Explainable Models
36 papers with code • 0 benchmarks • 2 datasets
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ScienceQA
OpenXAI
Most implemented papers
Discovery of Nonlinear Dynamical Systems using a Runge-Kutta Inspired Dictionary-based Sparse Regression Approach
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
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CVPR 2018
We propose a multimodal approach to explanation, and argue that the two modalities provide complementary explanatory strengths.
Variable Selection with Copula Entropy
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
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
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
To address this, prior work combines neural networks with decision trees.
EXPLAN: Explaining Black-box Classifiers using Adaptive Neighborhood Generation
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
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
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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
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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.
