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Greatest papers with code

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

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

NeurIPS 2018 mwydmuch/extremeText

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

Taming Pretrained Transformers for Extreme Multi-label Text Classification

7 May 2019OctoberChang/X-Transformer

However, naively applying deep transformer models to the XMC problem leads to sub-optimal performance due to the large output space and the label sparsity issue.

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

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

Probabilistic Label Trees for Extreme Multi-label Classification

23 Sep 2020mwydmuch/napkinXC

We first introduce the PLT model and discuss training and inference procedures and their computational costs.

EXTREME MULTI-LABEL CLASSIFICATION MULTI-LABEL CLASSIFICATION

Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling

26 Jun 2018wushanshan/L1AE

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 2018xmc-aalto/proxml

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

Revisiting the Vector Space Model: Sparse Weighted Nearest-Neighbor Method for Extreme Multi-Label Classification

12 Feb 2018hiro4bbh/sticker

Finally, we show that the Sparse Weighted Nearest-Neighbor Method can process data points in real time on XMLC datasets with equivalent performance to SOTA models, with a single thread and smaller storage footprint.

EXTREME MULTI-LABEL CLASSIFICATION INFORMATION RETRIEVAL MULTI-LABEL CLASSIFICATION