Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
Copy Recurrent Neural Network Structure Network
Electronic Health Record (EHR) coding involves automatically classifying EHRs into diagnostic codes. While most previous research treats this as a multi-label classification task, generating probabilities for each code a…
DiagnosticMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONPINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation
The eXtreme Multi-label Classification~(XMC) problem seeks to find relevant labels from an exceptionally large label space. Most of the existing XMC learners focus on the extraction of semantic features from input query …
Extreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONRecommendation SystemsF-PABEE: Flexible-patience-based Early Exiting for Single-label and Multi-label text Classification Tasks
Computational complexity and overthinking problems have become the bottlenecks for pre-training language models (PLMs) with millions or even trillions of parameters. A Flexible-Patience-Based Early Exiting method (F-PABE…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti Label Text ClassificationMulti-Label Text Classification+2sustain.AI: a Recommender System to analyze Sustainability Reports
We present sustainAI, an intelligent, context-aware recommender system that assists auditors and financial investors as well as the general public to efficiently analyze companies' sustainability reports. The tool levera…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONRecommendation SystemsDish detection in food platters: A framework for automated diet logging and nutrition management
Diet is central to the epidemic of lifestyle disorders. Accurate and effortless diet logging is one of the significant bottlenecks for effective diet management and calorie restriction. Dish detection from food platters …
Food recommendationManagementMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+4Semantic Embedded Deep Neural Network: A Generic Approach to Boost Multi-Label Image Classification Performance
Fine-grained multi-label classification models have broad applications in e-commerce, such as visual based label predictions ranging from fashion attribute detection to brand recognition. One challenge to achieve satisfa…
AttributeClassificationimage-classificationImage Classification+3Minimal Learning Machine for Multi-Label Learning
Distance-based supervised method, the minimal learning machine, constructs a predictive model from data by learning a mapping between input and output distance matrices. In this paper, we propose new methods and evaluate…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label LearningAn Exploration of Encoder-Decoder Approaches to Multi-Label Classification for Legal and Biomedical Text
Standard methods for multi-label text classification largely rely on encoder-only pre-trained language models, whereas encoder-decoder models have proven more effective in other classification tasks. In this study, we co…
ClassificationDecoderMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+4Data Efficient Training with Imbalanced Label Sample Distribution for Fashion Detection
Multi-label classification models have a wide range of applications in E-commerce, including visual-based label predictions and language-based sentiment classifications. A major challenge in achieving satisfactory perfor…
AttributeMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONAuxiliary Label Embedding for Multi-label Learning with Missing Labels
Label correlation has been exploited for multi-label learning in different ways. Existing approaches presume that label correlation information is available as a prior, but for multi-label datasets having incomplete labe…
Missing LabelsMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label LearningHarnessing the Power of BERT in the Turkish Clinical Domain: Pretraining Approaches for Limited Data Scenarios
In recent years, major advancements in natural language processing (NLP) have been driven by the emergence of large language models (LLMs), which have significantly revolutionized research and development within the fiel…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONUsing Language Models on Low-end Hardware
This paper evaluates the viability of using fixed language models for training text classification networks on low-end hardware. We combine language models with a CNN architecture and put together a comprehensive benchma…
ClassificationLanguage ModelingLanguage ModellingMulti-Label Classification+3Mimic-IV-ICD: A new benchmark for eXtreme MultiLabel Classification
Clinical notes are assigned ICD codes - sets of codes for diagnoses and procedures. In the recent years, predictive machine learning models have been built for automatic ICD coding. However, there is a lack of widely acc…
Multi-Label ClassificationGeometric Relational Embeddings: A Survey
Geometric relational embeddings map relational data as geometric objects that combine vector information suitable for machine learning and structured/relational information for structured/relational reasoning, typically …
Hierarchical Multi-label ClassificationKnowledge Graph CompletionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2Extreme Classification for Answer Type Prediction in Question Answering
Semantic answer type prediction (SMART) is known to be a useful step towards effective question answering (QA) systems. The SMART task involves predicting the top-$k$ knowledge graph (KG) types for a given natural langua…
ClassificationClusteringExtreme Multi-Label ClassificationMulti-Label Classification+5Learning in Imperfect Environment: Multi-Label Classification with Long-Tailed Distribution and Partial Labels
Conventional multi-label classification (MLC) methods assume that all samples are fully labeled and identically distributed. Unfortunately, this assumption is unrealistic in large-scale MLC data that has long-tailed (LT)…
Missing LabelsMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONPhilosophyBent & Broken Bicycles: Leveraging synthetic data for damaged object re-identification
Instance-level object re-identification is a fundamental computer vision task, with applications from image retrieval to intelligent monitoring and fraud detection. In this work, we propose the novel task of damaged obje…
Fraud DetectionImage RetrievalMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2How you feelin'? Learning Emotions and Mental States in Movie Scenes
Movie story analysis requires understanding characters' emotions and mental states. Towards this goal, we formulate emotion understanding as predicting a diverse and multi-label set of emotions at the level of a movie sc…
Emotion RecognitionMulti-Label ClassificationEvaluation of ChatGPT Model for Vulnerability Detection
In this technical report, we evaluated the performance of the ChatGPT and GPT-3 models for the task of vulnerability detection in code. Our evaluation was conducted on our real-world dataset, using binary and multi-label…
ClassificationmodelMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Asymmetric Polynomial Loss For Multi-Label Classification
Various tasks are reformulated as multi-label classification problems, in which the binary cross-entropy (BCE) loss is frequently utilized for optimizing well-designed models. However, the vanilla BCE loss cannot be tail…
Classificationimage-classificationImage ClassificationMulti-Label Classification+4