Search Results for author:
Found 5 papers, 2 papers with code
Dual-Encoders for Extreme Multi-Label Classification
We propose decoupled softmax loss - a simple modification to the InfoNCE loss - that overcomes the limitations of existing contrastive losses.
EHI: End-to-end Learning of Hierarchical Index for Efficient Dense Retrieval
Such techniques use a two-stage process: (a) contrastive learning to train a dual encoder to embed both the query and documents and (b) approximate nearest neighbor search (ANNS) for finding similar documents for a given query.
ELIAS: End-to-End Learning to Index and Search in Large Output Spaces
A popular approach for dealing with the large label space is to arrange the labels into a shallow tree-based index and then learn an ML model to efficiently search this index via beam search.
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.
Extreme Regression for Dynamic Search Advertising
This paper also introduces a (3) new labelwise prediction algorithm in XReg useful for DSA and other recommendation tasks.
