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Multi-Label Learning

6 papers with code · Methodology

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DiSMEC - Distributed Sparse Machines for Extreme Multi-label Classification

8 Sep 2016Refefer/fastxml

In this work, we present DiSMEC, which is a large-scale distributed framework for learning one-versus-rest linear classifiers coupled with explicit capacity control to control model size.

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

Pedestrian Attribute Recognition: A Survey

22 Jan 2019wangxiao5791509/Pedestrian-Attribute-Recognition-Paper-List

We also review some popular network architectures which have widely applied in the deep learning community.

MULTI-LABEL LEARNING MULTI-TASK LEARNING PEDESTRIAN ATTRIBUTE RECOGNITION

Attention-Based Capsule Networks with Dynamic Routing for Relation Extraction

EMNLP 2018 WHUNLPLab/Papers-to-read

A capsule is a group of neurons, whose activity vector represents the instantiation parameters of a specific type of entity.

MULTI-LABEL LEARNING RELATION EXTRACTION

Bonsai - Diverse and Shallow Trees for Extreme Multi-label Classification

17 Apr 2019xmc-aalto/bonsai

Extreme multi-label classification refers to supervised multi-label learning involving hundreds of thousand or even millions of labels.

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

Incremental Sparse Bayesian Ordinal Regression

18 Jun 2018chang-li/SBOR

Ordinal Regression (OR) aims to model the ordering information between different data categories, which is a crucial topic in multi-label learning.

MULTI-LABEL LEARNING