| Vqa: Visual question answering S Antol, A Agrawal, J Lu, M Mitchell, D Batra, CL Zitnick, D Parikh Proceedings of the IEEE international conference on computer vision, 2425-2433, 2015 | 3029 | 2015 |
| From captions to visual concepts and back H Fang, S Gupta, F Iandola, RK Srivastava, L Deng, P Dollár, J Gao, X He, ... Proceedings of the IEEE conference on computer vision and pattern …, 2015 | 1263 | 2015 |
| A neural network approach to context-sensitive generation of conversational responses A Sordoni, M Galley, M Auli, C Brockett, Y Ji, M Mitchell, JY Nie, J Gao, ... arXiv preprint arXiv:1506.06714, 2015 | 852 | 2015 |
| Mitigating unwanted biases with adversarial learning BH Zhang, B Lemoine, M Mitchell Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society, 335-340, 2018 | 494 | 2018 |
| Model cards for model reporting M Mitchell, S Wu, A Zaldivar, P Barnes, L Vasserman, B Hutchinson, ... Proceedings of the conference on fairness, accountability, and transparency …, 2019 | 462 | 2019 |
| Midge: Generating image descriptions from computer vision detections M Mitchell, J Dodge, A Goyal, K Yamaguchi, K Stratos, X Han, A Mensch, ... Proceedings of the 13th Conference of the European Chapter of the …, 2012 | 410 | 2012 |
| Spoken language derived measures for detecting mild cognitive impairment B Roark, M Mitchell, JP Hosom, K Hollingshead, J Kaye IEEE transactions on audio, speech, and language processing 19 (7), 2081-2090, 2011 | 261 | 2011 |
| Language models for image captioning: The quirks and what works J Devlin, H Cheng, H Fang, S Gupta, L Deng, X He, G Zweig, M Mitchell arXiv preprint arXiv:1505.01809, 2015 | 256 | 2015 |
| Visual storytelling TH Huang, F Ferraro, N Mostafazadeh, I Misra, A Agrawal, J Devlin, ... Proceedings of the 2016 Conference of the North American Chapter of the …, 2016 | 251 | 2016 |
| Generating natural questions about an image N Mostafazadeh, I Misra, J Devlin, M Mitchell, X He, L Vanderwende arXiv preprint arXiv:1603.06059, 2016 | 219 | 2016 |
| CLPsych 2015 shared task: Depression and PTSD on Twitter G Coppersmith, M Dredze, C Harman, K Hollingshead, M Mitchell Proceedings of the 2nd Workshop on Computational Linguistics and Clinical …, 2015 | 209 | 2015 |
| Exploring nearest neighbor approaches for image captioning J Devlin, S Gupta, R Girshick, M Mitchell, CL Zitnick arXiv preprint arXiv:1505.04467, 2015 | 168 | 2015 |
| Multi-task learning for mental health using social media text A Benton, M Mitchell, D Hovy arXiv preprint arXiv:1712.03538, 2017 | 163 | 2017 |
| Open domain targeted sentiment M Mitchell, J Aguilar, T Wilson, B Van Durme Proceedings of the 2013 Conference on Empirical Methods in Natural Language …, 2013 | 154 | 2013 |
| deltaBLEU: A discriminative metric for generation tasks with intrinsically diverse targets M Galley, C Brockett, A Sordoni, Y Ji, M Auli, C Quirk, M Mitchell, J Gao, ... arXiv preprint arXiv:1506.06863, 2015 | 151 | 2015 |
| Seeing through the human reporting bias: Visual classifiers from noisy human-centric labels I Misra, C Lawrence Zitnick, M Mitchell, R Girshick Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016 | 147 | 2016 |
| Understanding and predicting importance in images AC Berg, TL Berg, H Daume, J Dodge, A Goyal, X Han, A Mensch, ... 2012 IEEE Conference on Computer Vision and Pattern Recognition, 3562-3569, 2012 | 144 | 2012 |
| 50 years of test (un) fairness: Lessons for machine learning B Hutchinson, M Mitchell Proceedings of the Conference on Fairness, Accountability, and Transparency …, 2019 | 139 | 2019 |
| Quantifying the language of schizophrenia in social media M Mitchell, K Hollingshead, G Coppersmith Proceedings of the 2nd workshop on Computational linguistics and clinical …, 2015 | 121 | 2015 |
| Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing ID Raji, A Smart, RN White, M Mitchell, T Gebru, B Hutchinson, ... Proceedings of the 2020 conference on fairness, accountability, and …, 2020 | 111 | 2020 |
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