Papers Explainable Models
“Explainable Models” 태그가 달린 논문 128편 · 필터 해제
EXTRA: Explanation Ranking Datasets for Explainable Recommendation
Recently, research on explainable recommender systems has drawn much attention from both academia and industry, resulting in a variety of explainable models. As a consequence, their evaluation approaches vary from model …
Explainable ModelsExplainable RecommendationRecommendation SystemsTripletInvestigating ADR mechanisms with knowledge graph mining and explainable AI
Adverse Drug Reactions (ADRs) are characterized within randomized clinical trials and postmarketing pharmacovigilance, but their molecular mechanism remains unknown in most cases. Aside from clinical trials, many element…
Explainable ModelsGraph MiningKnowledge GraphsPharmacovigilanceRelational Boosted Bandits
Contextual bandits algorithms have become essential in real-world user interaction problems in recent years. However, these algorithms rely on context as attribute value representation, which makes them unfeasible for re…
AttributeDescriptiveExplainable ModelsLink Prediction+1Dependency Decomposition and a Reject Option for Explainable Models
Deploying machine learning models in safety-related do-mains (e.g. autonomous driving, medical diagnosis) demands for approaches that are explainable, robust against adversarial attacks and aware of the model uncertainty…
Autonomous DrivingExplainable Modelsimage-classificationImage Classification+1Learning Universal Shape Dictionary for Realtime Instance Segmentation
We present a novel explicit shape representation for instance segmentation. Based on how to model the object shape, current instance segmentation systems can be divided into two categories, implicit and explicit models. …
Explainable ModelsGPUInstance SegmentationObject+2A Language-Based Approach to Fake News Detection Through Interpretable Features and BRNN
‘Fake news’ – succinctly defined as false or misleading information masquerading as legitimate news – is a ubiquitous phenomenon and its dissemination weakens the fact-based reporting of the established news industry, ma…
Explainable ModelsFake News DetectionWord EmbeddingsLearning Explainable Interventions to Mitigate HIV Transmission in Sex Workers Across Five States in India
Female sex workers(FSWs) are one of the most vulnerable and stigmatized groups in society. As a result, they often suffer from a lack of quality access to care. Grassroot organizations engaged in improving health service…
Explainable ModelsSpecificityxFraud: Explainable Fraud Transaction Detection
At online retail platforms, it is crucial to actively detect the risks of transactions to improve customer experience and minimize financial loss. In this work, we propose xFraud, an explainable fraud transaction predict…
Explainable ModelsFraud DetectionGraph EmbeddingGraph Neural NetworkExplainable Multivariate Time Series Classification: A Deep Neural Network Which Learns To Attend To Important Variables As Well As Informative Time Intervals
Time series data is prevalent in a wide variety of real-world applications and it calls for trustworthy and explainable models for people to understand and fully trust decisions made by AI solutions. We consider the prob…
Explainable ModelsGeneral ClassificationTime SeriesTime Series Analysis+1CDT: Cascading Decision Trees for Explainable Reinforcement Learning
Deep Reinforcement Learning (DRL) has recently achieved significant advances in various domains. However, explaining the policy of RL agents still remains an open problem due to several factors, one being the complexity …
Deep Reinforcement LearningExplainable ModelsImitation Learningreinforcement-learning+3Towards Musically Meaningful Explanations Using Source Separation
Deep neural networks (DNNs) are successfully applied in a wide variety of music information retrieval (MIR) tasks. Such models are usually considered "black boxes", meaning that their predictions are not interpretable. P…
Explainable ModelsImage SegmentationInformation RetrievalMusic Information Retrieval+2EXPLAN: Explaining Black-box Classifiers using Adaptive Neighborhood Generation
Defining a representative locality is an urgent challenge in perturbation-based explanation methods, which influences the fidelity and soundness of explanations. We address this issue by proposing a robust and intuitive …
Explainable artificial intelligenceExplainable ModelsExplanation Fidelity EvaluationLearning to learn generative programs with Memoised Wake-Sleep
We study a class of neuro-symbolic generative models in which neural networks are used both for inference and as priors over symbolic, data-generating programs. As generative models, these programs capture compositional …
Explainable ModelsFew-Shot LearningProgram inductionContext-based Automated Scoring of Complex Mathematical Responses
The tasks of automatically scoring either textual or algebraic responses to mathematical questions have both been well-studied, albeit separately. In this paper we propose a method for automatically scoring responses tha…
Explainable ModelsCrop Yield Prediction Integrating Genotype and Weather Variables Using Deep Learning
Accurate prediction of crop yield supported by scientific and domain-relevant insights, can help improve agricultural breeding, provide monitoring across diverse climatic conditions and thereby protect against climatic c…
Crop Yield PredictionExplainable ModelsTime Series AnalysisSegNBDT: Visual Decision Rules for Segmentation
The black-box nature of neural networks limits model decision interpretability, in particular for high-dimensional inputs in computer vision and for dense pixel prediction tasks like segmentation. To address this, prior …
Explainable Modelsimage-classificationImage ClassificationSegmentationRetrieving and Highlighting Action with Spatiotemporal Reference
In this paper, we present a framework that jointly retrieves and spatiotemporally highlights actions in videos by enhancing current deep cross-modal retrieval methods. Our work takes on the novel task of action highlight…
Action RecognitionCross-Modal RetrievalExplainable ModelsRetrieval+3The Pragmatic Turn in Explainable Artificial Intelligence (XAI)
In this paper I argue that the search for explainable models and interpretable decisions in AI must be reformulated in terms of the broader project of offering a pragmatic and naturalistic account of understanding in AI.…
BIG-bench Machine LearningExplainable artificial intelligenceExplainable Artificial Intelligence (XAI)Explainable ModelsExplainable Machine Learning Control -- robust control and stability analysis
Recently, the term explainable AI became known as an approach to produce models from artificial intelligence which allow interpretation. Since a long time, there are models of symbolic regression in use that are perfectl…
BIG-bench Machine LearningExplainable ModelsregressionSymbolic RegressionAAAI FSS-19: Human-Centered AI: Trustworthiness of AI Models and Data Proceedings
To facilitate the widespread acceptance of AI systems guiding decision-making in real-world applications, it is key that solutions comprise trustworthy, integrated human-AI systems. Not only in safety-critical applicatio…
Autonomous DrivingDecision MakingExplainable Models