Papers Interpretable Machine Learning
“Interpretable Machine Learning” 태그가 달린 논문 632편 · 필터 해제
Interpretable Data Mining of Follicular Thyroid Cancer Ultrasound Features Using Enhanced Association Rules
Purpose: Thyroid cancer has been a common cancer. Papillary thyroid cancer and follicular thyroid cancer are the two most common types of thyroid cancer. Follicular thyroid cancer lacks distinctive ultrasound signs and i…
Interpretable Machine LearningTabular Diffusion Counterfactual Explanations
Counterfactual explanations methods provide an important tool in the field of {interpretable machine learning}. Recent advances in this direction have focused on diffusion models to explain a deep classifier. However, th…
Interpretable Machine LearningApproximate Bayesian Inference via Bitstring Representations
The machine learning community has recently put effort into quantized or low-precision arithmetics to scale large models. This paper proposes performing probabilistic inference in the quantized, discrete parameter space …
Interpretable Machine LearningBayesian InferenceInterpretable Machine Learning Model for Early Prediction of Acute Kidney Injury in Critically Ill Patients with Cirrhosis: A Retrospective Study
Background: Cirrhosis is a progressive liver disease with high mortality and frequent complications, notably acute kidney injury (AKI), which occurs in up to 50% of hospitalized patients and worsens outcomes. AKI stems f…
Interpretable Machine LearningMachines Learn Number Fields, But How? The Case of Galois Groups
By applying interpretable machine learning methods such as decision trees, we study how simple models can classify the Galois groups of Galois extensions over $\mathbb{Q}$ of degrees 4, 6, 8, 9, and 10, using Dedekind ze…
Interpretable Machine LearningQuantum Spectral Reasoning: A Non-Neural Architecture for Interpretable Machine Learning
We propose a novel machine learning architecture that departs from conventional neural network paradigms by leveraging quantum spectral methods, specifically Pade approximants and the Lanczos algorithm, for interpretable…
Interpretable Machine LearningAnomaly DetectionData-Driven Discovery of Mobility Periodicity for Understanding Urban Systems
Human mobility regularity is crucial for understanding urban dynamics and informing decision-making processes. This study first quantifies the periodicity in complex human mobility data as a sparse identification of domi…
Interpretable Machine LearningOperator-Based Machine Intelligence: A Hilbert Space Framework for Spectral Learning and Symbolic Reasoning
Traditional machine learning models, particularly neural networks, are rooted in finite-dimensional parameter spaces and nonlinear function approximations. This report explores an alternative formulation where learning t…
Interpretable Machine LearningEarly Mortality Prediction in ICU Patients with Hypertensive Kidney Disease Using Interpretable Machine Learning
Background: Hypertensive kidney disease (HKD) patients in intensive care units (ICUs) face high short-term mortality, but tailored risk prediction tools are lacking. Early identification of high-risk individuals is cruci…
Interpretable Machine LearningMortality PredictionHelix 1.0: An Open-Source Framework for Reproducible and Interpretable Machine Learning on Tabular Scientific Data
Helix is an open-source, extensible, Python-based software framework to facilitate reproducible and interpretable machine learning workflows for tabular data. It addresses the growing need for transparent experimental da…
Interpretable Machine LearningFeature Engineering is Not Dead: Reviving Classical Machine Learning with Entropy, HOG, and LBP Feature Fusion for Image Classification
Feature engineering continues to play a critical role in image classification, particularly when interpretability and computational efficiency are prioritized over deep learning models with millions of parameters. In thi…
Interpretable Machine LearningComputational EfficiencyTime Series AnalysisImage ClassificationTopological Social Choice: Designing a Noise-Robust Polar Distance for Persistence Diagrams
Topological Data Analysis (TDA) has emerged as a powerful framework for extracting robust and interpretable features from noisy high-dimensional data. In the context of Social Choice Theory, where preference profiles and…
Interpretable Machine LearningComprehensive Evaluation of Prototype Neural Networks
Prototype models are an important method for explainable artificial intelligence (XAI) and interpretable machine learning. In this paper, we perform an in-depth analysis of a set of prominent prototype models including P…
Interpretable Machine LearningMulti-Label ClassificationInterpretable Machine Learning for Urban Heat Mitigation: Attribution and Weighting of Multi-Scale Drivers
Urban heat islands (UHIs) are often accentuated during heat waves (HWs) and pose a public health risk. Mitigating UHIs requires urban planners to first estimate how urban heat is influenced by different land use types (L…
Interpretable Machine LearningInformation-theoretic Quantification of High-order Feature Effects in Classification Problems
Understanding the contribution of individual features in predictive models remains a central goal in interpretable machine learning, and while many model-agnostic methods exist to estimate feature importance, they often …
Interpretable Machine LearningFeature ImportanceCan "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
Integrated Information Theory (IIT) provides a quantitative framework for explaining consciousness phenomenon, positing that conscious systems comprise elements integrated through causal properties. We apply IIT 3.0 and …
Explainable Artificial Intelligence (XAI)Interpretable Machine LearningLanguage ModelingLanguage Modelling+1The Most Important Features in Generalized Additive Models Might Be Groups of Features
While analyzing the importance of features has become ubiquitous in interpretable machine learning, the joint signal from a group of related features is sometimes overlooked or inadvertently excluded. Neglecting the join…
Additive modelsInterpretable Machine LearningRisk Estimation of Knee Osteoarthritis Progression via Predictive Multi-task Modelling from Efficient Diffusion Model using X-ray Images
Medical imaging plays a crucial role in assessing knee osteoarthritis (OA) risk by enabling early detection and disease monitoring. Recent machine learning methods have improved risk estimation (i.e., predicting the like…
Image GenerationInterpretable Machine LearningLeveraging Predictive Equivalence in Decision Trees
Decision trees are widely used for interpretable machine learning due to their clearly structured reasoning process. However, this structure belies a challenge we refer to as predictive equivalence: a given tree's decisi…
Interpretable Machine LearningMissing ValuesModel SelectionInterpretable representation learning of quantum data enabled by probabilistic variational autoencoders
Interpretable machine learning is rapidly becoming a crucial tool for scientific discovery. Among existing approaches, variational autoencoders (VAEs) have shown promise in extracting the hidden physical features of some…
Interpretable Machine LearningRepresentation Learningscientific discovery