Search Results for author:
Found 6 papers, 3 papers with code
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.
Multi-Modal Extreme Classification
On the other hand, XC methods utilize classifier architectures to offer superior accuracies than embedding-only methods but mostly focus on text-based categorization tasks.
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.
ECLARE: Extreme Classification with Label Graph Correlations
This paper presents ECLARE, a scalable deep learning architecture that incorporates not only label text, but also label correlations, to offer accurate real-time predictions within a few milliseconds.
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.
