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, and physics-informed machine learning open new possibilities for establishing grid foundation models. However, it is still unclear which model structures is most capable and suitable, how well they
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respond to different situations. Imitation learning will be explored to train a model to select shots that reflect human players’ choices in similar situations. The project will then investigate how
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computational link between a predicted three-dimensional ensemble and the fluorescence distributions expected experimentally. This will allow measurements to constrain and improve a selected AI model
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) into a rich, shared latent representation. Conditional Molecular Generation. Design and train a conditional generative model that maps these fused spectral representations directly to 1D SMILES strings
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devices, which is completely out of the control of the model owners. They can be easily stolen or abused if not properly protected. A recent empirical study [1] has demonstrated that half of on-device ML
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known oncological pathophysiology. LIME (Local Interpretable Model-agnostic Explanations): Generates patient-level attributions, showing clinicians the exact weight and direction of individual lab
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devices, which is completely out of the control of the model owners. They can be easily stolen or abused if not properly protected. A recent empirical study [1] has demonstrated that half of on-device ML
-
) into a rich, shared latent representation. Conditional Molecular Generation. Design and train a conditional generative model that maps these fused spectral representations directly to 1D SMILES strings
-
known oncological pathophysiology. LIME (Local Interpretable Model-agnostic Explanations): Generates patient-level attributions, showing clinicians the exact weight and direction of individual lab
-
reliable evaluation of individual cell health. The master thesis focuses on the development of a machine learning model for the automatic sorting of used lithium-ion cells based on custom