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Björn Lütjens
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Year
Safe reinforcement learning with model uncertainty estimates
B Lütjens, M Everett, JP How
2019 International Conference on Robotics and Automation (ICRA), 8662-8668, 2019
1742019
Certified adversarial robustness for deep reinforcement learning
B Lütjens, M Everett, JP How
Conference on Robot Learning (CoRL), 1328-1337, 2020
872020
Certifiable robustness to adversarial state uncertainty in deep reinforcement learning
M Everett, B Lütjens, JP How
IEEE Transactions on Neural Networks and Learning Systems 33 (9), 4184-4198, 2021
53*2021
Digital Twin Earth--Coasts: Developing a fast and physics-informed surrogate model for coastal floods via neural operators
P Jiang, N Meinert, H Jordăo, C Weisser, S Holgate, A Lavin, B Lütjens, ...
2021 NeurIPS Workshop on Machine Learning for the Physical Sciences (ML4PS), 2021
292021
The World as a Graph: Improving El Nińo Forecasts with Graph Neural Networks
SR Cachay, E Erickson, AFC Bucker, E Pokropek, W Potosnak, S Bire, ...
arXiv preprint arXiv:2104.05089, 2021
29*2021
Physically-consistent generative adversarial networks for coastal flood visualization
B Lütjens, B Leshchinskiy, C Requena-Mesa, F Chishtie, ...
arXiv preprint arXiv:2104.04785, 2021
27*2021
Toward Foundation Models for Earth Monitoring: Proposal for a Climate Change Benchmark
A Lacoste, ED Sherwin, H Kerner, H Alemohammad, B Lütjens, J Irvin, ...
2021 NeurIPS Workshop on Tackling Climate Change with Machine Learning (CCML), 2021
212021
Spectral PINNs: Fast uncertainty propagation with physics-informed neural networks
B Lütjens, CH Crawford, M Veillette, D Newman
2021 NeurIPS Workshop on the Symbiosis of Deep Learning and Differential, 2021
17*2021
Machine learning-based estimation of forest carbon stocks to increase transparency of forest preservation efforts
B Lütjens, L Liebenwein, K Kramer
2019 NeurIPS Workshop on Tackling Climate Change with AI (CCAI), 2019
162019
ReforesTree: A Dataset for Estimating Tropical Forest Carbon Stock with Deep Learning and Aerial Imagery
G Reiersen, D Dao, B Lütjens, K Klemmer, K Amara, A Steinegger, ...
AAAI-22, 2022
152022
GEO-Bench: Toward Foundation Models for Earth Monitoring
A Lacoste, N Lehmann, P Rodriguez, ED Sherwin, H Kerner, B Lütjens, ...
NeurIPS Datasets and Benchmarks, 2023
142023
WiSoSuper: Benchmarking Super-Resolution Methods on Wind and Solar Data
R Kurinchi-Vendhan, B Lütjens, R Gupta, L Werner, D Newman
2021 NeurIPS Workshop on Tackling Climate Change with Machine Learning (CCML), 2021
122021
Multiscale neural operator: Learning fast and grid-independent pde solvers
B Lütjens, CH Crawford, CD Watson, C Hill, D Newman
arXiv preprint arXiv:2207.11417, 2022
102022
ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation
S Yu, W Hannah, L Peng, J Lin, MA Bhouri, R Gupta, B Lütjens, JC Will, ...
Advances in Neural Information Processing Systems 36, 2024
6*2024
How. 2019. Safe reinforcement learning with model uncertainty estimates
B Lütjens, M Everett, P Jonathan
2019 International Conference on Robotics and Automation (ICRA). IEEE, 8662-8668, 0
5
Tackling the Overestimation of Forest Carbon with Deep Learning and Aerial Imagery
G Reiersen, D Dao, B Lütjens, K Klemmer, X Zhu, C Zhang
2021 ICML Workshop on Tackling Climate Change with Machine Learning (CCML), 2021
42021
TrueBranch: Metric Learning-based Verification of Forest Conservation Projects
S Santamaria*, D Dao*, B Lütjens*, C Zhang
Best Proposal Paper at 2020 ICLR CCML, 2020
42020
Ocean emulation with fourier neural operators: Double gyre
S Bire, B Lütjens, K Azizzadenesheli, A Anandkumar, CN Hill
Authorea Preprints, 2023
22023
Oceanfourcast: Emulating ocean models with transformers for adjoint-based data assimilation
S Bire, B Lütjens, D Newman, C Hill
EGU General Assembly Conference Abstracts, EGU-10810, 2023
22023
Teaching computer vision for ecology
E Cole, S Stathatos, B Lütjens, T Sharma, J Kay, J Parham, ...
arXiv preprint arXiv:2301.02211, 2023
22023
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