictions. Here is an open source project that implements calculation of the influence function for any Tensorflow models. This . Understanding black-box predictions via influence functions. 2017. This package is a plug-n-play PyTorch reimplementation of Influence Functions. Here, we plot I up,loss against variants that are missing these terms and show that they are necessary for picking up the truly influential training points. (b) Using a random, wrongly-classified test point, we compared the predicted vs. actual differences in loss after leave-one-out retraining on the . Best-performing models: complicated, black-box . 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. In SIGIR. Basu et. Proc 34th Int Conf on Machine Learning, p.1885-1894. International conference on machine learning, 1885-1894, 2017. ; Liang, Percy. They use influence functions, a classic technique from robust statistics (Cook & Weisberg, 1980) that tells us how the model parameters change as we upweight a training point by an infinitesimal amount. Understanding Black-box Predictions via Influence Functions. Understanding black-box predictions via influence functions. 这是ICML 2017的最佳论文，有时候虽然神经网络模型取得了非常高的预测精度，但是却无法解释模型是怎么得到这些结果的，这篇论文可以在一定程度上理解模型对于数据的敏感程度，分析每一个数据的变化 . 이번에는 ICML2017에서 베스트페이퍼상을 받은 "딥러닝의 . Ananya Kumar, Tengyu Ma, Percy Liang. 1644 : 2017: Mobility network models of COVID-19 explain inequities and inform reopening. When testing for a single test image, you can then calculate which training images had the largest result on the classification outcome. Based on some existing implementations, I'm developing reliable Pytorch implementation of influence function. Understanding Black- box Predictions via Influence Functions Pang Wei Koh， Percy Liang Stanford University ICML2017 DLゼミ 小川一太郎 2. The . Understanding black-box predictions via influence functions. In Doina Precup and Yee Whye Teh, editors, Proceedings of the 34th International Conference on Machine Learning, volume 70 of . Koh P, Liang P, 2017. Understanding Black-box Predictions via Influence Functions. Do you remember "Understanding Black-box Predictions via Influence Functions", the best paper at ICML this year? Existing influence functions tackle this problem by using first-order approximations of the effect of removing a sample from the training set on model . a model predicts in this . Pang Wei Koh 1, Percy Liang 1 • Institutions (1) 14 Mar 2017-arXiv: Machine Learning. How can we explain the predictions of a black-box model? Understanding Black-box Predictions via Influence Functions Examples are not Enough, Learn to Criticize! On linear models and ConvNets, we show that influence functions can be used to understand model behavior, 이번에는 ICML2017에서 베스트페이퍼상을 받은 "딥러닝의 . International Conference on Machine Learning (ICML), 2017. 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 . Deep learning via hessian-free optimization. Understanding the particular weaknesses of a model by identifying influential instances helps to form a "mental model" of the . 63 Highly Influenced PDF View 10 excerpts, cites methods and background With the rapid adoption of machine learning systems in sensitive applications, there is an increasing need to make black-box models explainable. •Pearlmutter, B. Understanding black-box predictions via influence functions. Abstract: How can we explain the predictions of a black-box model? This is "Understanding Black-box Predictions via Influence Functions --- Pang Wei Koh, Percy Liang" by TechTalksTV on Vimeo, the home for high quality… Tue Apr 12: More deep learning . Best paper award. International Conference on Machine Learning (ICML), 2017. Abstract: How can we explain the predictions of a black-box model? How can we explain the predictions of a blackbox model? Understanding self-training for gradual domain adaptation. Understanding Black-box Predictions via Influence Functions. How can we explain the predictions of a black-box model? Yeh et. 1.1. Fast exact multiplication by the . Modular Multitask Reinforcement Learning with Policy Sketches Jacob Andreas, Dan Klein, Sergey Levine . The paper deals with the problem of finding infuential training samples using the Infuence Functions framework from classical statistics recently revisited in the paper "Understanding Black-box Predictions via Influence Functions" (code).The classical approach, however, is only applicable to smooth . If a model's influential training points for a specific action are unrelated to this action, we might suppose that . How a fixed model leads to particular predictions, i.e., what predictions . Proceedings of the 34th International Conference on Machine Learning, in PMLR 70:1885-1894 •Martens, J. Uses cases Roadmap 2 Even if two models have the same performance, the way they make predictions from the features can be very different and therefore fail in different scenarios. How can we explain the predictions of a black-box model? Understanding Black-box Predictions via Influence Functions. Pang Wei Koh, Percy Liang. Title:Understanding black-box predictions via influence functions by Pang Wei Koh, Percy Liang, International Conference on Machine Learning (ICML), 2017 November 14, 2017 Speaker: Jiae Kim Title: The Geometry of Nonlinear Embeddings in Discriminant Analysis with Gaussian Kernel In this paper, we use influence func- tions — a classic technique from robust statis- tics — to trace a model's prediction through the learning algorithm and back to its training data, thereby identifying training points most respon- sible for a given prediction. Koh, Pang Wei, and Percy Liang. 影響関数(influence function)を用いて、特定の学習データの有無や、 学習データに加える摂動が予測結果に与える影響を定式化 2. P. Koh , and P. Liang . Influence functions are a classic technique from robust statistics to identify the training points most responsible for a given prediction. Tensorflow KR에서 진행하고 있는 논문읽기 모임 PR12에서 발표한 저의 네번째 발표입니다. ICML, 2017. Metrics give a local notion of distance on a manifold. Koh, Pang Wei. al. Criticism for Interpretability: Xu Chu Nidhi Menon Yue Hu : 11/15: Reducing Training Set: Introduction to papers in this class LightGBM: A Highly Efficient Gradient Boosting Decision Tree BlinkML: Approximate Machine Learning with Probabilistic Guarantees: Xu Chu Eric Qin Xiang Cheng . This code replicates the experiments from the following paper: Pang Wei Koh and Percy Liang. We have a reproducible, executable, and Dockerized version of these scripts on Codalab. of ML models. S Chang*, E Pierson*, PW Koh*, J Gerardin, B Redbird, D Grusky, . Google Scholar Krizhevsky A, Sutskever I, Hinton GE, 2012. Let's study the change in model parameters due to removing a point zfrom training set: ^ z def= argmin 2 1 n X z i6=z L(z i; ) Than, the change is given by: ^ z . How can we explain the predictions of a black-box model? Such approaches aim to provide explanations for a particular model prediction by highlighting important words in the corresponding input text. 5. Understanding Black-box Predictions via Influence Functions. 実際の解析例 . 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. (a) By varying t, we can approximate the hinge loss with arbitrary accuracy: the green and blue lines are overlaid on top of each other. . A Unified Maximum Likelihood Approach for Estimating Symmetric Properties of . Training point influence Slides: Released Interpreting Interpretations: Organizing Attribution Methods by Criteria Representer point selection for DNN Understanding Black-box Predictions via Influence Functions: Pre-recorded lecture: Released Homework 2: Released Description: In Homework 2, students gain hands-on exposure to a variety of explanation toolkits. Metrics give a local notion of distance on a manifold. Pang Wei Koh (Stanford), Percy Liang (Stanford) ICML 2017 Best Paper Award. 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. 2018 link 1.College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China 2.College of Intelligence and Computing, Tianjin University, Tianjin 300072, China; Received:2018-11-30 Online:2019-02-28 Published:2020-08-21 4. This is the Dockerfile: FROM tensorflow/tensorflow:1.1.-gpu MAINTAINER Pang Wei Koh koh.pangwei@gmail.com RUN apt-get update && apt-get install -y python-tk RUN pip install keras==2.0.4 . Nos marques; Galeries; Wishlist Applying deep learning to solve security . Honorable Mentions. We are not allowed to display external PDFs yet. uence functions The goal is to understand the e ect of training points to model's predictions. 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. ICML, 2017. 作者也分别在不同规模 (CIFAR, ImageNet)和不同应用 (Classification, Denoising)中证明了 . This Dockerfile specifies the run-time environment for the experiments in the paper "Understanding Black-box Predictions via Influence Functions" (ICML 2017). The influence function could be very useful to understand and debug deep learning models. Xin Xin, Xiangnan He, Yongfeng Zhang, Yongdong Zhang, and Joemon Jose. This code replicates the experiments from the following paper: Pang Wei Koh and Percy Liang. How can we explain the predictions of a black-box model? Parameters: workspace - Path for workspace directory; feeder (InfluenceFeeder) - Dataset . Understanding Black-box Predictions via Influence Functions Figure 3. Understanding model behavior. Security of Deep Learning. In this paper, we use influence functions -- a classic technique from robust statistics -- to trace a model's prediction through the. Understanding Black-box Predictions via Influence Functions Pang Wei Koh, Percy Liang. How can we explain the predictions of a black-box model? lonely planet restaurant. Imagenet classification with deep convolutional neural networks. Correspondence to: Yuchen Zhang, Percy Liang, Martin J. Wainwright. In this paper, they tackle this question by tracing a model's predictions through its learning algorithm and back to the training data, where the model parameters ultimately derive from. "Understanding black-box predictions via influence functions." arXiv preprint arXiv:1703.04730 (2017). Let's study the change in model parameters due to removing a point zfrom training set: ^ z def= argmin 2 1 n X z i6=z L(z i; ) Than, the change is given by: ^ z . How can we explain the predictions of a black-box model? 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. Understanding Black-box Predictions via Influence Functions and Estimating Training Data Influence by Tracking Gradient Descent are both methods designed to find training data which is influential for specific model decisions. [ICML] Understanding Black-box Predictions via Influence Functions 李浩 低头玩手机相当于在脖子上挂两个大铁球。 156 人 赞同了该文章 1.摘要 本文是ICML 2017 best paper，来自Stanford的Pang Wei Koh和Percy liang。 文章从训练数据的角度出发，解释模型的预测结果。 具体地说，输入一个测试样本 ，模型给出了预测结果 ，我们想知道这一行为与哪些训练样本关系最大；换个角度说，把这些训练样本去掉，或者改变他们的label，那模型就很可能在 上给出不同的预测结果。 2.方法 假设有 个训练样本 ，其中 ， 令 表示样本 在模型参数为 下的损失函数，则经验风险为 Instead, we adjust those weights via an algorithm based on the influence function, a measure of a model's dependency on one training example. Validations 4. Influence Functions for PyTorch. 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. Baselines: Influence estimation methods & Deep KNN [4] poison defense Attack #1: Convex polytope data poisoning [5] on CIFAR10 Attack #2: Speech recognition backdoor dataset [6] References Experimental Results Using CosIn to Detect a Target [1] Koh et al., "Understanding black-box predictions via influence functions" ICML, 2017. Nature, 1-6, 2020. The datasets for the experiments . al. (a) Compared to I up,loss, the inner product is missing two key terms, train loss and H^θ. will a model make and . Influence function for neural networks is proposed in the ICML2017 best paper (Wei Koh & Liang, 2017). 2020 link; Representer Points: Representer Point Selection for Explaining Deep Neural Networks. Abstract. In this paper, they tackle this question by tracing a model's predictions through its learning algorithm and back to the training data, where the model parameters ultimately derive from. Understanding Black-box Predictions via Influence Functions. 783: 2020: Peer and self assessment in massive online classes. You will be redirected to the full text document in the repository in a few seconds, if not click here.click here. Understanding Black-box Predictions via Influence Functions Understanding Black-box Predictions via Influence Functions Pang Wei Koh & Perry Liang Presented by -Theo, Aditya, Patrick 1 1.Influence functions: definitions and theory 2.Efficiently calculating influence functions 3. While this might be useful for . This has motivated the development of methods for interpreting such models, e.g., via gradient-based saliency maps or the visualization of attention weights. explainability. This is a PyTorch reimplementation of Influence Functions from the ICML2017 best paper: Understanding Black-box Predictions via Influence Functions by Pang Wei Koh and Percy Liang. 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 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 . This approach can give more exact explanation to a given prediction. Influence Functions were introduced in the paper Understanding Black-box Predictions via Influence Functions by Pang Wei Koh and Percy Liang (ICML2017). We demonstrate that this technique outperforms state-of-the-art methods on semi-supervised image and language classification tasks. In ICML. Then we . However, to the best of my knowledge, there is no generic PyTorch implementation with reliable test codes. International Conference on Machine Learning (ICML), 2017. Laugel, Thibault, Marie-Jeanne Lesot, Christophe Marsala, Xavier Renard, and Marcin Detyniecki. Understanding Black-box Predictions via Influence Functions. In this paper, we proposed a novel model explanation method to explain the predictions or black-box models. To scale up influence functions to modern machine learning settings, we develop a simple, efficient implementation that requires only . Figure 1: Influence functions vs. Euclidean inner product. How would the model's predictions change if didn't have particular training point? To make the approach efficient, we propose a fast and effective approximation of the influence function. 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 this paper, we use influence functions — a classic technique from robust statistics — to trace a model's prediction through the learning. C Kulkarni, PW . Table 2: Counterfactual sets generated by ACCENT . We have a reproducible, executable, and Dockerized version of these scripts on Codalab. Pang Wei Koh and Percy Liang. Work on interpreting these black-box models has focused on un-derstanding how a fixed model leads to particular predic-tions, e.g., by locally fitting a simpler model around the test 1Stanford University, Stanford, CA. 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. Modern deep learning models for NLP are notoriously opaque. How would the model's predictions change if didn't have particular training point? Understanding black-box predictions via influence functions. First, a local prediction explanation has been designed, which combines the key training points identified via influence function and the framework of LIME. How can we explain the predictions of a black-box model? The reference implementation can be found here: link. Lost Relatives of the Gumbel Trick Matej Balog, Nilesh Tripuraneni, Zoubin Ghahramani, Adrian Weller. Influence Functions: Understanding Black-box Predictions via Influence Functions. This work takes a novel look at black box interpretation of test predictions in terms of training examples, making use of Fisher kernels as the defining feature embedding of each data point, combined with Sequential Bayesian Quadrature (SBQ) for efficient selection of examples. How can we explain the predictions of a black- box model? 简介. The datasets for the experiments . Background. 3: 1/27: Metrics. This repository implements the LeafRefit and LeafInfluence methods described in the paper __.. Pang Wei Koh and Percy Liang "Understanding Black-box Predictions via Influence Functions" ICML2017: class Influence (workspace, feeder, loss_op_train, loss_op_test, x_placeholder, y_placeholder, test_feed_options=None, train_feed_options=None, trainable_variables=None) [source] ¶ Influence Class. uence functions The goal is to understand the e ect of training points to model's predictions. Google Scholar NIPS, p.1097-1105. Contact; Boutique. Understanding Black-box Predictions via Influence Functions (ICML 2017 Best Paper) DeepXplore: Automated Whitebox Testing of Deep Learning Systems (SOSP 2017 Best Paper) Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data(ICLR 2017 Best Paper) Overview of Deep Learning and Security in 2017. DNN等の複雑なモデルに対する影響関数の効率的な計算手法の提案 ナイーブに行うとパラメータ数の二乗のオーダーの計算となり、不可能 3. To scale up influence . Understanding Black-box Predictions via Influence Functions. 3: 1/28: Metrics. In International Conference on Machine Learning (ICML), pp. PW Koh, P Liang. 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. This . NeurIPS materials . pytorch-influence-functionsRelease 0.1.1. ICML , volume 70 of Proceedings of Machine Learning Research, page 1885-1894. 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. 735-742, 2010. Convexified convolutional neural networks. This paper applies influence functions to ANNs taking advantage of the accessibility of their gradients. What is now often being studied? Smooth approximations to the hinge loss. Koh and Liang 2017 link; Influence Functions and Non-convex models: Influence functions in Deep Learning are Fragile. Relational Collaborative Filtering: Modeling Multiple Item Relations for Recommendation. 2019. How can we explain the predictions of a black-box model? A. 具体做法是首先使用全部数据得到一个初始模型，然后在这样的初始模型上计算每个样本的influence，去掉那些对降低验证集loss的样本后，使用新的训练集再次训练得到最终的模型。. Understanding Blackbox Predictions via Influence Functions 1. Understanding black-box predictions via influence functions. tion (Krizhevsky et al.,2012) — are complicated, black-box models whose predictions seem hard to explain. "Inverse classification for comparison-based interpretability in machine learning." arXiv preprint arXiv . 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, identifying the points most responsible for a given prediction. Why Use Influence Functions? Understanding Black-box Predictions via Influence Functions. They use influence functions, a classic technique from robust statistics (Cook & Weisberg, 1980) that tells us how the model parameters change as we upweight a training point by an infinitesimal amount. why. This is "Understanding Black-box Predictions via Influence Functions --- Pang Wei Koh, Percy Liang" by TechTalksTV on Vimeo, the home for high quality… How can we explain the predictions of a black-box model? Often we want to identify an influential group of training samples in a particular test prediction.
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