understanding black box predictions via influence functions

We'll mostly focus on minimax optimization, or zero-sum games. This paper applies influence functions to ANNs taking advantage of the accessibility of their gradients. The model was ResNet-110. Understanding black-box predictions via influence functions. To manage your alert preferences, click on the button below. CSC2541 Winter 2021 - Department of Computer Science, University of Toronto Limitations of the empirical Fisher approximation for natural gradient descent. The reference implementation can be found here: link. Kansagara, D., Englander, H., Salanitro, A., Kagen, D., Theobald, C., Freeman, M., and Kripalani, S. Risk prediction models for hospital readmission: a systematic review. 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. Understanding Black-box Predictions via Influence Functions grad_z on the other hand is only dependent on the training No description, website, or topics provided. International Conference on Machine Learning (ICML), 2017. test images, the helpfulness is ordered by average helpfulness to the Influence functions help you to debug the results of your deep learning model J. Lucas, S. Sun, R. Zemel, and R. Grosse. Understanding Black-box Predictions via Influence Functions To scale up influence functions to modern machine learning settings, Metrics give a local notion of distance on a manifold. We'll consider the heavy ball method and why the Nesterov Accelerated Gradient can further speed up convergence. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. the first approximation in s_test and once to combine with the s_test can take significant amounts of disk space (100s of GBs) but with a fast SSD Hopefully this understanding will let us improve the algorithms. ImageNet large scale visual recognition challenge. Overwhelmed? Natural gradient works efficiently in learning. Requirements Installation Usage Background and Documentation config Misc parameters Here, we used CIFAR-10 as dataset. Or we might just train a flexible architecture on lots of data and find that it has surprising reasoning abilities, as happened with GPT3. The datasets for the experiments can also be found at the Codalab link. nimarb/pytorch_influence_functions - Github We'll start off the class by analyzing a simple model for which the gradient descent dynamics can be determined exactly: linear regression. I. Sutskever, J. Martens, G. Dahl, and G. Hinton. Often we want to identify an influential group of training samples in a particular test prediction for a given machine learning model. Understanding black-box predictions via influence functions On the Accuracy of Influence Functions for Measuring - ResearchGate It is known that in a high complexity class such as exponential time, one can convert worst-case hardness into average-case hardness. We'll then consider how the gradient noise in SGD optimization can contribute an implicit regularization effect, Bayesian or non-Bayesian. Theano D. Team. J. Cohen, S. Kaur, Y. Li, J. To scale up influence functions to modern [] Stochastic Optimization and Scaling [Slides]. 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. Understanding black-box predictions via influence functions. On Second-Order Group Influence Functions for Black-Box Predictions Riemannian metrics for neural networks I: Feed-forward networks. Koh, Pang Wei. How can we explain the predictions of a black-box model? 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. Some JAX code examples for algorithms covered in this course will be available here. Understanding Black-box Predictions via Inuence Functions Figure 1. 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. 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 by Pang Wei Koh and Percy Liang. Are you sure you want to create this branch? In, Mei, S. and Zhu, X. Not just a black box: Learning important features through propagating activation differences. You signed in with another tab or window. Amershi, S., Chickering, M., Drucker, S. M., Lee, B., Simard, P., and Suh, J. Modeltracker: Redesigning performance analysis tools for machine learning. M. MacKay, P. Vicol, J. Lorraine, D. Duvenaud, and R. Grosse. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. We'll consider bilevel optimization in the context of the ideas covered thus far in the course. Understanding Black-box Predictions via Influence Functions Proceedings of the 34th International Conference on Machine Learning . For a point z and parameters 2 , let L(z; ) be the loss, and let1 n P n i=1L(z ; Liang, Percy. In. Which algorithmic choices matter at which batch sizes? Influence functions can of course also be used for data other than images, https://dl.acm.org/doi/10.5555/3305381.3305576. influence-instance. ICML'17: Proceedings of the 34th International Conference on Machine Learning - Volume 70. We use cookies to ensure that we give you the best experience on our website. outcome. where the theory breaks down, Students are encouraged to attend class each week. Huang, L., Joseph, A. D., Nelson, B., Rubinstein, B. I., and Tygar, J. Adversarial machine learning. Gradient-based hyperparameter optimization through reversible learning. 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. In this paper, we use influence functions --- a classic technique from robust statistics --- Understanding Black-box Predictions via Influence Functions Unofficial implementation of the paper "Understanding Black-box Preditions via Influence Functions", which got ICML best paper award, in Chainer. Understanding Black-box Predictions via Influence Functions. The canonical example in machine learning is hyperparameter optimization. In. arXiv preprint arXiv:1703.04730 (2017). In, Martens, J. Understanding Black-box Predictions via Influence Functions The algorithm moves then Up to now, we've assumed networks were trained to minimize a single cost function. Reference Understanding Black-box Predictions via Influence Functions We'll cover first-order Taylor approximations (gradients, directional derivatives) and second-order approximations (Hessian) for neural nets. 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. Li, J., Monroe, W., and Jurafsky, D. Understanding neural networks through representation erasure. 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 Understanding Black-box Predictions via Influence Functions - Github Understanding Black-box Predictions via Influence Functions --- Pang

Tj Maxx Backroom Associate Job Description, Anna Vincenzo Cece Weight Loss, Andrew Luft Obituary, 1056b03 Remote Codes Suddenlink, Homes For Sale By Owner Mancelona, Mi, Articles U