Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Ayush Maheshwari"'
Autor:
Pradeep M. K. Nair, Karishma Silwal, Jyoti Keswani, Sucheta Kriplani, Vakeel Khan, Ayush Maheshwari, Mili Arpan Shah, Naga Jyoti, Vinutha Rao, Cijith Sreedhar, Kinjal Dilipsinh Bhalavat, Renjish Mohanan, Jerin Subha M, Rakesh Gupta, Hemanshu Sharma, Gulab Rai Tewani
Publikováno v:
Frontiers in Pain Research, Vol 4 (2023)
Externí odkaz:
https://doaj.org/article/ea09f96bc7f24beba69bfb6e2846845f
Autor:
Pradeep M. K. Nair, Sucheta Kriplani, Prakash Babu Kodali, Ayush Maheshwari, Kinjal Dilipsinh Bhalavat, Deepika Singh, Sanjeev Saini, Dinesh Yadav, Jyoti Keswani, Karishma Silwal, Hemanshu Sharma, Gulab Rai Tewani
Publikováno v:
Frontiers in Pain Research, Vol 4 (2023)
ObjectivesThe aim of this study is to identify the characteristics of patients who underwent yoga therapy for pain in yoga and naturopathy clinical settings in India.MethodsElectronic medical records of patients who received yoga therapy for pain in
Externí odkaz:
https://doaj.org/article/ea6d3691469b4816aeac4a16b44dd498
Autor:
Shreya Singh, Ayush Maheshwari
Publikováno v:
2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6acc67bf3f8d202ba4ea7d3ba6283dca
https://doi.org/10.1109/icac3n56670.2022.10074052
https://doi.org/10.1109/icac3n56670.2022.10074052
Autor:
Gulab Rai Tewani, Karishma Silwal, Dinesh Yadav, Aarfa Siddiqui, Sucheta Kriplani, Ayush Maheshwari, Varsha Vijay Nathani, Deepika Singh, Kunal Gyanchandani, Rukmani Iyer, Vakeel Khan, Piyush Dubey, Hemanshu Sharma, Pradeep M.K. Nair
Publikováno v:
Medicine. 102:e33260
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4c0feaae5ec74e503e6015a0895e9913
https://doi.org/10.1097/md.0000000000033260
https://doi.org/10.1097/md.0000000000033260
We introduce UDAAN, an open-source post-editing tool that can reduce manual editing efforts to quickly produce publishable-standard documents in several Indic languages. UDAAN has an end-to-end Machine Translation (MT) plus post-editing pipeline wher
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d193082b27e8cbf5201580647030c63c
http://arxiv.org/abs/2203.01644
http://arxiv.org/abs/2203.01644
Publikováno v:
EACL
We consider the problem of multi-label classification where the labels lie in a hierarchy. However, unlike most existing works in hierarchical multi-label classification, we do not assume that the label-hierarchy is known. Encouraged by the recent su
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::06cc8e7e1c7620cb431432c6b5752951
http://arxiv.org/abs/2101.04997
http://arxiv.org/abs/2101.04997
Autor:
KrishnaTeja Killamsetty, Oishik Chatterjee, Rishabh Iyer, Ayush Maheshwari, Ganesh Ramakrishnan
Publikováno v:
ACL/IJCNLP (Findings)
The paradigm of data programming, which uses weak supervision in the form of rules/labelling functions, and semi-supervised learning, which augments small amounts of labelled data with a large unlabelled dataset, have shown great promise in several t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dcf0ef8b79a677e6c46c384066fd2459
https://doi.org/10.18653/v1/2021.findings-acl.408
https://doi.org/10.18653/v1/2021.findings-acl.408
Publikováno v:
ACL/IJCNLP (Findings)
Recently, unsupervised parsing of syntactic trees has gained considerable attention. A prototypical approach to such unsupervised parsing employs reinforcement learning and auto-encoders. However, no mechanism ensures that the learnt model leverages
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::40ec5dc3c3c3858bf202ba46f2033ffc
https://doi.org/10.18653/v1/2021.findings-acl.436
https://doi.org/10.18653/v1/2021.findings-acl.436
Autor:
Manjesh K. Hanawal, Ritesh Kumar, Atul Sahay, Ganesh Ramakrishnan, Kavi Arya, Ayush Maheshwari
Publikováno v:
IJCNN
Recursive neural networks (RvNN) have been shown useful for learning sentence representations and helped achieve competitive performance on several natural language inference tasks. However, recent RvNN-based models fail to learn simple grammar and m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8b5efd3546236acdef5739e23a8cff59
Publikováno v:
COMSNETS
The problem of representation learning on graph can be difficult due to limited knowledge of training data and large presence of missing edges. Real-world social networks do not provide complete information about the network due to hidden information
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::abae8c7b54f6fef10059421c4d8ee814
https://doi.org/10.1109/comsnets.2019.8711221
https://doi.org/10.1109/comsnets.2019.8711221