Papers Extreme Multi-Label Classification
“Extreme Multi-Label Classification” 태그가 달린 논문 75편 · 필터 해제
Cluster-Guided Label Generation in Extreme Multi-Label Classification
For extreme multi-label classification (XMC), existing classification-based models poorly perform for tail labels and often ignore the semantic relations among labels, like treating "Wikipedia" and "Wiki" as independent …
ClassificationExtreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONReview of Extreme Multilabel Classification
Extreme multi-label classification or XMLC, is an active area of interest in machine learning. Compared to traditional multi-label classification, here the number of labels is extremely large, hence, the name extreme mul…
Classificationcompressed sensingExtreme Multi-Label ClassificationMulti-Label Classification+1CascadeXML: Rethinking Transformers for End-to-end Multi-resolution Training in Extreme Multi-label Classification
Extreme Multi-label Text Classification (XMC) involves learning a classifier that can assign an input with a subset of most relevant labels from millions of label choices. Recent approaches, such as XR-Transformer and Li…
Extreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti Label Text Classification+3Uncertainty in Extreme Multi-label Classification
Uncertainty quantification is one of the most crucial tasks to obtain trustworthy and reliable machine learning models for decision making. However, most research in this domain has only focused on problems with small la…
ClassificationDecision MakingExtreme Multi-Label ClassificationMulti-Label Classification+4ELIAS: End-to-End Learning to Index and Search in Large Output Spaces
Extreme multi-label classification (XMC) is a popular framework for solving many real-world problems that require accurate prediction from a very large number of potential output choices. A popular approach for dealing w…
Extreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONOn Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification
The propensity model introduced by Jain et al. 2016 has become a standard approach for dealing with missing and long-tail labels in extreme multi-label classification (XMLC). In this paper, we critically revise this appr…
Extreme Multi-Label ClassificationMissing LabelsMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Augmenting Training Data for Massive Semantic Matching Models in Low-Traffic E-commerce Stores
Extreme multi-label classification (XMC) systems have been successfully applied in e-commerce (Shen et al., 2020; Dahiya et al., 2021) for retrieving products based on customer behavior. Such systems require large amount…
Extreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONInvestigating Active Learning Sampling Strategies for Extreme Multi Label Text Classification
Large scale, multi-label text datasets with high numbers of different classes are expensive to annotate, even more so if they deal with domain specific language. In this work, we aim to build classifiers on these dataset…
Active LearningExtreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+4Open Vocabulary Extreme Classification Using Generative Models
The extreme multi-label classification (XMC) task aims at tagging content with a subset of labels from an extremely large label set. The label vocabulary is typically defined in advance by domain experts and assumed to c…
ClassificationExtreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+3Extreme Multi-Label Classification with Label Masking for Product Attribute Value Extraction
Although most studies have treated attribute value extraction (AVE) as named entity recognition, these approaches are not practical in real-world e-commerce platforms because they perform poorly, and require canonicaliza…
AttributeAttribute Value ExtractionExtreme Multi-Label ClassificationMulti-Label Classification+4Node 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+3Propensity-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 SystemsAdaptive 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+1On Riemannian Approach for Constrained Optimization Model in Extreme Classification Problems
We propose a novel Riemannian method for solving the Extreme multi-label classification problem that exploits the geometric structure of the sparse low-dimensional local embedding models. A constrained optimization probl…
Extreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONRiemannian optimizationTailMix: Overcoming the Label Sparsity for Extreme Multi-label Classification
Extreme multi-label classification (XMC) aims at finding the most relevant labels from a huge label set at the industrial scale. The XMC problem inherently poses two challenges: data scalability and label sparsity. This …
Extreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONSpeeding-up One-vs-All Training for Extreme Classification via Smart Initialization
In this paper we show that a simple, data dependent way of setting the initial vector can be used to substantially speed up the training of linear one-versus-all (OVA) classifiers in extreme multi-label classification (X…
AllExtreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONUnbiased Loss Functions for Multilabel Classification with Missing Labels
This paper considers binary and multilabel classification problems in a setting where labels are missing independently and with a known rate. Missing labels are a ubiquitous phenomenon in extreme multi-label classificati…
ArticlesClassificationExtreme Multi-Label ClassificationMissing Labels+2DECAF: Deep Extreme Classification with Label Features
Extreme multi-label classification (XML) involves tagging a data point with its most relevant subset of labels from an extremely large label set, with several applications such as product-to-product recommendation with m…
ClassificationExtreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+5ECLARE: Extreme Classification with Label Graph Correlations
Deep extreme classification (XC) seeks to train deep architectures that can tag a data point with its most relevant subset of labels from an extremely large label set. The core utility of XC comes from predicting labels …
ClassificationExtreme Multi-Label ClassificationGraph EmbeddingMulti-Label Classification+6Label Disentanglement in Partition-based Extreme Multilabel Classification
Partition-based methods are increasingly-used in extreme multi-label classification (XMC) problems due to their scalability to large output spaces (e.g., millions or more). However, existing methods partition the large l…
ClassificationDisentanglementExtreme Multi-Label ClassificationMulti-Label Classification+1