Your file of search results citations is now ready. Understanding Black-box Predictions via Influence Functions Understanding Black-box Predictions via Influence Functions International Conference on Machine Learning (ICML), 2017. If Influence Functions are the Answer, Then What is the Question? logistic regression p (y|x)=\sigma (y \theta^Tx) \sigma . We show that even on non-convex and non-differentiable models where the theory breaks down, approximations to influence functions can still provide valuable information. Goodfellow, I. J., Shlens, J., and Szegedy, C. Explaining and harnessing adversarial examples. The deep bootstrap framework: Good online learners are good offline generalizers. Understanding black-box predictions via influence functions Computing methodologies Machine learning Recommendations On second-order group influence functions for black-box predictions With the rapid adoption of machine learning systems in sensitive applications, there is an increasing need to make black-box models explainable. [ICML] Understanding Black-box Predictions via Influence Functions PDF Understanding Black-box Predictions via Influence Functions - arXiv 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. we demonstrate that influence functions are useful for multiple purposes: The reference implementation can be found here: link. >> 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. the original paper linked here. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks, Chris Zhang, Dami Choi, Anqi (Joyce) Yang. Students are encouraged to attend synchronous lectures to ask questions, but may also attend office hours or use Piazza. 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. D. Maclaurin, D. Duvenaud, and R. P. Adams. Linearization is one of our most important tools for understanding nonlinear systems. An empirical model of large-batch training. 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. more recursions when approximating the influence. the first approximation in s_test and once to combine with the s_test your individual test dataset. For more details please see S. L. Smith, B. Dherin, D. Barrett, and S. De. Often we want to identify an influential group of training samples in a particular test prediction for a given We study the task of hardness amplification which transforms a hard function into a harder one. Haoping Xu, Zhihuan Yu, and Jingcheng Niu. 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. 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. We see how to approximate the second-order updates using conjugate gradient or Kronecker-factored approximations. calculations, which could potentially be 10s of thousands. 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. Understanding Black-box Predictions via Influence Functions While influence estimates align well with leave-one-out. This paper applies influence functions to ANNs taking advantage of the accessibility of their gradients. Despite its simplicity, linear regression provides a surprising amount of insight into neural net training. Noisy natural gradient as variational inference. test images, the helpfulness is ordered by average helpfulness to the influence function. Understanding Black-box Predictions via Influence Functions How can we explain the predictions of a black-box model? The main choices are. calculate which training images had the largest result on the classification In. The meta-optimizer has to confront many of the same challenges we've been dealing with in this course, so we can apply the insights to reverse engineer the solutions it picks. Understanding Black-box Predictions via Influence Functions. Thomas, W. and Cook, R. D. Assessing influence on predictions from generalized linear models. Model-agnostic meta-learning for fast adaptation of deep networks. The final report is due April 7. 2019. While this class draws upon ideas from optimization, it's not an optimization class. This is a better choice if you want all the bells-and-whistles of a near-state-of-the-art model. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. can speed up the calculation significantly as no duplicate calculations take We show that even on non-convex and non-differentiable models where the theory breaks down, approximations to influence functions can still provide valuable information. sample. PDF Understanding Black-box Predictions via Influence Functions - GitHub Pages For the final project, you will carry out a small research project relating to the course content. J. Cohen, S. Kaur, Y. Li, J. While these topics had consumed much of the machine learning research community's attention when it came to simpler models, the attitude of the neural nets community was to train first and ask questions later. After all, the optimization landscape is nonconvex, highly nonlinear, and high-dimensional, so why are we able to train these networks? Understanding Blackbox Prediction via Influence Functions - SlideShare No description, website, or topics provided. On linear models and convolutional neural networks, Disentangled graph convolutional networks. The next figure shows the same but for a different model, DenseNet-100/12. Understanding Black-box Predictions via Influence Functions , . % International conference on machine learning, 1885-1894, 2017. in terms of the dataset. Are you sure you want to create this branch? Reference Understanding Black-box Predictions via Influence Functions I am grateful to my supervisor Tasnim Azad Abir sir, for his . On Second-Order Group Influence Functions for Black-Box Predictions He, M. Narayanan, S. Gershman, B. Kim, and F. Doshi-Velez. Training test 7, Training 1, test 7 . In this paper, we use influence functions -- a classic technique from robust statistics -- to trace a model's prediction through . We'll mostly focus on minimax optimization, or zero-sum games. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Theano D. Team. Optimizing neural networks with Kronecker-factored approximate curvature. PW Koh*, KS Ang*, H Teo*, PS Liang. 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. Understanding Black-box Predictions via Influence Functions (2017) 1. In, Martens, J. Overview Neural nets have achieved amazing results over the past decade in domains as broad as vision, speech, language understanding, medicine, robotics, and game playing. Understanding black-box predictions via influence functions. /Filter /FlateDecode For toy functions and simple architectures (e.g. Ribeiro, M. T., Singh, S., and Guestrin, C. "why should I trust you? While one grad_z is used to estimate the Understanding black-box predictions via influence functions [1703.04730] Understanding Black-box Predictions via Influence Functions 2018. below is divided into parameters affecting the calculation and parameters A sign-up sheet will be distributed via email. On the limited memory BFGS method for large scale optimization. Programming languages & software engineering, Programming languages and software engineering, Designing AI Systems with Steerable Long-Term Dynamics, Using platform models responsibly: Developer tools with human-AI partnership at the center, [ICSE'22] TOGA: A Neural Method for Test Oracle Generation, Characterizing and Predicting Engagement of Blind and Low-Vision People with an Audio-Based Navigation App [Pre-recorded CHI 2022 presentation], Provably correct, asymptotically efficient, higher-order reverse-mode automatic differentiation [video], Closing remarks: Empowering software developers and mathematicians with next-generation AI, Research talks: AI for software development, MDETR: Modulated Detection for End-to-End Multi-Modal Understanding, Introducing Retiarii: A deep learning exploratory-training framework on NNI, Platform for Situated Intelligence Workshop | Day 2. Helpful is a list of numbers, which are the IDs of the training data samples Second-Order Group Influence Functions for Black-Box Predictions A Dockerfile with these dependencies can be found here: https://hub.docker.com/r/pangwei/tf1.1/. An evaluation of the human-interpretability of explanation. In. Y. LeCun, L. Bottou, G. B. Orr, and K.-R. Muller. Liu, D. C. and Nocedal, J. Understanding Black-box Predictions via Influence Functions --- Pang This is a tentative schedule, which will likely change as the course goes on. Terry Taewoong Um (terry.t.um@gmail.com) University of Waterloo Department of Electrical & Computer Engineering Terry T. Um UNDERSTANDING BLACK-BOX PRED -ICTION VIA INFLUENCE FUNCTIONS 1 If the influence function is calculated for multiple It is known that in a high complexity class such as exponential time, one can convert worst-case hardness into average-case hardness. Your search export query has expired. Fast exact multiplication by the hessian. nimarb/pytorch_influence_functions - Github Hopefully this understanding will let us improve the algorithms. The algorithm moves then As a result, the practical success of neural nets has outpaced our ability to understand how they work. When can we take advantage of parallelism to train neural nets? PVANet: Lightweight Deep Neural Networks for Real-time Object Detection. Li, B., Wang, Y., Singh, A., and Vorobeychik, Y. Understanding black-box predictions via influence functions. Not just a black box: Learning important features through propagating activation differences. 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. Aggregated momentum: Stability through passive damping. The project proposal is due on Feb 17, and is primarily a way for us to give you feedback on your project idea. Limitations of the empirical Fisher approximation for natural gradient descent. All information about attending virtual lectures, tutorials, and office hours will be sent to enrolled students through Quercus. Stochastic gradient descent as approximate Bayesian inference. The details of the assignment are here. How can we explain the predictions of a black-box model? In Proceedings of the 34th International Conference on Machine Learning-Volume 70, pages 1885--1894. lage2019evaluationI. Inception-V3 vs RBF SVM(use SmoothHinge) The inception networks(DNN) picked up on the distinctive characteristics of the fish. Subsequently, Approach Consider a prediction problem from some input space X (e.g., images) to an output space Y(e.g., labels). 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 Often we want to identify an influential group of training samples in a particular test prediction. Please download or close your previous search result export first before starting a new bulk export. 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. If you have questions, please contact Pang Wei Koh (pangwei@cs.stanford.edu). Explain and Predict, and then Predict Again | Proceedings of the 14th Metrics give a local notion of distance on a manifold. Frenay, B. and Verleysen, M. Classification in the presence of label noise: a survey. calculations even if we could reuse them for all subsequent s_test We show that even on non-convex and non-differentiable models reading both values from disk and calculating the influence base on them. test images, the harmfulness is ordered by average harmfullness to the 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. Loss non-convex, quadratic loss . The more recent Neural Tangent Kernel gives an elegant way to understand gradient descent dynamics in function space. We use cookies to ensure that we give you the best experience on our website. Understanding Black-box Predictions via Inuence Functions Figure 1. Understanding black-box predictions via influence functions. How can we explain the predictions of a black-box model? # do someting with influences/harmful/helpful. which can of course be changed. A. M. Saxe, J. L. McClelland, and S. Ganguli. The implicit and explicit regularization effects of dropout. When testing for a single test image, you can then You can get the default config by calling ptif.get_default_config(). In. $-hm`nrurh%\L(0j/hM4/AO*V8z=./hQ-X=g(0 /f83aIF'Mu2?ju]n|# =7$_--($+{=?bvzBU[.Q. The datasets for the experiments can also be found at the Codalab link. Stochastic Optimization and Scaling [Slides]. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. lehman2019inferringE. Deep learning via hessian-free optimization. : , , , . On the importance of initialization and momentum in deep learning, A mathematical theory of semantic development in deep neural networks. P. Nakkiran, B. Neyshabur, and H. Sedghi. training time, and reduce memory requirements. There are various full-featured deep learning frameworks built on top of JAX and designed to resemble other frameworks you might be familiar with, such as PyTorch or Keras. Uses cases Roadmap 2 Reviving an "old technique" from Robust statistics: Influence function A spherical analysis of Adam with batch normalization. Things get more complicated when there are multiple networks being trained simultaneously to different cost functions. For details and examples, look here.
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