Benchmarks
Greatest papers with code
DiSMEC - Distributed Sparse Machines for Extreme Multi-label Classification
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
• OctoberChang/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
HFT-CNN: Learning Hierarchical Category Structure for Multi-label Short Text Categorization
The lower the HS level, the less the categorization performance.
EXTREME MULTI-LABEL CLASSIFICATION HIERARCHICAL STRUCTURE MULTI-LABEL CLASSIFICATION TEXT CATEGORIZATION
Bonsai -- Diverse and Shallow Trees for Extreme Multi-label Classification
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
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
• wushanshan/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
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
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