Dual-encoders for Extreme Multi-label Classification
A parameter efficient encoder only model for multi-shot retrieval (aka extreme classification)
Extreme-Classification
NGAME: Negative Mining aware Mini-batching for Extreme Classification
A light-weight mini-batch creation technique that offers provably accurate in-batch negative samples for training retrieval models. This allows training with larger mini-batches offering significantly faster convergence and higher accuracies than existing negative sampling techniques.
ELIAS: End-to-end Learning to Search and Index in Large Output Spaces
Learnable graph-based search index for classification/retrieval in large output space, scalable to label space on a single A100 GPU, achieves SOTA on multiple large-scale extreme classification benchmarks
Generalized Zero-shot Extreme Multi-label Classification
This paper proposes Generalized Zero-shot XML (GZXML), a paradigm where the task is to tag a data point with the most relevant labels from a large universe of both seen and unseen labels.
Extreme Regression for Dynamic Search Advertising
This paper introduces a new learning paradigm called eXtreme Regression (XR) whose objective is to accurately predict the numerical degrees of relevance of an extremely large number of labels to a data point. XR can provide elegant solutions to many large-scale ranking and recommendation applications including Dynamic Search Advertising (DSA).