S
Shresth Verma
Researcher at Indian Institute of Information Technology and Management, Gwalior
Publications - 19
Citations - 345
Shresth Verma is an academic researcher from Indian Institute of Information Technology and Management, Gwalior. The author has contributed to research in topics: Computer science & Key (lock). The author has an hindex of 2, co-authored 9 publications receiving 18 citations. Previous affiliations of Shresth Verma include Indian Institutes of Information Technology.
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Journal ArticleDOI
The Astropy Project: Sustaining and Growing a Community-oriented Open-source Project and the Latest Major Release (v5.0) of the Core Package
The Astropy Collaboration,Adrian M. Price-Whelan,Pey Lian Lim,Nicholas Earl,Nathaniel Starkman,Larry Bradley,David L. Shupe,Aarya A. Patil,Lia Corrales,C. E. Brasseur,M. Nöthe,Axel Donath,Erik Tollerud,Brett M. Morris,Adam Ginsburg,Eero Vaher,B. A. Weaver,James Tocknell,William Jamieson,M. H. van Kerkwijk,Thomas P. Robitaille,Bruce Merry,Matteo Bachetti,H. M. Gunther,Tom Aldcroft,Jaime A. Alvarado-Montes,Anne M. Archibald,A. B'odi,Shreyas Bapat,Geert Barentsen,Juanjo Baz'an,Manish J Biswas,Médéric Boquien,D. J. Burke,D Di Cara,Mihai Cara,Kyle E. Conroy,Simon Conseil,Matt Craig,Robert M. Cross,Kelle L. Cruz,Francesco D'Eugenio,Nadia Dencheva,Hadrien A. R. Devillepoix,J. P. Dietrich,Arthur Eigenbrot,Thomas Erben,Leonardo Ferreira,Daniel Foreman-Mackey,R. T. Fox,Nabil Freij,Suyog Garg,Robel Geda,Lauren Glattly,Yash Gondhalekar,Karl D. Gordon,David Grant,Perry Greenfield,A. M. Groener,S. Guest,Sebastián Gurovich,Rasmus Handberg,Akeem Hart,Zac Hatfield-Dodds,Derek Homeier,Griffin Hosseinzadeh,Tim Jenness,Craig Jones,Prajwel Joseph,J. Bryce Kalmbach,Emir Karamehmetoglu,M. Kaluszy'nski,Michaelann Kelley,Nicholas S. Kern,Wolfgang Kerzendorf,Eric W. Koch,Shankar Kulumani,Antony H. Lee,Chun Ly,Zhiyuan Mao,Conor D. MacBride,Jakob M. Maljaars,Demitri Muna,Nellie Appy Murphy,Henrik Norman,R. G. O'Steen,Kyle A. Oman,Camilla Pacifici,Sergio Pascual,J. Pascual-Granado,Rohit R Patil,G. I. Perren,T. E. Pickering,Tanuja Rastogi,Benjamin R. Roulston,Daniel F Ryan,Eli S. Rykoff,J. Sabater,Parikshit Sakurikar,Jesús Busto Salgado,Aniket Sanghi,Nicholas Saunders,V. G. Savchenko,L. C. Schwardt,Michael Seifert-Eckert,Albert J. Shih,A. S. Jain,G. R. Shukla,J. Sick,Chris Simpson,Sudheesh Singanamalla,Leo Singer,Jaladh Singhal,Manodeep Sinha,B. SipHocz,Lee R. Spitler,David Stansby,Ole Streicher,Jani vSumak,John D. Swinbank,Dan S. Taranu,N. B. Tewary,Grant R. Tremblay,Miguel De Val-Borro,Samuel J. Van Kooten,Zlatan Vasovi'c,Shresth Verma,José Vinícius de Miranda Cardoso,Peter K. G. Williams,Tom J. Wilson,Benjamin Winkel,W. M. Wood-Vasey,Rui Xue,Peter Yoachim,Chenchen Zhang,Andrea Zonca +135 more
TL;DR: Astropy as mentioned in this paper is a Python package that provides commonly needed functionality to the astronomical community, such as astronomy, astronomy, and astronomy data visualization, as well as other related projects and packages.
Journal ArticleDOI
SunPy: A Python package for Solar Physics
Stuart Mumford,N. Freij,Steven Christe,Jack Ireland,Florian Mayer,V. Keith Hughitt,Albert Y. Shih,Daniel Ryan,Simon Liedtke,David Pérez-Suárez,Pritish Chakraborty,Vishnunarayan K,Andrew Inglis,Punyaslok Pattnaik,Brigitta Sipőcz,Rishabh Sharma,Andrew J. Leonard,David Stansby,Russell J. Hewett,Alex Hamilton,Laura A. Hayes,Asish Panda,Matt Earnshaw,Nitin Choudhary,Ankit Kumar,Prateek Chanda,Akramul Haque,Michael S. Kirk,Michael Mueller,Sudarshan Konge,Rajul Srivastava,Yash Jain,Samuel Bennett,Ankit Baruah,W. T. Barnes,Michael Charlton,Shane A. Maloney,Nicky Chorley,Himanshu,Sanskar Modi,James Mason,Naman,Jose Ivan Campos Rozo,Larry Manley,Agneet Chatterjee,John G Evans,Michael Malocha,Monica G. Bobra,Sourav Ghosh,Airmansmith,Dominik Stańczak,Ruben De Visscher,Shresth Verma,Ankit Agrawal,Dumindu Buddhika,Swapnil Sharma,Jongyeob Park,Matt Bates,Dhruv Goel,Garrison Taylor,Goran Cetusic,Jacob,Mateo Inchaurrandieta,S. Dacie,Sanjeev Dubey,Deepankar Sharma,Erik M. Bray,Jai Ram Rideout,S. Zahniy,Tomas Meszaros,Abhigyan Bose,André Chicrala,Ankit,Chloé Guennou,Daniel D'Avella,Daniel Williams,Jordan Ballew,Nicholas A. Murphy,Priyank Lodha,Thomas P. Robitaille,Yash Krishan,Andrew Hill,Arthur Eigenbrot,Benjamin Mampaey,Bernhard M. Wiedemann,Carlos Molina,Duygu Keşkek,Ishtyaq Habib,Joseph Letts,Juanjo Bazán,Quinn Arbolante,Reid Gomillion,Yash Kothari,Yash Sharma,Abigail L. Stevens,Adrian M. Price-Whelan,Ambar Mehrotra,Arseniy Kustov,Brandon Stone,Trung Kien Dang,Emmanuel Arias,Fionnlagh Mackenzie Dover,Freek Verstringe,Gulshan Kumar,Harsh Mathur,Igor Babuschkin,Jaylen Wimbish,Juan Camilo Buitrago-Casas,Kalpesh Krishna,Kaustubh Hiware,Manas Mangaonkar,Matthew Mendero,Mickaël Schoentgen,Norbert G. Gyenge,Ole Streicher,Rajasekhar Reddy Mekala,Rishabh Mishra,Shashank Srikanth,Sarthak Jain,Tannmay Yadav,Tessa D. Wilkinson,Tiago M. D. Pereira,Yudhik Agrawal,Jamescalixto,Yasintoda,Sophie A. Murray +125 more
TL;DR: Stuart J. Mumford’s aim was to provide a platform for the next generation of interpreters and interpreters to be able to understand each other better and provide a voice to the voiceless.
Proceedings ArticleDOI
Deep Reinforcement Learning for Single-Shot Diagnosis and Adaptation in Damaged Robots
TL;DR: In this article, the authors propose a damage aware control architecture which diagnoses the damage prior to gait selection while also incorporating domain randomization in the damage space for learning a robust policy.
Journal Article
Decision-Focused Learning in Restless Multi-Armed Bandits with Application to Maternal and Child Care Domain
Kaihou Wang,Shresth Verma,Aditya Mate,Sanket Shah,Aparna Taneja,Neha Madhiwalla,Aparna Hegde,Milind Tambe +7 more
TL;DR: A novel approach for decision-focused learning in RMAB that directly trains the predictive model to maximize the Whittle index solution quality is proposed, and it is observed that two-stage learning consistently converges to a slightly smaller predictive loss, while DF-Whittle outperforms two- stage on all solution quality evaluation metrics.
Posted Content
Deep Reinforcement Learning for Single-Shot Diagnosis and Adaptation in Damaged Robots
TL;DR: This work proposes a damage aware control architecture which diagnoses the damage prior to gait selection while also incorporating domain randomization in the damage space for learning a robust policy.