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Learning with Algebraic Invariances, and the Invariant Kernel Trick
When solving data analysis problems it is important to integrate prior knowledge and/or structural invariances.
Learning Invariant Representations of Molecules for Atomization Energy Prediction
The accurate prediction of molecular energetics in chemical compound space is a crucial ingredient for rational compound design.
Estimating vector fields using sparse basis field expansions
We introduce a novel framework for estimating vector fields using sparse basis field expansions (S-FLEX).
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