Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Dwarakanath, Anurag"'
Autor:
FitzGerald, Jack, Ananthakrishnan, Shankar, Arkoudas, Konstantine, Bernardi, Davide, Bhagia, Abhishek, Bovi, Claudio Delli, Cao, Jin, Chada, Rakesh, Chauhan, Amit, Chen, Luoxin, Dwarakanath, Anurag, Dwivedi, Satyam, Gojayev, Turan, Gopalakrishnan, Karthik, Gueudre, Thomas, Hakkani-Tur, Dilek, Hamza, Wael, Hueser, Jonathan, Jose, Kevin Martin, Khan, Haidar, Liu, Beiye, Lu, Jianhua, Manzotti, Alessandro, Natarajan, Pradeep, Owczarzak, Karolina, Oz, Gokmen, Palumbo, Enrico, Peris, Charith, Prakash, Chandana Satya, Rawls, Stephen, Rosenbaum, Andy, Shenoy, Anjali, Soltan, Saleh, Sridhar, Mukund Harakere, Tan, Liz, Triefenbach, Fabian, Wei, Pan, Yu, Haiyang, Zheng, Shuai, Tur, Gokhan, Natarajan, Prem
Publikováno v:
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), August 14-18, 2022, Washington, DC, USA
We present results from a large-scale experiment on pretraining encoders with non-embedding parameter counts ranging from 700M to 9.3B, their subsequent distillation into smaller models ranging from 17M-170M parameters, and their application to the N
Externí odkaz:
http://arxiv.org/abs/2206.07808
In this paper, we present the Metamorphic Testing of an in-use deep learning based forecasting application. The application looks at the past data of system characteristics (e.g. `memory allocation') to predict outages in the future. We focus on two
Externí odkaz:
http://arxiv.org/abs/1907.06632
In this paper we study the trustworthiness of the crowd for crowdsourced software development. Through the study of literature from various domains, we present the risks that impact the trustworthiness in an enterprise context. We survey known techni
Externí odkaz:
http://arxiv.org/abs/1809.09477
Automated software testing involves the execution of test scripts by a machine instead of being manually run. This significantly reduces the amount of manual time & effort needed and thus is of great interest to the software testing industry. There h
Externí odkaz:
http://arxiv.org/abs/1809.09455
Autor:
Dwarakanath, Anurag, Jankiti, Aruna
The software system under test can be modeled as a graph comprising of a set of vertices, (V) and a set of edges, (E). Test Cases are Test Paths over the graph meeting a particular test criterion. In this paper, we present a method to achieve the min
Externí odkaz:
http://arxiv.org/abs/1809.08446
Test automation involves the automatic execution of test scripts instead of being manually run. This significantly reduces the amount of manual effort needed and thus is of great interest to the software testing industry. There are two key problems i
Externí odkaz:
http://arxiv.org/abs/1809.08100
Autor:
Dwarakanath, Anurag, Ahuja, Manish, Sikand, Samarth, Rao, Raghotham M., Bose, R. P. Jagadeesh Chandra, Dubash, Neville, Podder, Sanjay
We have recently witnessed tremendous success of Machine Learning (ML) in practical applications. Computer vision, speech recognition and language translation have all seen a near human level performance. We expect, in the near future, most business
Externí odkaz:
http://arxiv.org/abs/1808.05353
Autor:
FitzGerald, Jack, Ananthakrishnan, Shankar, Arkoudas, Konstantine, Bernardi, Davide, Bhagia, Abhishek, Bovi, Claudio Delli, Cao, Jin, Chada, Rakesh, Chauhan, Amit, Chen, Luoxin, Dwarakanath, Anurag, Dwivedi, Satyam, Gojayev, Turan, Gopalakrishnan, Karthik, Gueudre, Thomas, Hakkani-Tur, Dilek, Hamza, Wael, Hueser, Jonathan, Jose, Kevin Martin, Khan, Haidar, Liu, Beiye, Lu, Jianhua, Manzotti, Alessandro, Natarajan, Pradeep, Owczarzak, Karolina, Oz, Gokmen, Palumbo, Enrico, Peris, Charith, Prakash, Chandana Satya, Rawls, Stephen, Rosenbaum, Andy, Shenoy, Anjali, Soltan, Saleh, Sridhar, Mukund Harakere, Tan, Liz, Triefenbach, Fabian, Wei, Pan, Yu, Haiyang, Zheng, Shuai, Tur, Gokhan, Natarajan, Prem
Publikováno v:
Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.
We present results from a large-scale experiment on pretraining encoders with non-embedding parameter counts ranging from 700M to 9.3B, their subsequent distillation into smaller models ranging from 17M-170M parameters, and their application to the N
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3be35e4212a3f3d4bb494e7d071b86ca
https://doi.org/10.1145/3534678.3539173
https://doi.org/10.1145/3534678.3539173
Publikováno v:
Proceedings of the 38th International Conference on Software Engineering Companion; 2016, p41-50, 10p
Autor:
Dwarakanath, Anurag, Chintala, Upendra, Shrikanth, N.C., Virdi, Gurdeep, Kass, Alex, Chandran, Anitha, Sengupta, Shubhashis, Paul, Sanjoy
Publikováno v:
2015 IEEE/ACM 2nd International Workshop on CrowdSourcing in Software Engineering; 2015, p8-14, 7p