Search Results for author: Sheshansh Agrawal

Found 3 papers, 2 papers with code

OOD-DiskANN: Efficient and Scalable Graph ANNS for Out-of-Distribution Queries

no code implementations22 Oct 2022 Shikhar Jaiswal, Ravishankar Krishnaswamy, Ankit Garg, Harsha Vardhan Simhadri, Sheshansh Agrawal

State-of-the-art algorithms for Approximate Nearest Neighbor Search (ANNS) such as DiskANN, FAISS-IVF, and HNSW build data dependent indices that offer substantially better accuracy and search efficiency over data-agnostic indices by overfitting to the index data distribution.

DECAF: Deep Extreme Classification with Label Features

1 code implementation1 Aug 2021 Anshul Mittal, Kunal Dahiya, Sheshansh Agrawal, Deepak Saini, Sumeet Agarwal, Purushottam Kar, Manik Varma

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.

Classification Extreme Multi-Label Classification +5

ECLARE: Extreme Classification with Label Graph Correlations

1 code implementation31 Jul 2021 Anshul Mittal, Noveen Sachdeva, Sheshansh Agrawal, Sumeet Agarwal, Purushottam Kar, Manik Varma

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.

Classification Extreme Multi-Label Classification +6

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