Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. The details of the assignment are here. To scale up influence functions to modern [] 2018. This We'll see how to efficiently compute with them using Jacobian-vector products. Theano: A Python framework for fast computation of mathematical expressions. Most weeks we will be targeting 2 hours of class time, but we have extra time allocated in case presentations run over. Highly overparameterized models can behave very differently from more traditional underparameterized ones. Datta, A., Sen, S., and Zick, Y. Algorithmic transparency via quantitative input influence: Theory and experiments with learning systems. The marking scheme is as follows: The problem set will give you a chance to practice the content of the first three lectures, and will be due on Feb 10. Students are encouraged to attend class each week. 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. Requirements Installation Usage Background and Documentation config Misc parameters Adaptive Gradient Methods, Normalization, and Weight Decay [Slides]. Huang, L., Joseph, A. D., Nelson, B., Rubinstein, B. I., and Tygar, J. Adversarial machine learning. A. Understanding Black-box Predictions via Influence Functions --- Pang and even creating visually-indistinguishable training-set attacks. All information about attending virtual lectures, tutorials, and office hours will be sent to enrolled students through Quercus. nimarb/pytorch_influence_functions - Github numbers above the images show the actual influence value which was calculated. 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. International Conference on Machine Learning (ICML), 2017. Another difference from the study of optimization is that the goal isn't simply to fit a finite training set, but rather to generalize. How can we explain the predictions of a black-box model? The datasets for the experiments can also be found at the Codalab link. Explain and Predict, and then Predict Again | Proceedings of the 14th Measuring and regularizing networks in function space. A Dockerfile with these dependencies can be found here: https://hub.docker.com/r/pangwei/tf1.1/. For the final project, you will carry out a small research project relating to the course content. To scale up influence functions to modern machine learning Things get more complicated when there are multiple networks being trained simultaneously to different cost functions. Requirements chainer v3: It uses FunctionHook. 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 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. On the origin of implicit regularization in stochastic gradient descent. Interpreting black box predictions using Fisher kernels. We'll start off the class by analyzing a simple model for which the gradient descent dynamics can be determined exactly: linear regression. If Influence Functions are the Answer, Then What is the Question? prediction outcome of the processed test samples. Simonyan, K., Vedaldi, A., and Zisserman, A. Understanding Black-box Predictions via Influence Functions sample. Understanding Black-box Predictions via Influence Functions on the final predictions is straight forward. 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. Loss , . How can we explain the predictions of a black-box model? Amershi, S., Chickering, M., Drucker, S. M., Lee, B., Simard, P., and Suh, J. Modeltracker: Redesigning performance analysis tools for machine learning. affecting everything else. This is a tentative schedule, which will likely change as the course goes on. 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. Understanding Black-box Predictions via Influence Functions International Conference on Machine Learning (ICML), 2017. prediction outcome of the processed test samples. # do someting with influences/harmful/helpful. Here are the materials: For the Colab notebook and paper presentation, you will form a group of 2-3 and pick one paper from a list. The final report is due April 7. ImageNet large scale visual recognition challenge. 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. Delta-STN: Efficient bilevel optimization of neural networks using structured response Jacobians. 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 (2017) In order to have any hope of understanding the solutions it comes up with, we need to understand the problems. 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. Goodfellow, I. J., Shlens, J., and Szegedy, C. Explaining and harnessing adversarial examples. Springenberg, J. T., Dosovitskiy, A., Brox, T., and Riedmiller, M. Striving for simplicity: The all convolutional net. , . Are you sure you want to create this branch? Understanding Black-box Predictions via Influence Functions. when calculating the influence of that single image. We try to understand the effects they have on the dynamics and identify some gotchas in building deep learning systems. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks, Chris Zhang, Dami Choi, Anqi (Joyce) Yang. However, as stated 7 1 . Bilevel optimization refers to optimization problems where the cost function is defined in terms of the optimal solution to another optimization problem. Self-tuning networks: Bilevel optimization of hyperparameters using structured best-response functions. The deep bootstrap framework: Good online learners are good offline generalizers. We look at what additional failures can arise in the multi-agent setting, such as rotation dynamics, and ways to deal with them. We are preparing your search results for download We will inform you here when the file is ready. Understanding Black-box Predictions via Influence Functions - SlideShare . Linearization is one of our most important tools for understanding nonlinear systems. Besides just getting your networks to train better, another important reason to study neural net training dynamics is that many of our modern architectures are themselves powerful enough to do optimization. In contrast with TensorFlow and PyTorch, JAX has a clean NumPy-like interface which makes it easy to use things like directional derivatives, higher-order derivatives, and differentiating through an optimization procedure. A Survey of Methods for Explaining Black Box Models 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 . Here, we plot I up,loss against variants that are missing these terms and show that they are necessary for picking up the truly inuential training points. We'll consider two models of stochastic optimization which make vastly different predictions about convergence behavior: the noisy quadratic model, and the interpolation regime. Agarwal, N., Bullins, B., and Hazan, E. Second order stochastic optimization in linear time. Riemannian metrics for neural networks I: Feed-forward networks. Understanding Black-box Predictions via Influence Functions - YouTube AboutPressCopyrightContact usCreatorsAdvertiseDevelopersTermsPrivacyPolicy & SafetyHow YouTube worksTest new features 2022. GitHub - kohpangwei/influence-release All Holdings within the ACM Digital Library. J. Cohen, S. Kaur, Y. Li, J. To run the tests, further requirements are: You can either install this package directly through pip: Calculating the influence of the individual samples of your training dataset For more details please see calculated. Krizhevsky, A., Sutskever, I., and Hinton, G. E. Imagenet classification with deep convolutional neural networks. On the Accuracy of Influence Functions for Measuring - ResearchGate A classic result by Radford Neal showed that (using proper scaling) the distribution of functions of random neural nets approaches a Gaussian process. In, Cadamuro, G., Gilad-Bachrach, R., and Zhu, X. Debugging machine learning models. 10.5 Influential Instances | Interpretable Machine Learning - GitHub Pages the first approximation in s_test and once to combine with the s_test A. S. Benjamin, D. Rolnick, and K. P. Kording. Liu, D. C. and Nocedal, J. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. 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. Google Scholar Krizhevsky A, Sutskever I, Hinton GE, 2012. When testing for a single test image, you can then test images, the helpfulness is ordered by average helpfulness to the thereby identifying training points most responsible for a given prediction. The reference implementation can be found here: link. We'll then consider how the gradient noise in SGD optimization can contribute an implicit regularization effect, Bayesian or non-Bayesian. Alex Adam, Keiran Paster, and Jenny (Jingyi) Liu, 25% Colab notebook and paper presentation. Theano D. Team. we demonstrate that influence functions are useful for multiple purposes: your individual test dataset. To manage your alert preferences, click on the button below. 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. Most importantnly however, s_test is only The answers boil down to an observation that neural net training seems to have two distinct phases: a small-batch, noise-dominated phase, and a large-batch, curvature-dominated one. He, M. Narayanan, S. Gershman, B. Kim, and F. Doshi-Velez. To scale up influence functions to modern machine learning settings, Understanding Black-box Predictions via Influence Functions Proceedings of the 34th International Conference on Machine Learning . This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. values s_test and grad_z for each training image are computed on the fly Hopefully this understanding will let us improve the algorithms. In, Martens, J. Understanding Blackbox Prediction via Influence Functions - SlideShare 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. Reconciling modern machine-learning practice and the classical bias-variance tradeoff. (b) 7 , 7 . Jaeckel, L. A. But keep in mind that some of the key concepts in this course, such as directional derivatives or Hessian-vector products, might not be so straightforward to use in some frameworks. fast SSD, lots of free storage space, and want to calculate the influences on Pearlmutter, B. below is divided into parameters affecting the calculation and parameters We have a reproducible, executable, and Dockerized version of these scripts on Codalab. Neural tangent kernel: Convergence and generalization in neural networks. 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. Systems often become easier to analyze in the limit. insignificant. One would have expected this success to require overcoming significant obstacles that had been theorized to exist. Understanding Black-box Predictions via Influence Functions - Github We look at three algorithmic features which have become staples of neural net training. 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. 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.

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