| 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 | 4265 | 2021 |
| GQA: A new dataset for real-world visual reasoning and compositional question answering DA Hudson, CD Manning arXiv preprint arXiv:1902.09506, 2019 | 1931 | 2019 |
| Holistic evaluation of language models P Liang, R Bommasani, T Lee, D Tsipras, D Soylu, M Yasunaga, Y Zhang, ... arXiv preprint arXiv:2211.09110, 2022 | 1084 | 2022 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context G Team, P Georgiev, VI Lei, R Burnell, L Bai, A Gulati, G Tanzer, ... arXiv preprint arXiv:2403.05530, 2024 | 671* | 2024 |
| Compositional attention networks for machine reasoning DA Hudson, CD Manning arXiv preprint arXiv:1803.03067, 2018 | 639 | 2018 |
| Learning by abstraction: The neural state machine D Hudson, CD Manning Advances in neural information processing systems 32, 2019 | 314 | 2019 |
| Generative adversarial transformers DA Hudson, L Zitnick International conference on machine learning, 4487-4499, 2021 | 217 | 2021 |
| On the opportunities and risks of foundation models. arXiv R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ... arXiv preprint arXiv:2108.07258, 2021 | 112 | 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 | 95 | 2023 |
| On the opportunities and risks of foundation models (arXiv: 2108.07258). arXiv R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ... | 78 | 2022 |
| & Liang, P.(2021). 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, 0 | 71 | |
| 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 | 62 | 2022 |
| Compositional transformers for scene generation D Arad Hudson, L Zitnick Advances in neural information processing systems 34, 9506-9520, 2021 | 50 | 2021 |
| SLM: Learning a discourse language representation with sentence unshuffling H Lee, DA Hudson, K Lee, CD Manning arXiv preprint arXiv:2010.16249, 2020 | 45 | 2020 |
| others (2021). 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 24, 0 | 29 | |
| Soda: Bottleneck diffusion models for representation learning DA Hudson, D Zoran, M Malinowski, AK Lampinen, A Jaegle, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | 19 | 2024 |
| Scaling instructable agents across many simulated worlds MA Raad, A Ahuja, C Barros, F Besse, A Bolt, A Bolton, B Brownfield, ... arXiv preprint arXiv:2404.10179, 2024 | 8 | 2024 |
| Scaling instructable agents across many simulated worlds M Abi Raad, A Ahuja, C Barros, F Besse, A Bolt, A Bolton, B Brownfield, ... arXiv e-prints, arXiv: 2404.10179, 2024 | 5 | 2024 |
| Neural Assets: 3D-Aware Multi-Object Scene Synthesis with Image Diffusion Models Z Wu, Y Rubanova, R Kabra, DA Hudson, I Gilitschenski, Y Aytar, ... arXiv preprint arXiv:2406.09292, 2024 | 2 | 2024 |
| Moving Off-the-Grid: Scene-Grounded Video Representations S van Steenkiste, D Zoran, Y Yang, Y Rubanova, R Kabra, C Doersch, ... arXiv preprint arXiv:2411.05927, 2024 | | 2024 |
