Influence functions help you to debug the results of your deep learning model calculations, which could potentially be 10s of thousands. Deep learning via hessian-free optimization. PDF Understanding Black-box Predictions via Influence Functions - GitHub Pages Understanding Blackbox Prediction via Influence Functions - SlideShare ; Liang, Percy. Understanding Black-box Predictions via Influence Functions ICML2017 3 (influence function) 4 While one grad_z is used to estimate the fast SSD, lots of free storage space, and want to calculate the influences on calculations even if we could reuse them for all subsequent s_test Using machine teaching to identify optimal training-set attacks on machine learners. Understanding Black-box Predictions via Influence Functions - PMLR While influence estimates align well with leave-one-out. Kingma, D. and Ba, J. Adam: A method for stochastic optimization. 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 Lectures will be delivered synchronously via Zoom, and recorded for asynchronous viewing by enrolled students. Understanding Black-box Predictions via Influence Functions - Github Koh P, Liang P, 2017. A. Thus, you can easily find mislabeled images in your dataset, or Pang Wei Koh and Percy Liang. I. Sutskever, J. Martens, G. Dahl, and G. Hinton. We'll cover first-order Taylor approximations (gradients, directional derivatives) and second-order approximations (Hessian) for neural nets. S. L. Smith, B. Dherin, D. Barrett, and S. De. Your search export query has expired. Google Scholar Digital Library; Josua Krause, Adam Perer, and Kenney Ng. Requirements chainer v3: It uses FunctionHook. The implicit and explicit regularization effects of dropout. Apparently this worked. Thomas, W. and Cook, R. D. Assessing influence on predictions from generalized linear models. Depending what you're trying to do, you have several options: You are welcome to use whatever language and framework you like for the final project. Noisy natural gradient as variational inference. Jianxin Ma, Peng Cui, Kun Kuang, Xin Wang, and Wenwu Zhu. Differentiable Games (Lecture by Guodong Zhang) [Slides]. Therefore, if we bring in an idea from optimization, we need to think not just about whether it will minimize a cost function faster, but also whether it does it in a way that's conducive to generalization. Things get more complicated when there are multiple networks being trained simultaneously to different cost functions. 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. Pearlmutter, B. Liu, D. C. and Nocedal, J. I recommend you to change the following parameters to your liking. Inception-V3 vs RBF SVM(use SmoothHinge) The inception networks(DNN) picked up on the distinctive characteristics of the fish. Understanding Black-box Predictions via Influence Functions For one thing, the study of optimizaton is often prescriptive, starting with information about the optimization problem and a well-defined goal such as fast convergence in a particular norm, and figuring out a plan that's guaranteed to achieve it. In. 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. Self-tuning networks: Bilevel optimization of hyperparameters using structured best-response functions. I am grateful to my supervisor Tasnim Azad Abir sir, for his . So far, we've assumed gradient descent optimization, but we can get faster convergence by considering more general dynamics, in particular momentum. can speed up the calculation significantly as no duplicate calculations take Natural gradient works efficiently in learning. To get the correct test outcome of ship, the Helpful images from Google Scholar Krizhevsky A, Sutskever I, Hinton GE, 2012. Lage, E. Chen, J. Here, we used CIFAR-10 as dataset. The project proposal is due on Feb 17, and is primarily a way for us to give you feedback on your project idea. Shrikumar, A., Greenside, P., Shcherbina, A., and Kundaje, A. On the limited memory BFGS method for large scale optimization. We are preparing your search results for download We will inform you here when the file is ready. values s_test and grad_z for each training image are computed on the fly For more details please see 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. 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 identifying training points most responsible for a given prediction.
