| Routing networks: Adaptive selection of non-linear functions for multi-task learning C Rosenbaum, T Klinger, M Riemer arXiv preprint arXiv:1711.01239, 2017 | 276 | 2017 |
| Eigenoption discovery through the deep successor representation MC Machado, C Rosenbaum, X Guo, M Liu, G Tesauro, M Campbell arXiv preprint arXiv:1710.11089, 2017 | 173 | 2017 |
| Routing networks and the challenges of modular and compositional computation C Rosenbaum, I Cases, M Riemer, T Klinger arXiv preprint arXiv:1904.12774, 2019 | 87 | 2019 |
| Recursive routing networks: Learning to compose modules for language understanding I Cases, C Rosenbaum, M Riemer, A Geiger, T Klinger, A Tamkin, O Li, ... Proceedings of the 2019 Conference of the North American Chapter of the …, 2019 | 30 | 2019 |
| Learning to query, reason, and answer questions on ambiguous texts X Guo, T Klinger, C Rosenbaum, JP Bigus, M Campbell, B Kawas, ... International Conference on Learning Representations, 2016 | 30* | 2016 |
| Deep reinforcement learning with macro-actions IP Durugkar, C Rosenbaum, S Dernbach, S Mahadevan arXiv preprint arXiv:1606.04615, 2016 | 28 | 2016 |
| On the role of weight sharing during deep option learning M Riemer, I Cases, C Rosenbaum, M Liu, G Tesauro Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 5519-5526, 2020 | 16 | 2020 |
| Efficiently learning from highly-diverse data sets T Klinger, MD Riemer, C Rosenbaum, B Zhou US Patent App. 16/168,266, 2020 | 8 | 2020 |
| Dispatched routing networks C Rosenbaum, I Cases, M Riemer, A Geiger, L Karttunen, JD Greene, ... Technical Report Stanford AI Lab, NLP Group Tech Report 2019-1, 2019 | 4 | 2019 |
| Routing Networks: Adaptive Selection of Non-linear Functions for Multi-Task Learning.(2017) C Rosenbaum, T Klinger, M Riemer arXiv preprint cs.LG/1711.01239, 2017 | 3 | 2017 |
| GistScore: Learning Better Representations for In-Context Example Selection with Gist Bottlenecks S Gupta, C Rosenbaum, ER Elenberg arXiv preprint arXiv:2311.09606, 2023 | 2 | 2023 |
| Mathematical models of graphical user interfaces DA Ciolek, CGB Rosenbaum, AP Botta US Patent 11,567,851, 2023 | 2 | 2023 |
| Dynamic Composition of Functions for Modular Learning CGB Rosenbaum | 2 | 2020 |
| e-QRAQ: A Multi-turn Reasoning Dataset and Simulator with Explanations C Rosenbaum, T Gao, T Klinger arXiv preprint arXiv:1708.01776, 2017 | 2 | 2017 |
| Routing networks and the challenges of modular and compositional computation, 2019 C Rosenbaum, I Cases, M Riemer, T Klinger URL https://arxiv. org/abs, 1904 | 2 | 1904 |
| Mathematical models of graphical user interfaces DA Ciolek, CGB Rosenbaum, AP Botta US Patent App. 18/089,415, 2023 | | 2023 |
| Processing clusters with mathematical models for message suggestion WA Wolf, M Sclar, CGB Rosenbaum, CD Fox, KQ Weinberger US Patent App. 17/246,263, 2022 | | 2022 |
| CEREAL: Few-Sample Clustering Evaluation NV Nayak, ER Elenberg, C Rosenbaum arXiv preprint arXiv:2210.00064, 2022 | | 2022 |
| Vector-space representations of graphical user interfaces CGB Rosenbaum, AP Botta, AI Montero US Patent App. 16/805,927, 2021 | | 2021 |
| Was man von" Lernen" lernen kann: die Entwicklung eines philosophischen Begriffs C Rosenbaum Verlag nicht ermittelbar, 2010 | | 2010 |
