Learning the context of a category
This paper outlines a hierarchical Bayesian model for human category learning that learns both the organization of objects into categories, and the context in which this knowledge should be applied. The model is fit to multiple data sets, and provides a parsimonious method for describing how humans learn context specific conceptual representations.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Better Queries for Aspect-Category Sentiment Classification
Aspect-category sentiment classification (ACSC) aims to identify the sentiment polarities towards the aspect categories mentioned in a sentence. Because a sentence often mentions more than one aspect category and express…
Aspect Category DetectionAspect Category Sentiment ClassificationClassificationSentence+2Category-Extensible Out-of-Distribution Detection via Hierarchical Context Descriptions
The key to OOD detection has two aspects: generalized feature representation and precise category description. Recently, vision-language models such as CLIP provide significant advances in both two issues, but constructi…
Out-of-Distribution DetectionPrompt EngineeringContextuality Helps Representation Learning for Generalized Category Discovery
This paper introduces a novel approach to Generalized Category Discovery (GCD) by leveraging the concept of contextuality to enhance the identification and classification of categories in unlabeled datasets. Drawing insp…
Contrastive LearningRepresentation LearningGCE-Pose: Global Context Enhancement for Category-level Object Pose Estimation
A key challenge in model-free category-level pose estimation is the extraction of contextual object features that generalize across varying instances within a specific category. Recent approaches leverage foundational fe…
Pose EstimationSeq2seq Translation Model for Sequential Recommendation
The context information such as product category plays a critical role in sequential recommendation. Recent years have witnessed a growing interest in context-aware sequential recommender systems. Existing studies often …
modelRecommendation SystemsSequential RecommendationTranslation