| NoScope: Optimizing Neural Network Queries over Video at Scale D Kang, J Emmons, F Abuzaid, P Bailis, M Zaharia Proceedings of the VLDB Endowment 10 (11), 1586-1597, 2017 | 412* | 2017 |
| 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 | 323 | 2017 |
| MLPerf Training Benchmark P Mattson, C Cheng, C Coleman, G Diamos, P Micikevicius, D Patterson, ... arXiv preprint arXiv:1910.01500, 2019 | 246 | 2019 |
| BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-based Video Analytics D Kang, P Bailis, M Zaharia PVLDB 13 (4), 2019 | 158* | 2019 |
| Testing Robustness Against Unforeseen Adversaries D Kang, Y Sun, D Hendrycks, T Brown, J Steinhardt arXiv preprint arXiv:1908.08016, 2019 | 141* | 2019 |
| 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 | 107 | 2019 |
| Network Offloading Policies for Cloud Robotics: a Learning-based Approach S Chinchali, A Sharma, J Harrison, A Elhafsi, D Kang, E Pergament, ... arXiv preprint arXiv:1902.05703, 2019 | 99 | 2019 |
| Model Assertions for Monitoring and Improving ML Models D Kang, D Raghavan, P Bailis, M Zaharia MLSys, 2020 | 85* | 2020 |
| LIT: Learned Intermediate Representation Training for Model Compression A Koratana, D Kang, P Bailis, M Zaharia International Conference on Machine Learning, 3509-3518, 2019 | 66* | 2019 |
| Improved Natural Language Generation via Loss Truncation D Kang, T Hashimoto ACL, 2020 | 61 | 2020 |
| GERV: a statistical method for generative evaluation of regulatory variants for transcription factor binding H Zeng, T Hashimoto, DD Kang, DK Gifford Bioinformatics 32 (4), 490-496, 2016 | 43 | 2016 |
| Jointly Optimizing Preprocessing and Inference for DNN-based Visual Analytics D Kang, A Mathur, T Veeramacheneni, P Bailis, M Zaharia arXiv preprint arXiv:2007.13005, 2020 | 27 | 2020 |
| Willump: A Statistically-Aware End-to-end Optimizer for Machine Learning Inference P Kraft, D Kang, D Narayanan, S Palkar, P Bailis, M Zaharia MLSys, 2020 | 20 | 2020 |
| A synergistic DNA logic predicts genome-wide chromatin accessibility T Hashimoto, RI Sherwood, DD Kang, N Rajagopal, AA Barkal, H Zeng, ... Genome research 26 (10), 1430-1440, 2016 | 19 | 2016 |
| TASTI: Semantic Indexes for Machine Learning-based Queries over Unstructured Data D Kang, J Guibas, PD Bailis, T Hashimoto, M Zaharia Proceedings of the 2022 International Conference on Management of Data, 1934 …, 2022 | 17* | 2022 |
| Selection of transform size in video coding RS Bultje, D Kang US Patent 9,219,915, 2015 | 16 | 2015 |
| Approximate Selection with Guarantees using Proxies D Kang, E Gan, P Bailis, T Hashimoto, M Zaharia PVLDB, 2020 | 15 | 2020 |
| Finding label and model errors in perception data with learned observation assertions D Kang, N Arechiga, S Pillai, PD Bailis, M Zaharia Proceedings of the 2022 International Conference on Management of Data, 496-505, 2022 | 10 | 2022 |
| VIVA: An End-to-End System for Interactive Video Analytics D Kang, F Romero, P Bailis, C Kozyrakis, M Zaharia CIDR, 2022 | 8 | 2022 |
| Accelerating Approximate Aggregation Queries with Expensive Predicates D Kang, J Guibas, P Bailis, T Hashimoto, Y Sun, M Zaharia arXiv preprint arXiv:2108.06313, 2021 | 8 | 2021 |
Matei ZahariaStanford DAWN Lab and DatabricksVerified email at cs.stanford.edu
