{"paper":{"title":"On Optimizing Human-Machine Task Assignments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.HC","authors_text":"Aayush Mudgal, Abhinav S., Aditya Nadimpalli, Aditya Raisinghani, Akanksha Periwal, Alankrit Mishra, Amod Agrawal, Andreas Veit, Ankit Dhall, Anshu Aviral, Anuj Pahuja, Anurag. D. Yadav, Arya Aishwarya, Aurgho Bhattacharjee, Ayush Sagar, Ayush Shah, Ayush Sharma, Chandana G, Chinmaya Devaraj, Darpana Sinha, Faizal Siddiqui, James Davis, Kanniganti Abhishek, Karthik Paga, K. Bala Vignesh, Kinjal Jain, Meghana Kasula, Michael Wilber, Munakala Sree Nihit, Nisarg Thakkar, Parth Kundaliya, Prithvijit Chakrabarty, Rajan Vaish, Sanket Gupte, Sarveshwaran Dhanasekar, Serge Belongie, Sharath N. Sridhar, Shashi Kumar, Shuchita Gupta, Shweta Sharma, Sidharth Chaturvedi, Simran Kapur, Utkarsh Dwivedi, Utkarsh Mathur, Utkarsh Verma, Venkata Karthik Gullapalli, Vikas Sharma, Virender Singh, Vishal Anand, Yash Chandak, Yashovardhan Sharma","submitted_at":"2015-09-24T21:38:07Z","abstract_excerpt":"When crowdsourcing systems are used in combination with machine inference systems in the real world, they benefit the most when the machine system is deeply integrated with the crowd workers. However, if researchers wish to integrate the crowd with \"off-the-shelf\" machine classifiers, this deep integration is not always possible. This work explores two strategies to increase accuracy and decrease cost under this setting. First, we show that reordering tasks presented to the human can create a significant accuracy improvement. Further, we show that greedily choosing parameters to maximize machi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1509.07543","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}