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Interpretable Machine Learning
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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
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Leveraging Context for Multimodal Fallacy Classification in Political Debates
Discovering multiple antibiotic resistance phenotypes using diverse top-k subgroup list discovery
MT2-CSD: A New Dataset and Multi-Semantic Knowledge Fusion Method for Conversational Stance Detection
Can "consciousness" be observed from large language model (LLM) internal states? Dissecting LLM representations obtained from Theory of Mind test with Integrated Information Theory and Span Representation analysis
The Most Important Features in Generalized Additive Models Might Be Groups of Features
Leveraging Predictive Equivalence in Decision Trees
ELLIS Alicante at CQs-Gen 2025: Winning the critical thinking questions shared task: LLM-based question generation and selection
Risk Estimation of Knee Osteoarthritis Progression via Predictive Multi-task Modelling from Efficient Diffusion Model using X-ray Images
Interpretable representation learning of quantum data enabled by probabilistic variational autoencoders
An Interpretable Machine Learning Approach in Predicting Inflation Using Payments System Data: A Case Study of Indonesia
An Attention-based Spatio-Temporal Neural Operator for Evolving Physics
midr: Learning from Black-Box Models by Maximum Interpretation Decomposition
Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data
Explainable-AI powered stock price prediction using time series transformers: A Case Study on BIST100
LLMs for Argument Mining: Detection, Extraction, and Relationship Classification of pre-defined Arguments in Online Comments
What About Emotions? Guiding Fine-Grained Emotion Extraction from Mobile App Reviews
Data Model Design for Explainable Machine Learning-based Electricity Applications
Interpretable Machine Learning for Macro Alpha: A News Sentiment Case Study
Can Large Language Models be Effective Online Opinion Miners?
Are machine learning interpretations reliable? A stability study on global interpretations
PL-FGSA: A Prompt Learning Framework for Fine-Grained Sentiment Analysis Based on MindSpore
Machine Learning-Based Prediction of Mortality in Geriatric Traumatic Brain Injury Patients
Advancing Tabular Stroke Modelling Through a Novel Hybrid Architecture and Feature-Selection Synergy
Towards Comprehensive Argument Analysis in Education: Dataset, Tasks, and Method
On the definition and importance of interpretability in scientific machine learning
Enhanced Photonic Chip Design via Interpretable Machine Learning Techniques
LLM4CD: Leveraging Large Language Models for Open-World Knowledge Augmented Cognitive Diagnosis
Understanding molecular ratios in the carbon and oxygen poor outer Milky Way with interpretable machine learning
Manifold Learning with Normalizing Flows: Towards Regularity, Expressivity and Iso-Riemannian Geometry
Navigating the Rashomon Effect: How Personalization Can Help Adjust Interpretable Machine Learning Models to Individual Users
Attention Mechanisms in Dynamical Systems: A Case Study with Predator-Prey Models
Exploring new Approaches for Information Retrieval through Natural Language Processing
Towards Probabilistic Dynamic Security Assessment and Enhancement of Large Power Systems
Interpretable machine learning-guided design of Fe-based soft magnetic alloys
NFISiS: New Perspectives on Fuzzy Inference Systems for Renewable Energy Forecasting
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