Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Agrawal, Sheshansh"'
Autor:
Jaiswal, Shikhar, Krishnaswamy, Ravishankar, Garg, Ankit, Simhadri, Harsha Vardhan, Agrawal, Sheshansh
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 t
Externí odkaz:
http://arxiv.org/abs/2211.12850
Autor:
Mittal, Anshul, Dahiya, Kunal, Agrawal, Sheshansh, Saini, Deepak, Agarwal, Sumeet, Kar, Purushottam, Varma, Manik
Publikováno v:
Web Search and Data Mining 2021
Extreme multi-label classification (XML) involves tagging a data point with its most relevant subset of labels from an extremely large label set, with several applications such as product-to-product recommendation with millions of products. Although
Externí odkaz:
http://arxiv.org/abs/2108.00368
Autor:
Mittal, Anshul, Sachdeva, Noveen, Agrawal, Sheshansh, Agarwal, Sumeet, Kar, Purushottam, Varma, Manik
Publikováno v:
The Web Conference 2021
Deep extreme classification (XC) seeks to train deep architectures that can tag a data point with its most relevant subset of labels from an extremely large label set. The core utility of XC comes from predicting labels that are rarely seen during tr
Externí odkaz:
http://arxiv.org/abs/2108.00261
Probabilistic programs extend classical imperative programs with real-valued random variables and random branching. The most basic liveness property for such programs is the termination property. The qualitative (aka almost-sure) termination problem
Externí odkaz:
http://arxiv.org/abs/1709.04037