Explainable Models

30 papers with code • 0 benchmarks • 2 datasets

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Use these libraries to find Explainable Models models and implementations

Most implemented papers

Discovery of Nonlinear Dynamical Systems using a Runge-Kutta Inspired Dictionary-based Sparse Regression Approach

goyalpike/RK4_SinDy 11 May 2021

Discovering dynamical models to describe underlying dynamical behavior is essential to draw decisive conclusions and engineering studies, e. g., optimizing a process.

Multimodal Explanations: Justifying Decisions and Pointing to the Evidence

Seth-Park/MultimodalExplanations CVPR 2018

We propose a multimodal approach to explanation, and argue that the two modalities provide complementary explanatory strengths.

Variable Selection with Copula Entropy

majianthu/aps2020 28 Oct 2019

It is believed that CE based variable selection can help to build more explainable models.

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

samuelkim314/DeepSymReg 10 Dec 2019

We find that the EQL-based architecture can extrapolate quite well outside of the training data set compared to a standard neural network-based architecture, paving the way for deep learning to be applied in scientific exploration and discovery.

Retrieving and Highlighting Action with Spatiotemporal Reference

yiskw713/yiskw713 19 May 2020

In this paper, we present a framework that jointly retrieves and spatiotemporally highlights actions in videos by enhancing current deep cross-modal retrieval methods.

SegNBDT: Visual Decision Rules for Segmentation

daniel-ho/SegNBDT 11 Jun 2020

To address this, prior work combines neural networks with decision trees.

EXPLAN: Explaining Black-box Classifiers using Adaptive Neighborhood Generation

peymanrasouli/EXPLAN 2020 International Joint Conference on Neural Networks (IJCNN) 2020

Defining a representative locality is an urgent challenge in perturbation-based explanation methods, which influences the fidelity and soundness of explanations.

Towards Musically Meaningful Explanations Using Source Separation

CPJKU/audioLIME 4 Sep 2020

Prior work on explainable models in MIR has generally used image processing tools to produce explanations for DNN predictions, but these are not necessarily musically meaningful, or can be listened to (which, arguably, is important in music).

CDT: Cascading Decision Trees for Explainable Reinforcement Learning

quantumiracle/Cascading-Decision-Tree 15 Nov 2020

As a second contribution our study reveals limitations of explaining black-box policies via imitation learning with tree-based explainable models, due to its inherent instability.

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

shuheng-li/units-sensory-time-series-classification 23 Nov 2020

Time series data is prevalent in a wide variety of real-world applications and it calls for trustworthy and explainable models for people to understand and fully trust decisions made by AI solutions.