https://github.com/loadingbyte/timexplain
공식 코드timeXplain -- A Framework for Explaining the Predictions of Time Series Classifiers
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XAI refers to methods and techniques in the application of artificial intelligence (AI) such that the results of the solution can be understood by humans. It contrasts with the concept of the "black box" in machine learning where even its designers cannot explain why an AI arrived at a specific decision. XAI may be an implementation of the social right to explanation. XAI is relevant even if there is no legal right or regulatory requirement—for example, XAI can improve the user experience of a product or service by helping end users trust that the AI is making good decisions. This way the aim of XAI is to explain what has been done, what is done right now, what will be done next and unveil the information the actions are based on. These characteristics make it possible (i) to confirm existing knowledge (ii) to challenge existing knowledge and (iii) to generate new assumptions.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
timeXplain -- A Framework for Explaining the Predictions of Time Series Classifiers
Explainability in Practice: Estimating Electrification Rates from Mobile Phone Data in Senegal
Impact of lung segmentation on the diagnosis and explanation of COVID-19 in chest X-ray images
Multi-Excitation Projective Simulation with a Many-Body Physics Inspired Inductive Bias
Insights Into the Inner Workings of Transformer Models for Protein Function Prediction
Causality-Aware Local Interpretable Model-Agnostic Explanations
Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine Learning
From Attribution Maps to Human-Understandable Explanations through Concept Relevance Propagation
Reveal to Revise: An Explainable AI Life Cycle for Iterative Bias Correction of Deep Models
Explainable expected goal models for performance analysis in football analytics
Exploration of the Rashomon Set Assists Trustworthy Explanations for Medical Data
Model-agnostic explainable artificial intelligence for object detection in image data
iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios
Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles
survex: an R package for explaining machine learning survival models
Calibrated Explanations: with Uncertainty Information and Counterfactuals
CEnt: An Entropy-based Model-agnostic Explainability Framework to Contrast Classifiers' Decisions
Towards explainable artificial intelligence (XAI) for early anticipation of traffic accidents
Introducing User Feedback-based Counterfactual Explanations (UFCE)
Medical Slice Transformer: Improved Diagnosis and Explainability on 3D Medical Images with DINOv2
An Experimental Investigation into the Evaluation of Explainability Methods
EvalAttAI: A Holistic Approach to Evaluating Attribution Maps in Robust and Non-Robust Models
Decoding the Protein-ligand Interactions Using Parallel Graph Neural Networks
SIM-Shapley: A Stable and Computationally Efficient Approach to Shapley Value Approximation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.