Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
MEDFuse: Multimodal EHR Data Fusion with Masked Lab-Test Modeling and Large Language Models
Electronic health records (EHRs) are multimodal by nature, consisting of structured tabular features like lab tests and unstructured clinical notes. In real-life clinical practice, doctors use complementary multimodal EH…
Decision MakingMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSemantic Operators: A Declarative Model for Rich, AI-based Data Processing
The semantic capabilities of large language models (LLMs) have the potential to enable rich analytics and reasoning over vast knowledge corpora. Unfortunately, existing systems either empirically optimize expensive LLM-p…
Extreme Multi-Label ClassificationFact CheckingMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONDomain-Hierarchy Adaptation via Chain of Iterative Reasoning for Few-shot Hierarchical Text Classification
Recently, various pre-trained language models (PLMs) have been proposed to prove their impressive performances on a wide range of few-shot tasks. However, limited by the unstructured prior knowledge in PLMs, it is diffic…
few-shot-htcFew-shot HTCGraph Neural NetworkLanguage Modeling+6Top-K Pairwise Ranking: Bridging the Gap Among Ranking-Based Measures for Multi-Label Classification
Multi-label ranking, which returns multiple top-ranked labels for each instance, has a wide range of applications for visual tasks. Due to its complicated setting, prior arts have proposed various measures to evaluate mo…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-label Learning with Random Circular Vectors
The extreme multi-label classification~(XMC) task involves learning a classifier that can predict from a large label set the most relevant subset of labels for a data instance. While deep neural networks~(DNNs) have demo…
Extreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label LearningOpen-world Multi-label Text Classification with Extremely Weak Supervision
We study open-world multi-label text classification under extremely weak supervision (XWS), where the user only provides a brief description for classification objectives without any labels or ground-truth label space. S…
Keyword ExtractionLanguage ModellingLarge Language ModelMulti-Label Classification+5Noise-Free Explanation for Driving Action Prediction
Although attention mechanisms have achieved considerable progress in Transformer-based architectures across various Artificial Intelligence (AI) domains, their inner workings remain to be explored. Existing explainable m…
Multi-class ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONPredictionTile Compression and Embeddings for Multi-Label Classification in GeoLifeCLEF 2024
We explore methods to solve the multi-label classification task posed by the GeoLifeCLEF 2024 competition with the DS@GT team, which aims to predict the presence and absence of plant species at specific locations using s…
Contrastive LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONUsing LLMs to label medical papers according to the CIViC evidence model
We introduce the sequence classification problem CIViC Evidence to the field of medical NLP. CIViC Evidence denotes the multi-label classification problem of assigning labels of clinical evidence to abstracts of scientif…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONPrompt EngineeringregressionAutomatic register identification for the open web using multilingual deep learning
This article investigates how well deep learning models can identify web registers -- text varieties such as news reports and discussion forums -- across 16 languages. We introduce the Multilingual CORE corpora, which co…
AttributeMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONFedMLP: Federated Multi-Label Medical Image Classification under Task Heterogeneity
Cross-silo federated learning (FL) enables decentralized organizations to collaboratively train models while preserving data privacy and has made significant progress in medical image classification. One common assumptio…
Federated Learningimage-classificationImage ClassificationMedical Image Classification+4Investigating Self-Supervised Methods for Label-Efficient Learning
Vision transformers combined with self-supervised learning have enabled the development of models which scale across large datasets for several downstream tasks like classification, segmentation and detection. The low-sh…
ClassificationClusteringContrastive LearningMulti-class Classification+5Combining Supervised Learning and Reinforcement Learning for Multi-Label Classification Tasks with Partial Labels
Traditional supervised learning heavily relies on human-annotated datasets, especially in data-hungry neural approaches. However, various tasks, especially multi-label tasks like document-level relation extraction, pose …
Document-level Relation Extractionimage-classificationImage ClassificationMulti-Label Classification+4Cherry on the Cake: Fairness is NOT an Optimization Problem
In Fair AI literature, the practice of maliciously creating unfair models that nevertheless satisfy fairness constraints is known as "cherry-picking". A cherry-picking model is a model that makes mistakes on purpose, sel…
FairnessMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONCLIP-Decoder : ZeroShot Multilabel Classification using Multimodal CLIP Aligned Representation
Multi-label classification is an essential task utilized in a wide variety of real-world applications. Multi-label zero-shot learning is a method for classifying images into multiple unseen categories for which no traini…
ClassificationDecoderGeneralized Zero-Shot LearningMulti-Label Classification+5A General Online Algorithm for Optimizing Complex Performance Metrics
We consider sequential maximization of performance metrics that are general functions of a confusion matrix of a classifier (such as precision, F-measure, or G-mean). Such metrics are, in general, non-decomposable over i…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONA data-centric approach for assessing progress of Graph Neural Networks
Graph Neural Networks (GNNs) have achieved state-of-the-art results in node classification tasks. However, most improvements are in multi-class classification, with less focus on the cases where each node could have mult…
ClassificationMulti-class ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1WellDunn: On the Robustness and Explainability of Language Models and Large Language Models in Identifying Wellness Dimensions
Language Models (LMs) are being proposed for mental health applications where the heightened risk of adverse outcomes means predictive performance may not be a sufficient litmus test of a model's utility in clinical prac…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONDecoding the Narratives: Analyzing Personal Drug Experiences Shared on Reddit
Online communities such as drug-related subreddits serve as safe spaces for people who use drugs (PWUD), fostering discussions on substance use experiences, harm reduction, and addiction recovery. Users' shared narrative…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONCrisisSense-LLM: Instruction Fine-Tuned Large Language Model for Multi-label Social Media Text Classification in Disaster Informatics
In the field of crisis/disaster informatics, social media is increasingly being used for improving situational awareness to inform response and relief efforts. Efficient and accurate text classification tools have been a…
ClassificationInformativenessLanguage ModelingLanguage Modelling+5