Papers Multi-Label Classification
“Multi-Label Classification” 태그가 달린 논문 1,337편 · 필터 해제
multiPRover: Generating Multiple Proofs for Improved Interpretability in Rule Reasoning
We focus on a type of linguistic formal reasoning where the goal is to reason over explicit knowledge in the form of natural language facts and rules (Clark et al., 2020). A recent work, named PRover (Saha et al., 2020),…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONVolta at SemEval-2021 Task 6: Towards Detecting Persuasive Texts and Images using Textual and Multimodal Ensemble
Memes are one of the most popular types of content used to spread information online. They can influence a large number of people through rhetorical and psychological techniques. The task, Detection of Persuasion Techniq…
ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONTransfer LearningNewsEmbed: Modeling News through Pre-trained Document Representations
Effectively modeling text-rich fresh content such as news articles at document-level is a challenging problem. To ensure a content-based model generalize well to a broad range of applications, it is critical to have a tr…
ArticlesContrastive LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Enabling Efficiency-Precision Trade-offs for Label Trees in Extreme Classification
Extreme multi-label classification (XMC) aims to learn a model that can tag data points with a subset of relevant labels from an extremely large label set. Real world e-commerce applications like personalized recommendat…
Extreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONTAGESC: Redesigning WSD with Extractive Sense Comprehension
Word Sense Disambiguation (WSD) is a historical NLP task aimed at linking words in contexts to discrete sense inventories and it is usually cast as a multi-label classification task. Recently, several neural approaches h…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSentenceWord Sense DisambiguationClassification multi-label de cas cliniques avec CamemBERT (Multi-label classification of clinical cases with CamemBERT )
La quantité de documents textuels médicaux allant grandissant, la nécessité d’en extraire automatiquement des informations concernant des patients devient de plus en plus grande. La prédiction du profil clinique permet d…
ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONpRSL: Interpretable Multi-label Stacking by Learning Probabilistic Rules
A key task in multi-label classification is modeling the structure between the involved classes. Modeling this structure by probabilistic and interpretable means enables application in a broad variety of tasks such as ze…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONZero-Shot LearningBasic and Depression Specific Emotion Identification in Tweets: Multi-label Classification Experiments
In this paper, we present empirical analysis on basic and depression specific multi-emotion mining in Tweets with the help of state of the art multi-label classifiers. We choose our basic emotions from a hybrid emotion m…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label LearningPLM: Partial Label Masking for Imbalanced Multi-label Classification
Neural networks trained on real-world datasets with long-tailed label distributions are biased towards frequent classes and perform poorly on infrequent classes. The imbalance in the ratio of positive and negative sample…
Classificationimage-classificationImage ClassificationMulti-Label Classification+1Can multi-label classification networks know what they don’t know?
Estimating out-of-distribution (OOD) uncertainty is a major challenge for safely deploying machine learning models in the open-world environment. Improved methods for OOD detection in multi-class classification have emer…
ClassificationMulti-class ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1RotoGrad: Gradient Homogenization in Multi-Task Learning
Multi-task learning is being increasingly adopted in applications domains like computer vision and reinforcement learning. However, optimally exploiting its advantages remains a major challenge due to the effect of negat…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Task LearningA Consensual Collaborative Learning Method for Remote Sensing Image Classification Under Noisy Multi-Labels
Collecting a large number of reliable training images annotated by multiple land-cover class labels in the framework of multi-label classification is time-consuming and costly in remote sensing (RS). To address this prob…
image-classificationImage ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+2Priberam at MESINESP Multi-label Classification of Medical Texts Task
Medical articles provide current state of the art treatments and diagnostics to many medical practitioners and professionals. Existing public databases such as MEDLINE contain over 27 million articles, making it difficul…
ArticlesClassificationExtreme Multi-Label ClassificationInformation Retrieval+4Semantic Diversity Learning for Zero-Shot Multi-label Classification
Training a neural network model for recognizing multiple labels associated with an image, including identifying unseen labels, is challenging, especially for images that portray numerous semantically diverse labels. As c…
ClassificationDiversityImage RetrievalMulti-Label Classification+5Rethinking and Reweighting the Univariate Losses for Multi-Label Ranking: Consistency and Generalization
(Partial) ranking loss is a commonly used evaluation measure for multi-label classification, which is usually optimized with convex surrogates for computational efficiency. Prior theoretical work on multi-label ranking m…
Computational EfficiencyMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONFine-grained Generalization Analysis of Vector-valued Learning
Many fundamental machine learning tasks can be formulated as a problem of learning with vector-valued functions, where we learn multiple scalar-valued functions together. Although there is some generalization analysis on…
Extreme Multi-Label ClassificationGeneral ClassificationGeneralization BoundsMulti-class Classification+2SYNFIX: Automatically Fixing Syntax Errors using Compiler Diagnostics
Beginning programmers struggle with the complex grammar of modern programming languages like Java, and make lot of syntax errors. The diagnostic syntax error messages from compilers and IDEs are sometimes useful, but oft…
BIG-bench Machine LearningDiagnosticMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1ImageNet-21K Pretraining for the Masses
ImageNet-1K serves as the primary dataset for pretraining deep learning models for computer vision tasks. ImageNet-21K dataset, which is bigger and more diverse, is used less frequently for pretraining, mainly due to its…
Action RecognitionFine-Grained Image ClassificationImage ClassificationMulti-Label ClassificationSKID: Self-Supervised Learning for Knee Injury Diagnosis from MRI Data
In medical image analysis, the cost of acquiring high-quality data and their annotation by experts is a barrier in many medical applications. Most of the techniques used are based on supervised learning framework and nee…
Medical DiagnosisMedical Image AnalysisMedical Image ClassificationMulti-Label Classification+2Interpretation of multi-label classification models using shapley values
Multi-label classification is a type of classification task, it is used when there are two or more classes, and the data point we want to predict may belong to none of the classes or all of them at the same time. In the …
ClassificationGeneral ClassificationInformation RetrievalMulti-Label Classification+2