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Greatest papers with code

Multi-Moments in Time: Learning and Interpreting Models for Multi-Action Video Understanding

1 Nov 2019zhoubolei/moments_models

An event happening in the world is often made of different activities and actions that can unfold simultaneously or sequentially within a few seconds.

ACTION DETECTION ACTION RECOGNITION MULTI-LABEL LEARNING VIDEO UNDERSTANDING

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.

CLASSIFICATION 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.

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

Learning to Separate Object Sounds by Watching Unlabeled Video

ECCV 2018 rhgao/separating-object-sounds

Our work is the first to learn audio source separation from large-scale "in the wild" videos containing multiple audio sources per video.

AUDIO DENOISING AUDIO SOURCE SEPARATION DENOISING MULTI-LABEL LEARNING

Multi-Label Sampling based on Local Label Imbalance

7 May 2020tsoumakas/mulan

Experimental results on 13 multi-label datasets demonstrate the effectiveness of the proposed measure and sampling approaches for a variety of evaluation metrics, particularly in the case of an ensemble of classifiers trained on repeated samples of the original data.

MULTI-LABEL LEARNING

Synthetic Oversampling of Multi-Label Data based on Local Label Distribution

2 May 2019tsoumakas/mulan

Class-imbalance is an inherent characteristic of multi-label data which affects the prediction accuracy of most multi-label learning methods.

MULTI-LABEL LEARNING

Few-Shot and Zero-Shot Multi-Label Learning for Structured Label Spaces

EMNLP 2018 bionlproc/multi-label-zero-shot

Furthermore, we develop few- and zero-shot methods for multi-label text classification when there is a known structure over the label space, and evaluate them on two publicly available medical text datasets: MIMIC II and MIMIC III.

CLASSIFICATION MULTI-LABEL CLASSIFICATION MULTI-LABEL LEARNING MULTI-LABEL TEXT CLASSIFICATION

Cost-Sensitive Reference Pair Encoding for Multi-Label Learning

29 Nov 2016yangarbiter/multilabel-learn

Label space expansion for multi-label classification (MLC) is a methodology that encodes the original label vectors to higher dimensional codes before training and decodes the predicted codes back to the label vectors during testing.

ACTIVE LEARNING MULTI-LABEL CLASSIFICATION MULTI-LABEL LEARNING

CheXclusion: Fairness gaps in deep chest X-ray classifiers

14 Feb 2020LalehSeyyed/CheXclusion

We demonstrate that TPR disparities exist in the state-of-the-art classifiers in all datasets, for all clinical tasks, and all subgroups.

FAIRNESS MEDICAL DIAGNOSIS MULTI-LABEL CLASSIFICATION MULTI-LABEL LEARNING