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Bhaskar Mitra

Bhaskar Mitra

180 Abonné
38 SlideShares 0 Clipboards 180 Abonné 3 Suivis
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38 SlideShares 0 Clipboards 180 Abonné 3 Suivis

Personal Information
Entreprise/Lieu de travail
London, United Kingdom United Kingdom
Profession
Principal Applied Scientist at Microsoft
Secteur d’activité
Technology / Software / Internet
Site Web
research.microsoft.com/people/bmitra
À propos
I am a Principal Applied Scientist at Bing in Microsoft Research Cambridge. My research is focused on Web search, information retrieval and machine learning. I started at Bing in 2007 (then called Live Search) at the Search Technology Center in India. Since then I have worked on a number of problems related to document ranking, query formulation and recommendation, query re-writing, entity ranking and evaluation. My current research interests center on representation learning and in particular their application to modern day information retrieval.
Coordonnées
Mots-clés
information retrieval deep learning neural networks web search neural information retrieval deep neural networks search machine learning word embeddings search engine learning to rank trec document ranking evaluation trec deep learning benchmarking recommender systems expected exposure stochastic ranking ms marco bert document retrieval embeddings web ranking demographics search exposure exposure fairness conformer-kernel conformer leaderboard msmarco efficiency adversarial learning reinforcement learning representation learning microsoft duet sigir 2016 neu-ir 2016 sigir2016 text embeddings word2vec query formulation query auto-completion ethics data challegne shared task full retrieval qti query term independence transformer datasets domain transfer transfer learning query evaluation semantic search natural language processing bing ucl university college london sigir 2017 www2017 adhoc retrieval acl2016berlin acl2016 pseudo-relevance feedback artificial intelligence recurrent neural networks rnn ai metrics web qna question-answering neu-ir2016 auto suggest london cntk search solutions 2015 computational network toolkit auto completion dssm pointwise mutual information search solutions contextual search
Tout plus
Présentations (38)
Tout voir
Exploring Session Context using Distributed Representations of Queries and Reformulations (SIGIR 2015)
il y a 7 ans • 1587 Vues
Vectorland: Brief Notes from Using Text Embeddings for Search
il y a 7 ans • 6305 Vues
A Simple Introduction to Word Embeddings
il y a 6 ans • 30114 Vues
Using Text Embeddings for Information Retrieval
il y a 6 ans • 9956 Vues
Neu-IR 2016: Lessons from the Trenches
il y a 6 ans • 846 Vues
Neu-ir 2016: Opening note
il y a 6 ans • 873 Vues
A Proposal for Evaluating Answer Distillation from Web Data
il y a 6 ans • 678 Vues
Recurrent networks and beyond by Tomas Mikolov
il y a 6 ans • 2871 Vues
Query Expansion with Locally-Trained Word Embeddings (Neu-IR 2016)
il y a 6 ans • 1446 Vues
Query Expansion with Locally-Trained Word Embeddings (ACL 2016)
il y a 6 ans • 1117 Vues
Neural Text Embeddings for Information Retrieval (WSDM 2017)
il y a 5 ans • 17246 Vues
The Duet model
il y a 5 ans • 2608 Vues
Neu-IR 2017: welcome
il y a 5 ans • 596 Vues
Neural Models for Information Retrieval
il y a 5 ans • 2050 Vues
Neural Models for Document Ranking
il y a 5 ans • 1770 Vues
Neural Models for Information Retrieval
il y a 5 ans • 2446 Vues
A Simple Introduction to Neural Information Retrieval
il y a 4 ans • 3857 Vues
5 Lessons Learned from Designing Neural Models for Information Retrieval
il y a 4 ans • 1487 Vues
Adversarial and reinforcement learning-based approaches to information retrieval
il y a 4 ans • 694 Vues
Dual Embedding Space Model (DESM)
il y a 4 ans • 746 Vues
Deep Learning for Search
il y a 4 ans • 1289 Vues
Neural Learning to Rank
il y a 3 ans • 838 Vues
Deep Learning for Search
il y a 3 ans • 397 Vues
Deep Learning for Search
il y a 3 ans • 1141 Vues
Learning to Rank with Neural Networks
il y a 3 ans • 840 Vues
Neural Learning to Rank
il y a 2 ans • 856 Vues
Deep Neural Methods for Retrieval
il y a 2 ans • 615 Vues
Benchmarking for Neural Information Retrieval: MS MARCO, TREC, and Beyond
il y a 2 ans • 418 Vues
Duet @ TREC 2019 Deep Learning Track
il y a 2 ans • 103 Vues
Neural Learning to Rank
il y a 2 ans • 309 Vues
J’aime (1)
Overview of the TREC 2019 Deep Learning Track
Nick Craswell • il y a 2 ans
  • Activité
  • À propos

Présentations (38)
Tout voir
Exploring Session Context using Distributed Representations of Queries and Reformulations (SIGIR 2015)
il y a 7 ans • 1587 Vues
Vectorland: Brief Notes from Using Text Embeddings for Search
il y a 7 ans • 6305 Vues
A Simple Introduction to Word Embeddings
il y a 6 ans • 30114 Vues
Using Text Embeddings for Information Retrieval
il y a 6 ans • 9956 Vues
Neu-IR 2016: Lessons from the Trenches
il y a 6 ans • 846 Vues
Neu-ir 2016: Opening note
il y a 6 ans • 873 Vues
A Proposal for Evaluating Answer Distillation from Web Data
il y a 6 ans • 678 Vues
Recurrent networks and beyond by Tomas Mikolov
il y a 6 ans • 2871 Vues
Query Expansion with Locally-Trained Word Embeddings (Neu-IR 2016)
il y a 6 ans • 1446 Vues
Query Expansion with Locally-Trained Word Embeddings (ACL 2016)
il y a 6 ans • 1117 Vues
Neural Text Embeddings for Information Retrieval (WSDM 2017)
il y a 5 ans • 17246 Vues
The Duet model
il y a 5 ans • 2608 Vues
Neu-IR 2017: welcome
il y a 5 ans • 596 Vues
Neural Models for Information Retrieval
il y a 5 ans • 2050 Vues
Neural Models for Document Ranking
il y a 5 ans • 1770 Vues
Neural Models for Information Retrieval
il y a 5 ans • 2446 Vues
A Simple Introduction to Neural Information Retrieval
il y a 4 ans • 3857 Vues
5 Lessons Learned from Designing Neural Models for Information Retrieval
il y a 4 ans • 1487 Vues
Adversarial and reinforcement learning-based approaches to information retrieval
il y a 4 ans • 694 Vues
Dual Embedding Space Model (DESM)
il y a 4 ans • 746 Vues
Deep Learning for Search
il y a 4 ans • 1289 Vues
Neural Learning to Rank
il y a 3 ans • 838 Vues
Deep Learning for Search
il y a 3 ans • 397 Vues
Deep Learning for Search
il y a 3 ans • 1141 Vues
Learning to Rank with Neural Networks
il y a 3 ans • 840 Vues
Neural Learning to Rank
il y a 2 ans • 856 Vues
Deep Neural Methods for Retrieval
il y a 2 ans • 615 Vues
Benchmarking for Neural Information Retrieval: MS MARCO, TREC, and Beyond
il y a 2 ans • 418 Vues
Duet @ TREC 2019 Deep Learning Track
il y a 2 ans • 103 Vues
Neural Learning to Rank
il y a 2 ans • 309 Vues
J’aime (1)
Overview of the TREC 2019 Deep Learning Track
Nick Craswell • il y a 2 ans
Personal Information
Entreprise/Lieu de travail
London, United Kingdom United Kingdom
Profession
Principal Applied Scientist at Microsoft
Secteur d’activité
Technology / Software / Internet
Site Web
research.microsoft.com/people/bmitra
À propos
I am a Principal Applied Scientist at Bing in Microsoft Research Cambridge. My research is focused on Web search, information retrieval and machine learning. I started at Bing in 2007 (then called Live Search) at the Search Technology Center in India. Since then I have worked on a number of problems related to document ranking, query formulation and recommendation, query re-writing, entity ranking and evaluation. My current research interests center on representation learning and in particular their application to modern day information retrieval.
Coordonnées
Mots-clés
information retrieval deep learning neural networks web search neural information retrieval deep neural networks search machine learning word embeddings search engine learning to rank trec document ranking evaluation trec deep learning benchmarking recommender systems expected exposure stochastic ranking ms marco bert document retrieval embeddings web ranking demographics search exposure exposure fairness conformer-kernel conformer leaderboard msmarco efficiency adversarial learning reinforcement learning representation learning microsoft duet sigir 2016 neu-ir 2016 sigir2016 text embeddings word2vec query formulation query auto-completion ethics data challegne shared task full retrieval qti query term independence transformer datasets domain transfer transfer learning query evaluation semantic search natural language processing bing ucl university college london sigir 2017 www2017 adhoc retrieval acl2016berlin acl2016 pseudo-relevance feedback artificial intelligence recurrent neural networks rnn ai metrics web qna question-answering neu-ir2016 auto suggest london cntk search solutions 2015 computational network toolkit auto completion dssm pointwise mutual information search solutions contextual search
Tout plus

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