| Iq-learn: Inverse soft-q learning for imitation D Garg, S Chakraborty, C Cundy, J Song, S Ermon Advances in Neural Information Processing Systems 34, 4028-4039, 2021 | 143 | 2021 |
| On the opportunities and challenges of foundation models for geospatial artificial intelligence G Mai, W Huang, J Sun, S Song, D Mishra, N Liu, S Gao, T Liu, G Cong, ... arXiv preprint arXiv:2304.06798, 2023 | 126 | 2023 |
| Neural networks and the chomsky hierarchy G Delétang, A Ruoss, J Grau-Moya, T Genewein, LK Wenliang, E Catt, ... arXiv preprint arXiv:2207.02098, 2022 | 108 | 2022 |
| Parallelizing Linear Recurrent Neural Nets Over Sequence Length E Martin, C Cundy International Conference on Learning Representations (ICLR) 2018, 2018 | 86 | 2018 |
| BCD nets: Scalable variational approaches for bayesian causal discovery C Cundy, A Grover, S Ermon Advances in Neural Information Processing Systems 34, 7095-7110, 2021 | 66 | 2021 |
| Towards a foundation model for geospatial artificial intelligence (vision paper) G Mai, C Cundy, K Choi, Y Hu, N Lao, S Ermon Proceedings of the 30th International Conference on Advances in Geographic …, 2022 | 63 | 2022 |
| Geo-knowledge-guided GPT models improve the extraction of location descriptions from disaster-related social media messages Y Hu, G Mai, C Cundy, K Choi, N Lao, W Liu, G Lakhanpal, RZ Zhou, ... International Journal of Geographical Information Science 37 (11), 2289-2318, 2023 | 49 | 2023 |
| LMPriors: Pre-Trained Language Models as Task-Specific Priors K Choi, C Cundy, S Srivastava, S Ermon First Workshop on Foundation Models for Decision Making, Neurips 2022, 2022 | 28 | 2022 |
| Simulation of plants in buildings; incorporating plant-Air interactions in building energy simulation R Ward, R Choudhary, C Cundy, G Johnson, A McRobie Proc. 14th Intern Conf IBPSA-Building Simulation, 2256-63, 2015 | 24 | 2015 |
| Predicting human deliberative judgments with machine learning O Evans, A Stuhlmüller, C Cundy, R Carey, Z Kenton, T McGrath, ... Future of Humanity Institute, 2018 | 15 | 2018 |
| On the opportunities and challenges of foundation models for geoai (vision paper) G Mai, W Huang, J Sun, S Song, D Mishra, N Liu, S Gao, T Liu, G Cong, ... ACM Transactions on Spatial Algorithms and Systems, 2024 | 7 | 2024 |
| Sequencematch: Imitation learning for autoregressive sequence modelling with backtracking C Cundy, S Ermon arXiv preprint arXiv:2306.05426, 2023 | 7 | 2023 |
| Neural networks and the chomsky hierarchy, 2022 G Delétang, A Ruoss, J Grau-Moya, T Genewein, LK Wenliang, E Catt, ... URL https://arxiv. org/abs/2207.02098, 0 | 5 | |
| Flexible approximate inference via stratified normalizing flows C Cundy, S Ermon Conference on Uncertainty in Artificial Intelligence, 1288-1297, 2020 | 4 | 2020 |
| Exploring Hierarchy-Aware Inverse Reinforcement Learning C Cundy, D Filan First Workshop on Goal Specifications for Reinforcement Learning, ICML 2018 …, 2018 | 4 | 2018 |
| Privacy-constrained policies via mutual information regularized policy gradients CJ Cundy, R Desai, S Ermon International Conference on Artificial Intelligence and Statistics, 2809-2817, 2024 | 3 | 2024 |
| Beyond Bayes-optimality: meta-learning what you know you don't know J Grau-Moya, G Delétang, M Kunesch, T Genewein, E Catt, K Li, A Ruoss, ... arXiv preprint arXiv:2209.15618, 2022 | 3 | 2022 |
| Machine learning models for unresolved capillary effects in multiphase flows S Mirjalili, C Cundy, C Laurent, S Ermon, G Iaccarino, A Mani Bulletin of the American Physical Society, 2024 | | 2024 |
| A physics-informed machine learning model for the prediction of drop breakup in two-phase flows C Cundy, S Mirjalili, C Laurent, S Ermon, G Iaccarino, A Mani International Journal of Multiphase Flow 180, 104934, 2024 | | 2024 |
| A Physics-Informed Machine Learning Approach for Predicting Atomized Drop Distributions in Liquid Jet Simulations C Cundy, S Mirjalili, C Laurent, S Ermon, A Mani Bulletin of the American Physical Society, 2023 | | 2023 |
