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Interpretable Machine Learning

The goal of Interpretable Machine Learning is to allow oversight and understanding of machine-learned decisions. Much of the work in Interpretable Machine Learning has come in the form of devising methods to better explain the predictions of machine learning models.

Source: Assessing the Local Interpretability of Machine Learning Models

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 451–475 of 537 papers

TitleStatusHype
Exploring Interpretability for Predictive Process Analytics—0
Temporal Fusion Transformers for Interpretable Multi-horizon Time Series ForecastingCode3
Towards personalized diagnosis of Glioblastoma in Fluid-attenuated inversion recovery (FLAIR) by topological interpretable machine learning—0
A Decision-Theoretic Approach for Model Interpretability in Bayesian FrameworkCode0
Topological data analysis of zebrafish patterns—0
Understanding Deep Networks via Extremal Perturbations and Smooth MasksCode1
Bayesian Learning-Based Adaptive Control for Safety Critical SystemsCode0
Interpretable Convolutional Neural Networks for Preterm Birth Classification—0
MonoNet: Towards Interpretable Models by Learning Monotonic Features—0
MGP-AttTCN: An Interpretable Machine Learning Model for the Prediction of SepsisCode0
Explaining A Black-box By Using A Deep Variational Information Bottleneck Approach—0
Accurate and interpretable evaluation of surgical skills from kinematic data using fully convolutional neural networksCode0
The Partial Response Network: a neural network nomogram—0
Detecting Heterogeneous Treatment Effect with Instrumental Variables—0
Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch NormalizationCode0
Evaluating Explanation Without Ground Truth in Interpretable Machine Learning—0
Improving performance of deep learning models with axiomatic attribution priors and expected gradientsCode1
Model Bridging: Connection between Simulation Model and Neural Network—0
Trepan Reloaded: A Knowledge-driven Approach to Explaining Artificial Neural Networks—0
Adversarial Explanations for Understanding Image Classification Decisions and Improved Neural Network RobustnessCode0
A hybrid machine learning framework for analyzing human decision making through learning preferences—0
Regularizing Black-box Models for Improved Interpretability (HILL 2019 Version)—0
Disentangled Attribution Curves for Interpreting Random Forests and Boosted TreesCode1
Fast classification of small X-ray diffraction datasets using data augmentation and deep neural networksCode0
Hybrid Predictive Model: When an Interpretable Model Collaborates with a Black-box Model—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Q-SENNTop 1 Accuracy85.9—Unverified
2SLDD-ModelTop 1 Accuracy85.7—Unverified