| HarvardX and MITx: Two years of open online courses fall 2012-summer 2014 AD Ho, I Chuang, J Reich, CA Coleman, J Whitehill, CG Northcutt, ... MIT Office of Digital Learning; HarvardX Research Committee, 2015 | 789 | 2015 |
| MLPerf Inference Benchmark VJ Reddi, C Cheng, D Kanter, P Mattson, G Schmuelling, CJ Wu, ... 2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture …, 2020 | 665* | 2020 |
| DAWNBench: An End-to-End Deep Learning Benchmark and Competition C Coleman, D Narayanan, D Kang, T Zhao, J Zhang, L Nardi, P Bailis, ... NIPS ML Systems Workshop, 2017 | 401 | 2017 |
| MLPerf Training Benchmark P Mattson, C Cheng, C Coleman, G Diamos, P Micikevicius, D Patterson, ... arXiv preprint arXiv:1910.01500, 2019 | 360 | 2019 |
| Selection via proxy: Efficient data selection for deep learning C Coleman, C Yeh, S Mussmann, B Mirzasoleiman, P Bailis, P Liang, ... arXiv preprint arXiv:1906.11829, 2019 | 358 | 2019 |
| MLPerf: An industry standard benchmark suite for machine learning performance P Mattson, VJ Reddi, C Cheng, C Coleman, G Diamos, D Kanter, ... IEEE Micro 40 (2), 8-16, 2020 | 202 | 2020 |
| Analysis of dawnbench, a time-to-accuracy machine learning performance benchmark C Coleman, D Kang, D Narayanan, L Nardi, T Zhao, J Zhang, P Bailis, ... ACM SIGOPS Operating Systems Review 53 (1), 14-25, 2019 | 148 | 2019 |
| Beyond prediction: First steps toward automatic intervention in MOOC student stopout J Whitehill, J Williams, G Lopez, C Coleman, J Reich Available at SSRN 2611750, 2015 | 143 | 2015 |
| Dataperf: Benchmarks for data-centric ai development M Mazumder, C Banbury, X Yao, B Karlaš, W Gaviria Rojas, S Diamos, ... Advances in Neural Information Processing Systems 36, 5320-5347, 2023 | 136 | 2023 |
| Federated benchmarking of medical artificial intelligence with MedPerf A Karargyris, R Umeton, MJ Sheller, A Aristizabal, J George, A Wuest, ... Nature machine intelligence 5 (7), 799-810, 2023 | 94 | 2023 |
| Probabilistic use cases: Discovering behavioral patterns for predicting certification CA Coleman, DT Seaton, I Chuang Proceedings of the second (2015) acm conference on learning@ scale, 141-148, 2015 | 78 | 2015 |
| The dollar street dataset: Images representing the geographic and socioeconomic diversity of the world W Gaviria Rojas, S Diamos, K Kini, D Kanter, V Janapa Reddi, ... Advances in Neural Information Processing Systems 35, 12979-12990, 2022 | 70 | 2022 |
| Teacher enrollment in MITx MOOCs: Are we educating educators? D Seaton, C Coleman, J Daries, I Chuang Available at SSRN 2515385, 2014 | 47 | 2014 |
| Beyond time-on-task: The relationship between spaced study and certification in MOOCs YR Miyamoto, C Coleman, JJ Williams, J Whitehill, S Nesterko, J Reich Journal of Learning Analytics 2 (2), 47-69, 2015 | 46 | 2015 |
| Similarity search for efficient active learning and search of rare concepts C Coleman, E Chou, J Katz-Samuels, S Culatana, P Bailis, AC Berg, ... Proceedings of the AAAI Conference on Artificial Intelligence 36 (6), 6402-6410, 2022 | 36 | 2022 |
| Introducing v0. 5 of the ai safety benchmark from mlcommons B Vidgen, A Agrawal, AM Ahmed, V Akinwande, N Al-Nuaimi, N Alfaraj, ... arXiv preprint arXiv:2404.12241, 2024 | 33 | 2024 |
| Medperf: open benchmarking platform for medical artificial intelligence using federated evaluation A Karargyris, R Umeton, MJ Sheller, A Aristizabal, J George, S Bala, ... arXiv preprint arXiv:2110.01406, 2021 | 24 | 2021 |
| DMLR: Data-centric Machine Learning Research--Past, Present and Future L Oala, M Maskey, L Bat-Leah, A Parrish, NM Gürel, TS Kuo, Y Liu, R Dror, ... arXiv preprint arXiv:2311.13028, 2023 | 13 | 2023 |
| Analysis of video use in edX courses DT Seaton, AJ Rodenius, CA Coleman, DE Pritchard, I Chuang AIED 2013 Workshops Proceedings Volume, 57, 2013 | 6 | 2013 |
| Project C Coleman, E Chou, J Katz-Samuels, S Culatana, P Bailis, AC Berg, ... | 1 | 2022 |
Peter BailisGoogleE-mail megerősítve itt: google.com