Extreme Multi-Label Classification
29 papers with code • 0 benchmarks • 2 datasets
Extreme Multi-Label Classification is a supervised learning problem where an instance may be associated with multiple labels. The two main problems are the unbalanced labels in the dataset and the amount of different labels.
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Libraries
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LSHTC
EXTREME CLASSIFICATION
Latest papers
Propensity-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.
Adaptive Elastic Training for Sparse Deep Learning on Heterogeneous Multi-GPU Servers
YMA33/HeteroGPU
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We address these challenges with Adaptive SGD, an adaptive elastic model averaging stochastic gradient descent algorithm for heterogeneous multi-GPUs that is characterized by dynamic scheduling, adaptive batch size scaling, and normalized model merging.
DECAF: Deep Extreme Classification with Label Features
Extreme-classification/DECAF
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This paper develops the DECAF algorithm that addresses these challenges by learning models enriched by label metadata that jointly learn model parameters and feature representations using deep networks and offer accurate classification at the scale of millions of labels.
ECLARE: Extreme Classification with Label Graph Correlations
Extreme-classification/ECLARE
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This paper presents ECLARE, a scalable deep learning architecture that incorporates not only label text, but also label correlations, to offer accurate real-time predictions within a few milliseconds.
Extreme Multi-label Learning for Semantic Matching in Product Search
amzn/pecos
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In this paper, we aim to improve semantic product search by using tree-based XMC models where inference time complexity is logarithmic in the number of products.
Priberam at MESINESP Multi-label Classification of Medical Texts Task
Information retrieval tools are crucial in order to navigate and provide meaningful recommendations for articles and treatments.
Stratified Sampling for Extreme Multi-Label Data
Extreme multi-label classification (XML) is becoming increasingly relevant in the era of big data.
Top-$k$ eXtreme Contextual Bandits with Arm Hierarchy
We show that our algorithm has a regret guarantee of $O(k\sqrt{(A-k+1)T \log (|\mathcal{F}|T)})$, where $A$ is the total number of arms and $\mathcal{F}$ is the class containing the regression function, while only requiring $\tilde{O}(A)$ computation per time step.
Retrieving Skills from Job Descriptions: A Language Model Based Extreme Multi-label Classification Framework
wing-nus/jd2skills-bert-xmlc
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COLING 2020
We introduce a deep learning model to learn the set of enumerated job skills associated with a job description.
Probabilistic Label Trees for Extreme Multi-label Classification
We first introduce the PLT model and discuss training and inference procedures and their computational costs.
