Papers Explainable Models
“Explainable Models” 태그가 달린 논문 128편 · 필터 해제
Prototypical Self-Explainable Models Without Re-training
Explainable AI (XAI) has unfolded in two distinct research directions with, on the one hand, post-hoc methods that explain the predictions of a pre-trained black-box model and, on the other hand, self-explainable models …
Decision MakingExplainable ModelsExplaining black boxes with a SMILE: Statistical Model-agnostic Interpretability with Local Explanations
Machine learning is currently undergoing an explosion in capability, popularity, and sophistication. However, one of the major barriers to widespread acceptance of machine learning (ML) is trustworthiness: most ML models…
Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)Explainable ModelsInterpretable Machine LearningA Learnable Counter-condition Analysis Framework for Functional Connectivity-based Neurological Disorder Diagnosis
To understand the biological characteristics of neurological disorders with functional connectivity (FC), recent studies have widely utilized deep learning-based models to identify the disease and conducted post-hoc anal…
DiagnosticExplainable Modelsfeature selectionFunctional ConnectivityStable and Interpretable Deep Learning for Tabular Data: Introducing InterpreTabNet with the Novel InterpreStability Metric
As Artificial Intelligence (AI) integrates deeper into diverse sectors, the quest for powerful models has intensified. While significant strides have been made in boosting model capabilities and their applicability acros…
Decision MakingExplainable ModelsExplainable machine learning identifies multi-omics signatures of muscle response to spaceflight in mice
The adverse effects of microgravity exposure on mammalian physiology during spaceflight necessitate a deep understanding of the underlying mechanisms to develop effective countermeasures. One such concern is muscle atrop…
Explainable ModelsSymbolic RegressionHyperbolic Convolutional Neural Networks
Deep Learning is mostly responsible for the surge of interest in Artificial Intelligence in the last decade. So far, deep learning researchers have been particularly successful in the domain of image processing, where Co…
Explainable Modelsimage-classificationImage ClassificationInductive BiasExplainable AI for clinical risk prediction: a survey of concepts, methods, and modalities
Recent advancements in AI applications to healthcare have shown incredible promise in surpassing human performance in diagnosis and disease prognosis. With the increasing complexity of AI models, however, concerns regard…
Decision MakingExplainable ModelsFairnessPrediction+1A Comprehensive Overview of Computational Nuclei Segmentation Methods in Digital Pathology
In the cancer diagnosis pipeline, digital pathology plays an instrumental role in the identification, staging, and grading of malignant areas on biopsy tissue specimens. High resolution histology images are subject to hi…
Explainable ModelsChallenges and Opportunities for Second-life Batteries: A Review of Key Technologies and Economy
Due to the increasing volume of Electric Vehicles in automotive markets and the limited lifetime of onboard lithium-ion batteries (LIBs), the large-scale retirement of LIBs is imminent. The battery packs retired from Ele…
Explainable ModelsTowards eXplainable AI for Mobility Data Science
This paper presents our ongoing work towards XAI for Mobility Data Science applications, focusing on explainable models that can learn from dense trajectory data, such as GPS tracks of vehicles and vessels using temporal…
Explainable Artificial Intelligence (XAI)Explainable ModelsInterpreting GNN-based IDS Detections Using Provenance Graph Structural Features
Advanced cyber threats (e.g., Fileless Malware and Advanced Persistent Threat (APT)) have driven the adoption of provenance-based security solutions. These solutions employ Machine Learning (ML) models for behavioral mod…
Anomaly DetectionDecision MakingDescriptiveExplainable Models+3Interpreting Deep Forest through Feature Contribution and MDI Feature Importance
Deep forest is a non-differentiable deep model which has achieved impressive empirical success across a wide variety of applications, especially on categorical/symbolic or mixed modeling tasks. Many of the application fi…
Explainable ModelsFeature ImportanceGENIE-NF-AI: Identifying Neurofibromatosis Tumors using Liquid Neural Network (LTC) trained on AACR GENIE Datasets
In recent years, the field of medicine has been increasingly adopting artificial intelligence (AI) technologies to provide faster and more accurate disease detection, prediction, and assessment. In this study, we propose…
Explainable ModelsThe State of Human-centered NLP Technology for Fact-checking
Misinformation threatens modern society by promoting distrust in science, changing narratives in public health, heightening social polarization, and disrupting democratic elections and financial markets, among a myriad o…
Explainable ModelsFact CheckingMisinformationMultimodal and Explainable Internet Meme Classification
In the current context where online platforms have been effectively weaponized in a variety of geo-political events and social issues, Internet memes make fair content moderation at scale even more difficult. Existing wo…
ClassificationExplainable ModelsHate Speech DetectionMeme ClassificationPrototype-based Interpretable Graph Neural Networks
Graph neural networks have proved to be a key tool for dealing with many problems and domains such as chemistry, natural language processing and social networks. While the structure of the layers is simple, it is difficu…
Explainable ModelsGraph Neural NetworkNode ClassificationTowards Human-centered Explainable AI: A Survey of User Studies for Model Explanations
Explainable AI (XAI) is widely viewed as a sine qua non for ever-expanding AI research. A better understanding of the needs of XAI users, as well as human-centered evaluations of explainable models are both a necessity a…
Explainable Artificial Intelligence (XAI)Explainable ModelsFairnessRecommendation Systems+1The Ability of Image-Language Explainable Models to Resemble Domain Expertise
Recent advances in vision and language (V+L) models have a promising impact in the healthcare field. However, such models struggle to explain how and why a particular decision was made. In addition, model transparency an…
Explainable ModelsBilevel Optimization for Feature Selection in the Data-Driven Newsvendor Problem
We study the feature-based newsvendor problem, in which a decision-maker has access to historical data consisting of demand observations and exogenous features. In this setting, we investigate feature selection, aiming t…
Bilevel OptimizationExplainable Modelsfeature selectionSim2Word: Explaining Similarity with Representative Attribute Words via Counterfactual Explanations
Recently, we have witnessed substantial success using the deep neural network in many tasks. While there still exists concerns about the explainability of decision-making, it is beneficial for users to discern the defect…
AttributecounterfactualCounterfactual ExplanationDecision Making+4