Browse > Methodology > Extreme Multi-Label Classification

Extreme Multi-Label Classification

5 papers with code ยท Methodology

State-of-the-art leaderboards

No evaluation results yet. Help compare methods by submit evaluation metrics.

Latest papers without code

Ranking-Based Autoencoder for Extreme Multi-label Classification

NAACL 2019

Extreme Multi-label classification (XML) is an important yet challenging machine learning task, that assigns to each instance its most relevant candidate labels from an extremely large label collection, where the numbers of labels, features and instances could be thousands or millions.

EXTREME MULTI-LABEL CLASSIFICATION FEATURE IMPORTANCE MULTI-LABEL CLASSIFICATION

X-BERT: eXtreme Multi-label Text Classification with BERT

7 May 2019

Extreme multi-label text classification (XMC) aims to tag each input text with the most relevant labels from an extremely large label set, such as those that arise in product categorization and e-commerce recommendation.

EXTREME MULTI-LABEL CLASSIFICATION MULTI-LABEL CLASSIFICATION MULTI-LABEL TEXT CLASSIFICATION PRODUCT CATEGORIZATION SENTENCE CLASSIFICATION

Ranking-Based Autoencoder for Extreme Multi-label Classification

NAACL 2019

Extreme Multi-label classification (XML) is an important yet challenging machine learning task, that assigns to each instance its most relevant candidate labels from an extremely large label collection, where the numbers of labels, features and instances could be thousands or millions.

EXTREME MULTI-LABEL CLASSIFICATION FEATURE IMPORTANCE MULTI-LABEL CLASSIFICATION

A no-regret generalization of hierarchical softmax to extreme multi-label classification

NeurIPS 2018

Extreme multi-label classification (XMLC) is a problem of tagging an instance with a small subset of relevant labels chosen from an extremely large pool of possible labels.

EXTREME MULTI-LABEL CLASSIFICATION MULTI-LABEL CLASSIFICATION

Block-wise Partitioning for Extreme Multi-label Classification

4 Nov 2018

Extreme multi-label classification aims to learn a classifier that annotates an instance with a relevant subset of labels from an extremely large label set.

EXTREME MULTI-LABEL CLASSIFICATION MULTI-LABEL CLASSIFICATION

Fully Scalable Gaussian Processes using Subspace Inducing Inputs

6 Jul 2018

We introduce fully scalable Gaussian processes, an implementation scheme that tackles the problem of treating a high number of training instances together with high dimensional input data.

EXTREME MULTI-LABEL CLASSIFICATION MULTI-LABEL CLASSIFICATION

Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling

26 Jun 2018

Our experiments show that there is indeed additional structure beyond sparsity in the real datasets; our method is able to discover it and exploit it to create excellent reconstructions with fewer measurements (by a factor of 1. 1-3x) compared to the previous state-of-the-art methods.

EXTREME MULTI-LABEL CLASSIFICATION MULTI-LABEL CLASSIFICATION MULTI-LABEL LEARNING

Adversarial Extreme Multi-label Classification

5 Mar 2018

The goal in extreme multi-label classification is to learn a classifier which can assign a small subset of relevant labels to an instance from an extremely large set of target labels.

EXTREME MULTI-LABEL CLASSIFICATION MULTI-LABEL CLASSIFICATION

Subset Labeled LDA for Large-Scale Multi-Label Classification

16 Sep 2017

We conduct extensive experiments on eight data sets, with label sets sizes ranging from hundreds to hundreds of thousands, comparing our proposed algorithm with the previously proposed LLDA algorithms (Prior--LDA, Dep--LDA), as well as the state of the art in extreme multi-label classification.

EXTREME MULTI-LABEL CLASSIFICATION MULTI-LABEL CLASSIFICATION MULTI-LABEL LEARNING