| Learning what and where to attend with humans in the loop D Linsley, D Shiebler, S Eberhardt, T Serre ICLR 2019, 2018 | 170* | 2018 |
| Systems and methods for sensor-based vehicle crash prediction, detection, and reconstruction B Cordova, E Vaisman, D Shiebler US Patent 11,220,258, 2022 | 64 | 2022 |
| Category theory in machine learning D Shiebler, B Gavranović, P Wilson Applied Category Theory 2021, 2021 | 56 | 2021 |
| Systems and methods for detecting and assessing distracted drivers B Cordova, R Finegold, D Shiebler, K Farrell US Patent 10,158,977, 2018 | 40 | 2018 |
| Global-and-local attention networks for visual recognition D Linsley, D Shiebler, S Eberhardt, T Serre Benefits 64 (01), 2018 | 39 | 2018 |
| Tuning word2vec for large scale recommendation systems BP Chamberlain, E Rossi, D Shiebler, S Sedhain, MM Bronstein Proceedings of the 14th ACM Conference on Recommender Systems, 732-737, 2020 | 33 | 2020 |
| Method and system for accident detection using contextual data B Cordova, R Finegold, D Shiebler, E Vaisman US Patent 10,930,090, 2021 | 20 | 2021 |
| Categorical stochastic processes and likelihood D Shiebler Compositionality [https://doi.org/10.32408/compositionality-3-1] 3 (1), 2021 | 15 | 2021 |
| Systems and methods for detecting airbag deployment resulting from a vehicle crash B Cordova, E Vaisman, D Shiebler US Patent 10,232,847, 2019 | 15 | 2019 |
| Developments in AI and machine learning for neuroimaging S O’Sullivan, F Jeanquartier, C Jean-Quartier, A Holzinger, D Shiebler, ... Artificial Intelligence and Machine Learning for Digital Pathology: State-of …, 2020 | 11 | 2020 |
| Functorial clustering via simplicial complexes D Shiebler Topological Data Analysis and Beyond [NeurIPS 2020], 2020 | 9 | 2020 |
| Functorial manifold learning D Shiebler arXiv preprint arXiv:2011.07435, 2020 | 7 | 2020 |
| Incremental monoidal grammars D Shiebler, A Toumi, M Sadrzadeh arXiv preprint arXiv:2001.02296, 2020 | 6 | 2020 |
| Learning what and where to attend. arXiv D Linsley, D Shiebler, S Eberhardt, T Serre arXiv preprint arXiv:1805.08819, 2018 | 5 | 2018 |
| Making Machine Learning Easy with Embeddings D Shiebler, A Tayal SysML 2018, 2018 | 5* | 2018 |
| Learning what and where to attend. arXiv 2018 D Linsley, D Shiebler, S Eberhardt, T Serre arXiv preprint arXiv:1805.08819, 0 | 5 | |
| Compositionality and functorial invariants in machine learning D Shiebler University of Oxford, 2023 | 4 | 2023 |
| Lessons learned addressing dataset bias in model-based candidate generation at Twitter A Virani, J Baxter, D Shiebler, P Gautier, S Verma, Y Xia, A Sharma, ... arXiv preprint arXiv:2105.09293, 2021 | 4 | 2021 |
| Systems and methods for detecting and assessing distracted drivers B Cordova, R Finegold, D Shiebler, K Farrell US Patent 10,455,361, 2019 | 4 | 2019 |
| Fighting Redundancy and Model Decay with Embeddings D Shiebler Common Model Infrastructure Workshop [KDD 2018], 2018 | 4 | 2018 |
Sven EberhardtBrown University在 brown.edu 的电子邮件经过验证