| High-accuracy low-precision training C De Sa, M Leszczynski, J Zhang, A Marzoev, CR Aberger, K Olukotun, ... arXiv preprint arXiv:1803.03383, 2018 | 123 | 2018 |
| Low-memory neural network training: A technical report NS Sohoni, CR Aberger, M Leszczynski, J Zhang, C Ré arXiv preprint arXiv:1904.10631, 2019 | 97 | 2019 |
| Bootleg: Chasing the tail with self-supervised named entity disambiguation L Orr, M Leszczynski, S Arora, S Wu, N Guha, X Ling, C Re arXiv preprint arXiv:2010.10363, 2020 | 49 | 2020 |
| Kaleidoscope: An efficient, learnable representation for all structured linear maps T Dao, NS Sohoni, A Gu, M Eichhorn, A Blonder, M Leszczynski, A Rudra, ... arXiv preprint arXiv:2012.14966, 2020 | 47 | 2020 |
| Managing ml pipelines: feature stores and the coming wave of embedding ecosystems L Orr, A Sanyal, X Ling, K Goel, M Leszczynski arXiv preprint arXiv:2108.05053, 2021 | 22 | 2021 |
| Cross-domain data integration for named entity disambiguation in biomedical text M Varma, L Orr, S Wu, M Leszczynski, X Ling, C Ré arXiv preprint arXiv:2110.08228, 2021 | 18 | 2021 |
| Understanding the downstream instability of word embeddings M Leszczynski, A May, J Zhang, S Wu, C Aberger, C Ré Proceedings of Machine Learning and Systems 2, 262-290, 2020 | 17 | 2020 |
| TABi: Type-Aware Bi-Encoders for Open-Domain Entity Retrieval M Leszczynski, DY Fu, MF Chen, C Ré arXiv preprint arXiv:2204.08173, 2022 | 11 | 2022 |
| Generating synthetic data for conversational music recommendation using random walks and language models M Leszczynski, R Ganti, S Zhang, K Balog, F Radlinski, F Pereira, ... arXiv preprint arXiv:2301.11489, 2023 | 5 | 2023 |
| Machine solver for physics word problems M Leszczynski, J Moreira | 5 | 2016 |
| Beyond single items: Exploring user preferences in item sets with the conversational playlist curation dataset AT Chaganty, M Leszczynski, S Zhang, R Ganti, K Balog, F Radlinski Proceedings of the 46th International ACM SIGIR Conference on Research and …, 2023 | 3 | 2023 |
| High-accuracy low-precision training CR Aberger, C De Sa, M Leszczynski, A Marzoev, K Olukotun, C Ré, ... arXiv preprint arXiv:1803.03383, 2018 | 3 | 2018 |
| Conversational Music Retrieval with Synthetic Data ME Leszczynski, R Ganti, S Zhang, K Balog, F Radlinski, F Pereira, ... Second Workshop on Interactive Learning for Natural Language Processing at …, 2022 | 2 | 2022 |
| Talk the Walk: Synthetic Data Generation for Conversational Music Recommendation M Leszczynski, S Zhang, R Ganti, K Balog, F Radlinski, F Pereira, ... arXiv preprint arXiv:2301.11489, 2023 | 1 | 2023 |
| Demonstration of Geyser: Provenance Extraction and Applications over Data Science Scripts F Psallidas, ME Leszczynski, MH Namaki, A Floratou, A Agrawal, ... Companion of the 2023 International Conference on Management of Data, 123-126, 2023 | | 2023 |
| Beyond Single Items: Exploring User Preferences in Item Sets with the Conversational Playlist Curation Dataset A Tejasvi Chaganty, M Leszczynski, S Zhang, R Ganti, K Balog, ... arXiv e-prints, arXiv: 2303.06791, 2023 | | 2023 |
| Exploiting Structured Data for Robust and Adaptable Natural Language Representations ME Leszczynski Stanford University, 2023 | | 2023 |
Christopher RéComputer Science, Stanford UniversityAdresse e-mail validée de cs.stanford.edu
