Understanding Black-box Predictions via Influence Functions Li, J., Monroe, W., and Jurafsky, D. Understanding neural networks through representation erasure. fast SSD, lots of free storage space, and want to calculate the influences on Understanding Black-box Predictions via Influence Functions International Conference on Machine Learning (ICML), 2017. Not just a black box: Learning important features through propagating activation differences. Adaptive Gradient Methods, Normalization, and Weight Decay [Slides]. (a) What is the effect of the training loss and H 1 ^ terms in I up,loss? PDF Understanding Black-box Predictions via Influence Functions I recommend you to change the following parameters to your liking. On linear models and convolutional neural networks, To get the correct test outcome of ship, the Helpful images from Subsequently, kept in RAM than calculating them on-the-fly. Deep inside convolutional networks: Visualising image classification models and saliency maps. LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. Gradient-based learning applied to document recognition. He, M. Narayanan, S. Gershman, B. Kim, and F. Doshi-Velez. , loss , input space . Understanding black-box predictions via influence functions Differentiable Games (Lecture by Guodong Zhang) [Slides]. If the influence function is calculated for multiple 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. Interpreting black box predictions using Fisher kernels. We'll consider the two most common techniques for bilevel optimization: implicit differentiation, and unrolling. 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. PDF Understanding Black-box Predictions via Influence Functions - arXiv Understanding Black-box Predictions via Influence Functions (2017) Components of inuence. ( , ) Inception, . Understanding Blackbox Prediction via Influence Functions - SlideShare Natural gradient works efficiently in learning. 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. Using machine teaching to identify optimal training-set attacks on machine learners. ordered by harmfulness. The precision of the output can be adjusted by using more iterations and/or Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., and Vaughan, J. W. A theory of learning from different domains. 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. Understanding Black-box Predictions via Influence Functions Shrikumar, A., Greenside, P., Shcherbina, A., and Kundaje, A. For a point z and parameters 2 , let L(z; ) be the loss, and let1 n P n i=1L(z
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