| On the opportunities and risks of foundation models R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ... arXiv preprint arXiv:2108.07258, 2021 | 4676 | 2021 |
| Efficiently modeling long sequences with structured state spaces A Gu, K Goel, C Ré arXiv preprint arXiv:2111.00396, 2021 | 1626 | 2021 |
| Combining recurrent, convolutional, and continuous-time models with linear state space layers A Gu, I Johnson, K Goel, K Saab, T Dao, A Rudra, C Ré Advances in neural information processing systems 34, 572-585, 2021 | 527 | 2021 |
| On the parameterization and initialization of diagonal state space models A Gu, K Goel, A Gupta, C Ré Advances in Neural Information Processing Systems 35, 35971-35983, 2022 | 316 | 2022 |
| It’s raw! audio generation with state-space models K Goel, A Gu, C Donahue, C Ré International Conference on Machine Learning, 7616-7633, 2022 | 213 | 2022 |
| S4nd: Modeling images and videos as multidimensional signals with state spaces E Nguyen, K Goel, A Gu, G Downs, P Shah, T Dao, S Baccus, C Ré Advances in neural information processing systems 35, 2846-2861, 2022 | 194 | 2022 |
| Robustness gym: Unifying the NLP evaluation landscape K Goel, N Rajani, J Vig, S Tan, J Wu, S Zheng, C Xiong, M Bansal, C Ré arXiv preprint arXiv:2101.04840, 2021 | 145 | 2021 |
| On the opportunities and risks of foundation models. arXiv 2021 R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ... arXiv preprint arXiv:2108.07258, 2023 | 107 | 2023 |
| Model patching: Closing the subgroup performance gap with data augmentation K Goel, A Gu, Y Li, C Ré arXiv preprint arXiv:2008.06775, 2020 | 92 | 2020 |
| Mandoline: Model evaluation under distribution shift M Chen, K Goel, NS Sohoni, F Poms, K Fatahalian, C Ré International conference on machine learning, 1617-1629, 2021 | 90 | 2021 |
| On the opportunities and risks of foundation models (2021) R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ... arXiv preprint arXiv:2108.07258, 2022 | 74 | 2022 |
| Effectively modeling time series with simple discrete state spaces M Zhang, KK Saab, M Poli, T Dao, K Goel, C Ré arXiv preprint arXiv:2303.09489, 2023 | 48 | 2023 |
| 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 | 25 | 2021 |
| SummVis: Interactive visual analysis of models, data, and evaluation for text summarization J Vig, W Kryściński, K Goel, NF Rajani arXiv preprint arXiv:2104.07605, 2021 | 23 | 2021 |
| Learning procedural abstractions and evaluating discrete latent temporal structure K Goel, E Brunskill International Conference on Learning Representations, 2019 | 21 | 2019 |
| Personalized benchmarking with the ludwig benchmarking toolkit A Narayan, P Molino, K Goel, W Neiswanger, C Re arXiv preprint arXiv:2111.04260, 2021 | 12 | 2021 |
| Data Management Opportunities for Foundation Models. LJ Orr, K Goel, C Ré CIDR, 2022 | 9 | 2022 |
| Sample efficient policy search for optimal stopping domains K Goel, C Dann, E Brunskill arXiv preprint arXiv:1702.06238, 2017 | 9 | 2017 |
| Octopus: A framework for cost-quality-time optimization in crowdsourcing K Goel, S Rajpal, M Mausam Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 5 …, 2017 | 8 | 2017 |
| Goodwill hunting: Analyzing and repurposing off-the-shelf named entity linking systems K Goel, L Orr, NF Rajani, J Vig, C Ré Proceedings of the 2021 Conference of the North American Chapter of the …, 2021 | 7 | 2021 |
Christopher RéComputer Science, Stanford University確認したメール アドレス: cs.stanford.edu