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Explainable Artificial Intelligence (XAI)

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하위 태스크: 1
평가표: 1
보존된 평가 기록: 1

Explainable Artificial Intelligence

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/josesousaribeiro/exirt-xai-benchmark

공식 코드

Explanations Based on Item Response Theory (eXirt): A Model-Specific Method to Explain Tree-Ensemble Model in Trust Perspective

https://github.com/josesousaribeiro/exirt-xai-pipeline

공식 코드

Explanations Based on Item Response Theory (eXirt): A Model-Specific Method to Explain Tree-Ensemble Model in Trust Perspective

https://github.com/josesousaribeiro/pred2town-and-xai

공식 코드

Black Box Model Explanations and the Human Interpretability Expectations -- An Analysis in the Context of Homicide Prediction

https://github.com/jphall663/kdd_2019

공식 코드

Proposed Guidelines for the Responsible Use of Explainable Machine Learning

https://github.com/jphall663/responsible_xai

공식 코드

Proposed Guidelines for the Responsible Use of Explainable Machine Learning

https://github.com/jtbish/gecco22

공식 코드

Pittsburgh Learning Classifier Systems for Explainable Reinforcement Learning: Comparing with XCS

https://github.com/jvielhaben/mcd-xai

공식 코드

Multi-dimensional concept discovery (MCD): A unifying framework with completeness guarantees

https://github.com/kahramankostas/IoTGeM

공식 코드

IoTGeM: Generalizable Models for Behaviour-Based IoT Attack Detection

https://github.com/kahramankostas/heriot

공식 코드

IoTGeM: Generalizable Models for Behaviour-Based IoT Attack Detection

https://github.com/karim-53/compare-xai

공식 코드

Do We Need Another Explainable AI Method? Toward Unifying Post-hoc XAI Evaluation Methods into an Interactive and Multi-dimensional Benchmark

https://github.com/karyframling/ajcai_2021

공식 코드

Contextual Importance and Utility: aTheoretical Foundation

https://github.com/ksiminski/neuro-fuzzy-library

공식 코드

Automatic Extraction of Linguistic Description from Fuzzy Rule Base

https://github.com/kurtbutler/2024_attributions_paper

공식 코드

Explainable Learning with Gaussian Processes

https://github.com/lapalap/B-LRP

공식 코드

How Much Can I Trust You? -- Quantifying Uncertainties in Explaining Neural Networks

https://github.com/leauferreira/GpX

공식 코드

Applying Genetic Programming to Improve Interpretability in Machine Learning Models

https://github.com/leduckhai/wearable_ldf-fs

공식 코드

A Wearable Device Dataset for Mental Health Assessment Using Laser Doppler Flowmetry and Fluorescence Spectroscopy Sensors

https://github.com/leonchou5311/counterfactual-benchmark

공식 코드

Benchmarking Instance-Centric Counterfactual Algorithms for XAI: From White Box to Black Box

https://github.com/limorigu/LENS

공식 코드

Local Explanations via Necessity and Sufficiency: Unifying Theory and Practice

https://github.com/llm-as-dataset-analyst/SSDLLM

공식 코드

LLM as Dataset Analyst: Subpopulation Structure Discovery with Large Language Model

https://github.com/llnl/xnas

공식 코드

Learning Interpretable Models Through Multi-Objective Neural Architecture Search

https://github.com/lstate/explainability-in-practice

공식 코드

Explainability in Practice: Estimating Electrification Rates from Mobile Phone Data in Senegal

https://github.com/lucacoma/XAISrcLoc

공식 코드

Interpreting End-to-End Deep Learning Models for Speech Source Localization Using Layer-wise Relevance Propagation

https://github.com/lucasferrarocardoso/irt_xai

공식 코드

Explanation-by-Example Based on Item Response Theory

https://github.com/maikejulie/DNN4Cli/tree/main/THOR/BayesianApproach

공식 코드

Explainable Artificial Intelligence for Bayesian Neural Networks: Towards trustworthy predictions of ocean dynamics

https://github.com/mariorefoyo/multi-space

공식 코드

Multi-SpaCE: Multi-Objective Subsequence-based Sparse Counterfactual Explanations for Multivariate Time Series Classification

https://github.com/mariuskrumm/manybodymeps

공식 코드

Multi-Excitation Projective Simulation with a Many-Body Physics Inspired Inductive Bias

https://github.com/markuswenzel/xai-proteins

공식 코드

Insights Into the Inner Workings of Transformer Models for Protein Function Prediction

https://github.com/marti5ini/calime

공식 코드

Causality-Aware Local Interpretable Model-Agnostic Explanations

https://github.com/maxdreyer/crp-human-study

공식 코드

From Attribution Maps to Human-Understandable Explanations through Concept Relevance Propagation

https://github.com/maxdreyer/reveal2revise

공식 코드

Reveal to Revise: An Explainable AI Life Cycle for Iterative Bias Correction of Deep Models

https://github.com/meiyor/deep-learning-emotion-decoding-using-eeg-data-from-autism-individuals

공식 코드

Evaluation of Interpretability for Deep Learning algorithms in EEG Emotion Recognition: A case study in Autism

https://github.com/mi2datalab/rashomon-detect

공식 코드

Exploration of the Rashomon Set Assists Trustworthy Explanations for Medical Data

https://github.com/miquelmn/aixi-dataset

공식 코드

Assessing Fidelity in XAI post-hoc techniques: A Comparative Study with Ground Truth Explanations Datasets

https://github.com/mmschlk/ipdp-on-partial-dependence-plots-in-dynamic-modeling-scenarios

공식 코드

iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios

https://github.com/mmschlk/treeshap-iq

공식 코드

Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles

https://github.com/moein-shariatnia/OpenAI-CLIP

공식 코드

Domino: Discovering Systematic Errors with Cross-Modal Embeddings

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.