Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
Imposing Hard Logical Constraints on Multi-label Classification Neural Networks
Machine Learning, and in particular deep learning, is becoming increasingly ubiquitous, and it is likely to be applied in almost every aspect of our lives in the next few years. However, the careless application of such…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONA transformer-based model for default prediction in mid-cap corporate markets
In this paper, we study mid-cap companies, i.e. publicly traded companies with less than US $10 billion in market capitalisation. Using a large dataset of US mid-cap companies observed over 30 years, we look to predict t…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONTime SeriesTime Series Analysis+1Evaluating Extreme Hierarchical Multi-label Classification
Several natural language processing (NLP) tasks are defined as a classification problem in its most complex form: Multi-label Hierarchical Extreme classification, in which items may be associated with multiple classes fr…
ClassificationHierarchical Multi-label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONImproved Multi-label Classification under Temporal Concept Drift: Rethinking Group-Robust Algorithms in a Label-Wise Setting
In document classification for, e.g., legal and biomedical text, we often deal with hundreds of classes, including very infrequent ones, as well as temporal concept drift caused by the influence of real-world events, e.…
Document ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONEffective Token Graph Modeling using a Novel Labeling Strategy for Structured Sentiment Analysis
The state-of-the-art model for structured sentiment analysis casts the task as a dependency parsing problem, which has some limitations: (1) The label proportions for span prediction and span relation prediction are imb…
Dependency ParsingGraph AttentionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+4HMD-AMP: Protein Language-Powered Hierarchical Multi-label Deep Forest for Annotating Antimicrobial Peptides
Identifying the targets of an antimicrobial peptide is a fundamental step in studying the innate immune response and combating antibiotic resistance, and more broadly, precision medicine and public health. There have bee…
Binary ClassificationLanguage ModellingMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Do we still need ImageNet pre-training in remote sensing scene classification?
Due to the scarcity of labeled data, using supervised models pre-trained on ImageNet is a de facto standard in remote sensing scene classification. Recently, the availability of larger high resolution remote sensing (HRR…
ClassificationMulti-Label ClassificationScene ClassificationSelf-Supervised LearningCo-attention network with label embedding for text classification
Most existing methods for text classification focus on extracting a highly discriminative text representation, which, however, is typically computationally inefficient. To alleviate this issue, label embedding frameworks…
ClassificationMulti-class ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+4Unsupervised Paraphrasing Consistency Training for Low Resource Named Entity Recognition
Unsupervised consistency training is a way of semi-supervised learning that encourages consistency in model predictions between the original and augmented data. For Named Entity Recognition (NER), existing approaches aug…
Data AugmentationLow Resource Named Entity RecognitionMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+4Meta-LMTC: Meta-Learning for Large-Scale Multi-Label Text Classification
Large-scale multi-label text classification (LMTC) tasks often face long-tailed label distributions, where many labels have few or even no training instances. Although current methods can exploit prior knowledge to handl…
Meta-LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti Label Text Classification+3Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction
Learning on graphs has attracted significant attention in the learning community due to numerous real-world applications. In particular, graph neural networks (GNNs), which take numerical node features and graph structur…
Extreme Multi-Label ClassificationLanguage ModellingLink PredictionMulti-Label Classification+3Smart Fashion: A Review of AI Applications in the Fashion & Apparel Industry
The fashion industry is on the verge of an unprecedented change. The implementation of machine learning, computer vision, and artificial intelligence (AI) in fashion applications is opening lots of new opportunities for …
ArticlesMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMeta-Learning for Multi-Label Few-Shot Classification
Even with the luxury of having abundant data, multi-label classification is widely known to be a challenging task to address. This work targets the problem of multi-label meta-learning, where a model learns to predict mu…
ClassificationFew-Shot LearningMeta-LearningMulti-Label Classification+1Multi-label Classification with Partial Annotations using Class-aware Selective Loss
Large-scale multi-label classification datasets are commonly, and perhaps inevitably, partially annotated. That is, only a small subset of labels are annotated per sample. Different methods for handling the missing label…
Missing LabelsMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONPropensity-scored Probabilistic Label Trees
Extreme multi-label classification (XMLC) refers to the task of tagging instances with small subsets of relevant labels coming from an extremely large set of all possible labels. Recently, XMLC has been widely applied to…
Extreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONRecommendation SystemsEncT5: A Framework for Fine-tuning T5 as Non-autoregressive Models
Pre-trained encoder-decoder transformer architectures have become increasingly popular recently with the advent of T5 models. T5 has also become more favorable over other architectures like BERT due to the amount of data…
DecoderLanguage ModellingMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2Knowledge-driven Active Learning
The deployment of Deep Learning (DL) models is still precluded in those contexts where the amount of supervised data is limited. To answer this issue, active learning strategies aim at minimizing the amount of labelled d…
Active LearningMulti-Label ClassificationObject DetectionObject RecognitionAdaptive Elastic Training for Sparse Deep Learning on Heterogeneous Multi-GPU Servers
Motivated by extreme multi-label classification applications, we consider training deep learning models over sparse data in multi-GPU servers. The variance in the number of non-zero features across training batches and t…
Extreme Multi-Label ClassificationGPUMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1SCEHR: Supervised Contrastive Learning for Clinical Risk Prediction using Electronic Health Records
Contrastive learning has demonstrated promising performance in image and text domains either in a self-supervised or a supervised manner. In this work, we extend the supervised contrastive learning framework to clinical …
BenchmarkingBinary ClassificationContrastive LearningMortality Prediction+2Transformer-based Dual Relation Graph for Multi-label Image Recognition
The simultaneous recognition of multiple objects in one image remains a challenging task, spanning multiple events in the recognition field such as various object scales, inconsistent appearances, and confused inter-clas…
Multi-Label ClassificationMulti-Label Image RecognitionRelation