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Bhaskar Mitra
Researcher at Microsoft
Publications - 80
Citations - 3599
Bhaskar Mitra is an academic researcher from Microsoft. The author has contributed to research in topics: Ranking (information retrieval) & Deep learning. The author has an hindex of 22, co-authored 73 publications receiving 2934 citations. Previous affiliations of Bhaskar Mitra include University College London.
Papers
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Proceedings ArticleDOI
Learning to Match using Local and Distributed Representations of Text for Web Search
TL;DR: This work proposes a novel document ranking model composed of two separate deep neural networks, one that matches the query and the document using a local representation, and another that Matching with distributed representations complements matching with traditional local representations.
An Introduction to Computational Networks and the Computational Network Toolkit
Dong Yu,Adam Eversole,Michael L. Seltzer,Kaisheng Yao,Oleksii Kuchaiev,Yu Zhang,Frank Seide,Zhiheng Huang,Brian Guenter,Huaming Wang,Jasha Droppo,Geoffrey Zweig,Christopher J. Rossbach,Jie Gao,Andreas Stolcke,Jon Currey,Malcolm Slaney,Guoguo Chen,Amit Kumar Agarwal,Christopher H. Basoglu,Marko Padmilac,Alexey Kamenev,Vladimir Ivanov,Scott Cypher,Hari Parthasarathi,Bhaskar Mitra,Baolin Peng,Xuedong Huang +27 more
TL;DR: The computational network toolkit (CNTK), an implementation of CN that supports both GPU and CPU, is introduced and the architecture and the key components of the CNTK are described, the command line options to use C NTK, and the network definition and model editing language are described.
Book
An Introduction to Neural Information Retrieval
Bhaskar Mitra,Nick Craswell +1 more
TL;DR: The monograph provides a complete picture of neural information retrieval techniques that culminate in supervised neural learning to rank models including deep neural network architectures that are trained end-to-end for ranking tasks.
Proceedings ArticleDOI
Query Expansion with Locally-Trained Word Embeddings
TL;DR: It is demonstrated that word embeddings such as word2vec and GloVe, when trained globally, underperform corpus and query specific embeddlings for retrieval tasks, suggesting that other tasks benefiting from global embeddments may also benefit from local embeddins.
Posted Content
Overview of the TREC 2019 deep learning track.
TL;DR: The Deep Learning Track is a new track for TREC 2019, with the goal of studying ad hoc ranking in a large data regime, and is the first track with large human-labeled training sets, introducing two sets corresponding to two tasks, each with rigorous TREC-style blind evaluation and reusable test sets.