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 101–110 of 537 papers

TitleStatusHype
On the Shape of Brainscores for Large Language Models (LLMs)—0
Mathematics of statistical sequential decision-making: concentration, risk-awareness and modelling in stochastic bandits, with applications to bariatric surgery—0
Rapid Shear Capacity Prediction of TRM-Strengthened Unreinforced Masonry Walls through Interpretable Machine Learning using a Web App—0
LLM-SR: Scientific Equation Discovery via Programming with Large Language ModelsCode1
Feature graphs for interpretable unsupervised tree ensembles: centrality, interaction, and application in disease subtyping—0
Online Learning of Decision Trees with Thompson SamplingCode0
Cycle Life Prediction for Lithium-ion Batteries: Machine Learning and More—0
Comprehensible Artificial Intelligence on Knowledge Graphs: A survey—0
Sparse Concept Bottleneck Models: Gumbel Tricks in Contrastive LearningCode1
Explainable Deep Learning: A Visual Analytics Approach with Transition MatricesCode0
Show:102550
← PrevPage 11 of 54Next →

Benchmark Results

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