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Assessment of Antarctic moss health from multi-sensor UAS imagery with Random Forest Modelling.

, , , , , and . Int. J. Appl. Earth Obs. Geoinformation, (2018)

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NNP/MM: Fast molecular dynamics simulations with machine learning potentials and molecular mechanics., , , , , , , and . CoRR, (2022)DeepSite: protein-binding site predictor using 3D-convolutional neural networks., , , , and . Bioinform., 33 (19): 3036-3042 (2017)PlayMolecule Glimpse: Understanding Protein-Ligand Property Predictions with Interpretable Neural Networks., , , , , , and . J. Chem. Inf. Model., 62 (2): 225-231 (2022)PlayMolecule Viewer: A Toolkit for the Visualization of Molecules and Other Data., , , and . J. Chem. Inf. Model., 64 (3): 584-589 (2024)A Scalable Molecular Force Field Parameterization Method Based on Density Functional Theory and Quantum-Level Machine Learning., , , , and . J. Chem. Inf. Model., 59 (8): 3485-3493 (2019)Kinetic Characterization of Fragment Binding in AmpC β-Lactamase by High-Throughput Molecular Simulations., , , , , and . J. Chem. Inf. Model., 54 (2): 362-366 (2014)AdaptiveBandit: A multi-armed bandit framework for adaptive sampling in molecular simulations, , , and . (2020)cite arxiv:2002.12582.Dimensionality reduction methods for molecular simulations., , , and . CoRR, (2017)Machine Learning Coarse-Grained Potentials of Protein Thermodynamics., , , , , , , , , and . CoRR, (2022)TorchMD: A deep learning framework for molecular simulations., , , , , , , and . CoRR, (2020)