Papers Extreme Multi-Label Classification
“Extreme Multi-Label Classification” 태그가 달린 논문 75편 · 필터 해제
Extreme Multi-label Learning for Semantic Matching in Product Search
We consider the problem of semantic matching in product search: given a customer query, retrieve all semantically related products from a huge catalog of size 100 million, or more. Because of large catalog spaces and rea…
Extreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label LearningEnabling 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-ClASSIFICATIONTAGPriberam 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+4Fine-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+2Stratified Sampling for Extreme Multi-Label Data
Extreme multi-label classification (XML) is becoming increasingly relevant in the era of big data. Yet, there is no method for effectively generating stratified partitions of XML datasets. Instead, researchers typically …
Extreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONTop-$k$ eXtreme Contextual Bandits with Arm Hierarchy
Motivated by modern applications, such as online advertisement and recommender systems, we study the top-$k$ extreme contextual bandits problem, where the total number of arms can be enormous, and the learner is allowed …
Computational EfficiencyExtreme Multi-Label ClassificationMulti-Armed BanditsMulti-Label Classification+2Multi-label Ranking: Mining Multi-label and Label Ranking Data
We survey multi-label ranking tasks, specifically multi-label classification and label ranking classification. We highlight the unique challenges, and re-categorize the methods, as they no longer fit into the traditional…
ClassificationExtreme Multi-Label ClassificationGeneral ClassificationMulti-Label Classification+2Tensor Composition Net for Visual Relationship Prediction
We present a novel Tensor Composition Net (TCN) to predict visual relationships in images. Visual Relationship Prediction (VRP) provides a more challenging test of image understanding than conventional image tagging and …
Extreme Multi-Label Classificationimage-classificationImage ClassificationImage Retrieval+6Retrieving Skills from Job Descriptions: A Language Model Based Extreme Multi-label Classification Framework
We introduce a deep learning model to learn the set of enumerated job skills associated with a job description. In our analysis of a large-scale government job portal mycareersfuture.sg, we observe that as much as 65{\%}…
Extreme Multi-Label ClassificationLanguage ModelingLanguage ModellingMulti-Label Classification+1The Emerging Trends of Multi-Label Learning
Exabytes of data are generated daily by humans, leading to the growing need for new efforts in dealing with the grand challenges for multi-label learning brought by big data. For example, extreme multi-label classificati…
ClassificationExtreme Multi-Label ClassificationGeneral ClassificationMulti-Label Classification+2Learning from eXtreme Bandit Feedback
We study the problem of batch learning from bandit feedback in the setting of extremely large action spaces. Learning from extreme bandit feedback is ubiquitous in recommendation systems, in which billions of decisions a…
Extreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONRecommendation SystemsProbabilistic Label Trees for Extreme Multi-label Classification
Extreme multi-label classification (XMLC) is a learning task of tagging instances with a small subset of relevant labels chosen from an extremely large pool of possible labels. Problems of this scale can be efficiently h…
ClassificationExtreme Multi-Label ClassificationGeneral ClassificationMulti-Label Classification+1On Data Augmentation for Extreme Multi-label Classification
In this paper, we focus on data augmentation for the extreme multi-label classification (XMC) problem. One of the most challenging issues of XMC is the long tail label distribution where even strong models suffer from in…
ClassificationData AugmentationExtreme Multi-Label ClassificationGeneral Classification+2HGCN4MeSH: Hybrid Graph Convolution Network for MeSH Indexing
Recently deep learning has been used in Medical subject headings (MeSH) indexing to reduce the time and monetary cost by manual annotation, including DeepMeSH, TextCNN, etc. However, these models still suffer from failin…
Extreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONRepresentation LearningUnbiased Loss Functions for Extreme Classification With Missing Labels
The goal in extreme multi-label classification (XMC) is to tag an instance with a small subset of relevant labels from an extremely large set of possible labels. In addition to the computational burden arising from large…
ClassificationExtreme Multi-Label ClassificationGeneral ClassificationMissing Labels+3Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model
We investigate the Plackett-Luce (PL) model based listwise learning-to-rank (LTR) on data with partitioned preference, where a set of items are sliced into ordered and disjoint partitions, but the ranking of items within…
Extreme Multi-Label ClassificationLearning-To-RankMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Extreme Multi-label Classification from Aggregated Labels
Extreme multi-label classification (XMC) is the problem of finding the relevant labels for an input, from a very large universe of possible labels. We consider XMC in the setting where labels are available only for group…
ClassificationExtreme Multi-Label ClassificationGeneral ClassificationMulti-Label Classification+1On-the-fly Global Embeddings Using Random Projections for Extreme Multi-label Classification
The goal of eXtreme Multi-label Learning (XML) is to automatically annotate a given data point with the most relevant subset of labels from an extremely large vocabulary of labels (e.g., a million labels). Lately, many a…
Extreme Multi-Label ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label LearningTaming Pretrained Transformers for Extreme Multi-label Text Classification
We consider the extreme multi-label text classification (XMC) problem: given an input text, return the most relevant labels from a large label collection. For example, the input text could be a product description on Ama…
Extreme Multi-Label ClassificationGeneral ClassificationMulti-Label ClassificationMulti-Label Text Classification+4Bonsai -- Diverse and Shallow Trees for Extreme Multi-label Classification
Extreme multi-label classification (XMC) refers to supervised multi-label learning involving hundreds of thousand or even millions of labels. In this paper, we develop a suite of algorithms, called Bonsai, which generali…
ClassificationExtreme Multi-Label ClassificationGeneral ClassificationMulti-Label Classification+2