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Multi-Label Learning

9 papers with code · Methodology

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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

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

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

17 Apr 2019tomtung/omikuji

In this paper, we develop a suite of algorithms, called Bonsai, which generalizes the notion of label representation in XMC, and partitions the labels in the representation space to learn shallow trees.

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

Food Ingredients Recognition through Multi-label Learning

27 Jul 2017MarcBS/food_ingredients_recognition

Automatically constructing a food diary that tracks the ingredients consumed can help people follow a healthy diet.

MULTI-LABEL CLASSIFICATION MULTI-LABEL LEARNING

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

Learning Vector-valued Functions with Local Rademacher Complexity

11 Sep 2019superlj666/Learning-Vector-valued-Functions-with-Local-Rademacher-Complexity

We consider a general family of problems of which the output space admits vector-valued structure, covering a broad family of important domains, e. g. multi-label learning and multi-class classification.

MULTI-LABEL LEARNING

Variational Autoencoders for Sparse and Overdispersed Discrete Data

2 May 2019ethanhezhao/NBVAE

Many applications, such as text modelling, high-throughput sequencing, and recommender systems, require analysing sparse, high-dimensional, and overdispersed discrete (count-valued or binary) data.

COLLABORATIVE FILTERING 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