Open AccessPosted Content
Axiomatic Attribution for Deep Networks
TL;DR: The problem of attributing the prediction of a deep network to its input features, a problem previously studied by several other works, is studied and two fundamental axioms— Sensitivity and Implementation Invariance that attribution methods ought to satisfy are identified.
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Abstract: We study the problem of attributing the prediction of a deep network to its input features, a problem previously studied by several other works. We identify two fundamental axioms---Sensitivity and Implementation Invariance that attribution methods ought to satisfy. We show that they are not satisfied by most known attribution methods, which we consider to be a fundamental weakness of those methods. We use the axioms to guide the design of a new attribution method called Integrated Gradients. Our method requires no modification to the original network and is extremely simple to implement; it just needs a few calls to the standard gradient operator. We apply this method to a couple of image models, a couple of text models and a chemistry model, demonstrating its ability to debug networks, to extract rules from a network, and to enable users to engage with models better.
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Citations
•Posted Content
An Empirical Comparison of Instance Attribution Methods for NLP
Pouya Pezeshkpour,Sarthak Jain,Byron C. Wallace,Sameer Singh +3 more
- 09 Apr 2021
TL;DR: The authors evaluate the degree to which different potential instance attribution agree with respect to the importance of training samples and find that simple retrieval methods yield training instances that differ from those identified via gradient-based methods, but that nonetheless exhibit desirable characteristics similar to more complex attribution methods.
2
An Axiomatic Approach to Explain Computer Generated Decisions: Extended Abstract
Martin Strobel
- 27 Dec 2018
TL;DR: This thesis research will be concerned with an axiomatic analysis of automatically generated explanations of data-driven classifiers, concerned with how to decide which explanation of a decision to trust given that there are many, potentially conflicting, possible explanations for any given decision.
2
On Evaluating Explainability Algorithms
Gokula Krishnan Santhanam,Ali Alami-Idrissi,Nuno Mota,Anika Schumann,Ioana Giurgiu +4 more
- 25 Sep 2019
TL;DR: A suite of multifaceted metrics that enables us to objectively compare explainers based on the correctness, consistency, as well as the confidence of the generated explanations are proposed.
Interpreting and Comparing Convolutional Neural Networks: A Quantitative Approach
Mohammad Mohaiminul Islam,Zahid Hassan Tushar +1 more
- 28 Jan 2021
TL;DR: This research aims to establish a framework for interpreting the CNNs by profiling them in terms of interpretable visual concepts and verifying them by means of Integrated Gradient, and proposes an integrated gradient-based class-specific relevance mapping approach that takes the spatial position of the region of interest in the input image.
•Proceedings Article
Are We Ready For Learned Cardinality Estimation
Xiaoying Wang,Changbo Qu,Weiyuan Wu,Jiannan Wang,Qingqing Zhou +4 more
- 01 Jan 2020
TL;DR: In this article, the authors compare five learned methods with eight traditional methods on four real-world datasets under a unified workload setting and find that learned models are indeed more accurate than traditional methods, but they often suffer from high training and inference costs.
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Neural Machine Translation by Jointly Learning to Align and Translate
TL;DR: In this paper, the authors propose to use a soft-searching model to find the parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly.
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Marco Tulio Ribeiro,Sameer Singh,Carlos Guestrin +2 more
- 13 Aug 2016
TL;DR: In this article, the authors propose LIME, a method to explain models by presenting representative individual predictions and their explanations in a non-redundant way, framing the task as a submodular optimization problem.
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TL;DR: A novel visualization technique is introduced that gives insight into the function of intermediate feature layers and the operation of the classifier in large Convolutional Network models, used in a diagnostic role to find model architectures that outperform Krizhevsky et al on the ImageNet classification benchmark.
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