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. How can we explain the predictions of a black-box model? Metrics give a local notion of distance on a manifold. We see how to approximate the second-order updates using conjugate gradient or Kronecker-factored approximations. This packages offers two modes of computation to calculate the influence 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. 2172: 2017: . values s_test and grad_z for each training image are computed on the fly can speed up the calculation significantly as no duplicate calculations take numbers above the images show the actual influence value which was calculated. place. 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. 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 Delta-STN: Efficient bilevel optimization of neural networks using structured response Jacobians. , . For this class, we'll use Python and the JAX deep learning framework. The next figure shows the same but for a different model, DenseNet-100/12. Google Scholar Digital Library; Josua Krause, Adam Perer, and Kenney Ng. Students are encouraged to attend class each week. Assignments for the course include one problem set, a paper presentation, and a final project. Understanding Black-box Predictions via Influence Functions. Here, we used CIFAR-10 as dataset. Kansagara, D., Englander, H., Salanitro, A., Kagen, D., Theobald, C., Freeman, M., and Kripalani, S. Risk prediction models for hospital readmission: a systematic review. Approach Consider a prediction problem from some input space X (e.g., images) to an output space Y(e.g., labels). Gradient-based hyperparameter optimization through reversible learning. PDF Understanding Black-box Predictions via Influence Functions The implicit and explicit regularization effects of dropout. This isn't the sort of applied class that will give you a recipe for achieving state-of-the-art performance on ImageNet. Strack, B., DeShazo, J. P., Gennings, C., Olmo, J. L., Ventura, S., Cios, K. J., and Clore, J. N. Impact of HbA1c measurement on hospital readmission rates: analysis of 70,000 clinical database patient records. Wei, B., Hu, Y., and Fung, W. Generalized leverage and its applications. A classic result tells us that the influence of upweighting z on the parameters ^ is given by.

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