| A library for learning neural operators J Kossaifi, N Kovachki, Z Li, D Pitt, M Liu-Schiaffini, V Duruisseaux, ... arXiv preprint arXiv:2412.10354, 2024 | 22 | 2024 |
| Calibrated uncertainty quantification for operator learning via conformal prediction Z Ma, K Azizzadenesheli, A Anandkumar arXiv preprint arXiv:2402.01960, 2024 | 17 | 2024 |
| Physics-informed neural operators with exact differentiation on arbitrary geometries C White, J Berner, J Kossaifi, M Elleithy, D Pitt, D Leibovici, Z Li, ... The symbiosis of deep learning and differential equations III, 2023 | 13 | 2023 |
| Enabling automatic differentiation with mollified graph neural operators RY Lin, J Berner, V Duruisseaux, D Pitt, D Leibovici, J Kossaifi, ... arXiv preprint arXiv:2504.08277, 2025 | 5 | 2025 |
| Tensor-galore: Memory-efficient training via gradient tensor decomposition RJ George, D Pitt, J Zhao, J Kossaifi, C Luo, Y Tian, A Anandkumar | 3 | 2025 |
| TensorGRaD: Tensor Gradient Robust Decomposition for Memory-Efficient Neural Operator Training S Loeschcke, D Pitt, RJ George, J Zhao, C Luo, Y Tian, J Kossaifi, ... arXiv preprint arXiv:2501.02379, 2025 | 2 | 2025 |
| A library for learning neural operators, 2025 J Kossaifi, N Kovachki, Z Li, D Pitt, M Liu-Schiaffini, RJ George, B Bonev, ... URL https://arxiv. org/abs/2412.10354, 0 | 2 | |
Anima AnandkumarCalifornia Institute of Technology and NVIDIAVerified email at caltech.edu
