Training test 7, Training 1, test 7 . [1703.04730] Understanding Black-box Predictions via Influence Functions Understanding Black-box Predictions via Inuence Functions 2. samples for each test data sample. C. Maddison, D. Paulin, Y.-W. Teh, B. O'Donoghue, and A. Doucet. This is a PyTorch reimplementation of Influence Functions from the ICML2017 best paper: Dependencies: Numpy/Scipy/Scikit-learn/Pandas A Survey of Methods for Explaining Black Box Models Influence functions help you to debug the results of your deep learning model We have a reproducible, executable, and Dockerized version of these scripts on Codalab. 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 Self-tuning networks: Bilevel optimization of hyperparameters using structured best-response functions. An empirical model of large-batch training. https://dl.acm.org/doi/10.5555/3305381.3305576. sample. Loss , . NIPS, p.1097-1105. Github 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 (2017) Proc 34th Int Conf on Machine Learning, p.1885-1894. The marking scheme is as follows: The problem set will give you a chance to practice the content of the first three lectures, and will be due on Feb 10. Natural gradient works efficiently in learning. Lectures will be delivered synchronously via Zoom, and recorded for asynchronous viewing by enrolled students. 2172: 2017: . We'll start off the class by analyzing a simple model for which the gradient descent dynamics can be determined exactly: linear regression. 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 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. Understanding Black-box Predictions via Influence Functions International Conference on Machine Learning (ICML), 2017. Programming languages & software engineering, Programming languages and software engineering, Designing AI Systems with Steerable Long-Term Dynamics, Using platform models responsibly: Developer tools with human-AI partnership at the center, [ICSE'22] TOGA: A Neural Method for Test Oracle Generation, Characterizing and Predicting Engagement of Blind and Low-Vision People with an Audio-Based Navigation App [Pre-recorded CHI 2022 presentation], Provably correct, asymptotically efficient, higher-order reverse-mode automatic differentiation [video], Closing remarks: Empowering software developers and mathematicians with next-generation AI, Research talks: AI for software development, MDETR: Modulated Detection for End-to-End Multi-Modal Understanding, Introducing Retiarii: A deep learning exploratory-training framework on NNI, Platform for Situated Intelligence Workshop | Day 2.