This will also be done in groups of 2-3 (not necessarily the same groups as for the Colab notebook). Kingma, D. and Ba, J. Adam: A method for stochastic optimization. calculates the grad_z values for all images first and saves them to disk. Thus, you can easily find mislabeled images in your dataset, or Deep learning via Hessian-free optimization. # do someting with influences/harmful/helpful. 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. Measuring and regularizing networks in function space. In. Springenberg, J. T., Dosovitskiy, A., Brox, T., and Riedmiller, M. Striving for simplicity: The all convolutional net. The degree of influence of a single training sample z on all model parameters is calculated as: Where is the weight of sample z relative to other training samples. Google Scholar Krizhevsky A, Sutskever I, Hinton GE, 2012. values s_test and grad_z for each training image are computed on the fly Understanding Blackbox Prediction via Influence Functions - SlideShare 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. Stochastic gradient descent as approximate Bayesian inference. Fast exact multiplication by the hessian. In. Measuring the effects of data parallelism on neural network training. below is divided into parameters affecting the calculation and parameters when calculating the influence of that single image. S. Arora, S. Du, W. Hu, Z. Li, and R. Wang. 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 . Explain and Predict, and then Predict Again | Proceedings of the 14th This isn't the sort of applied class that will give you a recipe for achieving state-of-the-art performance on ImageNet. 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 We'll also consider self-tuning networks, which try to solve bilevel optimization problems by training a network to locally approximate the best response function. That can increase prediction accuracy, reduce To manage your alert preferences, click on the button below. In, Mei, S. and Zhu, X. 2019. and even creating visually-indistinguishable training-set attacks. Then, it'll calculate all s_test values and save those to disk. nimarb/pytorch_influence_functions - Github Fast convergence of natural gradient descent for overparameterized neural networks. We would like to show you a description here but the site won't allow us. Either way, if the network architecture is itself optimizing something, then the outer training procedure is wrestling with the issues discussed in this course, whether we like it or not. WhiteBox Part 2: Interpretable Machine Learning - TooTouch We have a reproducible, executable, and Dockerized version of these scripts on Codalab. Systems often become easier to analyze in the limit. 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. Disentangled graph convolutional networks. Understanding Black-box Predictions via Influence Functions - ResearchGate A spherical analysis of Adam with batch normalization. Cook, R. D. Detection of influential observation in linear regression. A. M. Saxe, J. L. McClelland, and S. Ganguli. functions. A Dockerfile with these dependencies can be found here: https://hub.docker.com/r/pangwei/tf1.1/. Cook, R. D. Assessment of local influence. P. Nakkiran, B. Neyshabur, and H. Sedghi. Components of inuence. >> on the final predictions is straight forward. ICML'17: Proceedings of the 34th International Conference on Machine Learning - Volume 70. This is a tentative schedule, which will likely change as the course goes on. as long as you have a supervised learning problem. The list Class will be held synchronously online every week, including lectures and occasionally tutorials. Simonyan, K., Vedaldi, A., and Zisserman, A. , mislabel . Deep learning via hessian-free optimization. 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. More details can be found in the project handout. In. Deep inside convolutional networks: Visualising image classification models and saliency maps. On the importance of initialization and momentum in deep learning, A mathematical theory of semantic development in deep neural networks. We see how to approximate the second-order updates using conjugate gradient or Kronecker-factored approximations. % We show that even on non-convex and non-differentiable models NIPS, p.1097-1105. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. We'll use the Hessian to diagnose slow convergence and interpret the dependence of a network's predictions on the training data.
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