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Found 6 papers, 3 papers with code
Graph Regularized Encoder Training for Extreme Classification
The paper notices that in these settings, it is much more effective to use graph data to regularize encoder training than to implement a GCN.
NGAME: Negative Mining-aware Mini-batching for Extreme Classification
This paper identifies that memory overheads of popular negative mining techniques often force mini-batch sizes to remain small and slow training down.
DeepXML: A Deep Extreme Multi-Label Learning Framework Applied to Short Text Documents
Scalability and accuracy are well recognized challenges in deep extreme multi-label learning where the objective is to train architectures for automatically annotating a data point with the most relevant subset of labels from an extremely large label set.
DECAF: Deep Extreme Classification with Label Features
This paper develops the DECAF algorithm that addresses these challenges by learning models enriched by label metadata that jointly learn model parameters and feature representations using deep networks and offer accurate classification at the scale of millions of labels.
GalaXC: Graph Neural Networks with Labelwise Attention for Extreme Classification
An efficient end-to-end implementation of GalaXC is presented that could be trained on a dataset with 50M labels and 97M training documents in less than 100 hours on 4×V100 GPUs.
DeepXML: Scalable & Accurate Deep Extreme Classification for Matching User Queries to Advertiser Bid Phrases
The objective in deep extreme multi-label learning is to jointly learn feature representations and classifiers to automatically tag data points with the most relevant subset of labels from an extremely large label set.
