SOTAVerified

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 451–500 of 537 papers

TitleStatusHype
From Physics-Based Models to Predictive Digital Twins via Interpretable Machine Learning—0
Adversarial Attacks and Defenses: An Interpretation Perspective—0
A machine learning methodology for real-time forecasting of the 2019-2020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic modelsCode0
Ontology-based Interpretable Machine Learning for Textual DataCode0
Interpretable machine learning models: a physics-based view—0
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications—0
Explaining Groups of Points in Low-Dimensional RepresentationsCode0
Interpretability of machine learning based prediction models in healthcare—0
Decoding pedestrian and automated vehicle interactions using immersive virtual reality and interpretable deep learning—0
Interpretable Machine Learning Model for Early Prediction of Mortality in Elderly Patients with Multiple Organ Dysfunction Syndrome (MODS): a Multicenter Retrospective Study and Cross Validation—0
Interpreting Machine Learning Malware Detectors Which Leverage N-gram AnalysisCode0
One Explanation Does Not Fit All: The Promise of Interactive Explanations for Machine Learning Transparency—0
Extending Class Activation Mapping Using Gaussian Receptive Field—0
Explainable Deep Relational Networks for Predicting Compound-Protein Affinities and Contacts—0
Exploring Interpretability for Predictive Process Analytics—0
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
Bayesian Learning-Based Adaptive Control for Safety Critical SystemsCode0
MonoNet: Towards Interpretable Models by Learning Monotonic Features—0
Interpretable Convolutional Neural Networks for Preterm Birth Classification—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
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
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
Full-Gradient Representation for Neural Network VisualizationCode0
Visualization of Convolutional Neural Networks for Monocular Depth EstimationCode0
Open Issues in Combating Fake News: Interpretability as an Opportunity—0
Re-Ranking Words to Improve Interpretability of Automatically Generated TopicsCode0
Explaining a black-box using Deep Variational Information Bottleneck ApproachCode0
Regularizing Black-box Models for Improved InterpretabilityCode0
ProtoAttend: Attention-Based Prototypical LearningCode0
Modeling Heterogeneity in Mode-Switching Behavior Under a Mobility-on-Demand Transit System: An Interpretable Machine Learning Approach—0
Natively Interpretable Machine Learning and Artificial Intelligence: Preliminary Results and Future Directions—0
Comparative Document Summarisation via ClassificationCode0
MLIC: A MaxSAT-Based framework for learning interpretable classification rulesCode0
Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models InsteadCode0
YASENN: Explaining Neural Networks via Partitioning Activation Sequences—0
Interpretable Neural Architectures for Attributing an Ad's Performance to its Writing Style—0
Towards making NLG a voice for interpretable Machine Learning—0
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Benchmark Results

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