methodology-11

Explainable Models

3260 papers • 126 benchmarks • 313 datasets

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ukp-square/square-core

2 papers 68

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ScienceQA

OpenXAI

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Most implemented papers

Multimodal Explanations: Justifying Decisions and Pointing to the Evidence

Trevor Darrell, B. Schiele, Marcus Rohrbach, Lisa Anne Hendricks, Anna Rohrbach, Zeynep Akata, Dong Huk Park•Wed Feb 14 2018

It is quantitatively shown that training with the textual explanations not only yields better textual justification models, but also better localizes the evidence that supports the decision, supporting the thesis that multimodal explanation models offer significant benefits over unimodal approaches.

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Variable Selection with Copula Entropy

Jian Ma•Sun Oct 27 2019

Experimental results show that CE based method can select the `right' variables out effectively and derive better interpretable results than traditional methods do without sacrificing accuracy performance.

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Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery

M. Soljačić, Samuel Kim, Peter Y. Lu, Srijon Mukherjee, M. Gilbert, Li Jing, V. Ceperic•Mon Dec 09 2019

This article uses a neural network-based architecture for symbolic regression called the equation learner (EQL) network and integrates it with other deep learning architectures such that the whole system can be trained end-to-end through backpropagation.

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Retrieving and Highlighting Action with Spatiotemporal Reference

Hirokatsu Kataoka, Yuchi Ishikawa, Seito Kasai, Y. Aoki, Masaki Hayashi, Kensho Hara•Mon May 18 2020

A framework that jointly retrieves and spatiotemporally highlights actions in videos by enhancing current deep cross-modal retrieval methods and generates various maps conditioned on different actions, in which conventional visual reasoning methods only go as far as to show a single deterministic saliency map.

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SegNBDT: Visual Decision Rules for Segmentation

Joseph E. Gonzalez, Alvin Wan, Sarah Adel Bargal, Daniel Ho, You Song, Henk Tillman•Wed Jun 10 2020

This work builds a hybrid neural-network and decision-tree model for segmentation that attains neural network segmentation accuracy and provides semi-automatically constructed visual decision rules such as "Is there a window?".

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EXPLAN: Explaining Black-box Classifiers using Adaptive Neighborhood Generation

P. Rasouli, Ingrid Chieh Yu•Tue Jun 30 2020

Defining a representative locality is an urgent challenge in perturbation-based explanation methods, which influences the fidelity and soundness of explanations. We address this issue by proposing a robust and intuitive approach for EXPLaining black-box classifiers using Adaptive Neighborhood generation (EXPLAN). EXPLAN is a module-based algorithm consisted of dense data generation, representative data selection, data balancing, and rule-based interpretable model. It takes into account the adjacency information derived from the black-box decision function and the structure of the data for creating a representative neighborhood for the instance being explained. As a local model-agnostic explanation method, EXPLAN generates explanations in the form of logical rules that are highly interpretable and well-suited for qualitative analysis of the model’s behavior. We discuss fidelity-interpretability trade-offs and demonstrate the performance of the proposed algorithm by a comprehensive comparison with state-of-the-art explanation methods LIME, LORE, and Anchor. The conducted experiments on real-world data sets show our method achieves solid empirical results in terms of fidelity, precision, and stability of explanations.

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Towards Musically Meaningful Explanations Using Source Separation

Ethan Manilow, Verena Haunschmid, G. Widmer•Thu Sep 03 2020

This work proposes audioLIME, a method based on Local Interpretable Model-agnostic Explanation (LIME), extended by a musical definition of locality, and demonstrates the general applicability of the method on a third-party music tagger.

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CDT: Cascading Decision Trees for Explainable Reinforcement Learning

Pablo Hernandez-Leal, Zihan Ding, G. Ding, Ruitong Huang•Sat Nov 14 2020

Cascading Decision Trees (CDTs) apply representation learning on the decision path to allow richer expressivity and show that in both situations, where CDTs are used as policy function approximators or as imitation learners to explain black-box policies, CDTs can achieve better performances with more succinct and explainable models than SDTs.

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Explainable Multivariate Time Series Classification: A Deep Neural Network Which Learns To Attend To Important Variables As Well As Informative Time Intervals

Suhang Wang, Tsung-Yu Hsieh, Yiwei Sun, Vasant G Honavar•Sun Nov 22 2020

A novel, modular, convolution-based feature extraction and attention mechanism that simultaneously identifies the variables as well as time intervals which determine the classifier output on multi-variate time series classification task.

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