Search Results for author:
Found 3 papers, 1 papers with code
3DMolNet: A Generative Network for Molecular Structures
With the recent advances in machine learning for quantum chemistry, it is now possible to predict the chemical properties of compounds and to generate novel molecules.
Learning Conditional Invariance through Cycle Consistency
We address these shortcomings with a novel approach to cycle consistency.
Learning Invariances with Generalised Input-Convex Neural Networks
These networks represent functions that are guaranteed to have connected level sets forming smooth manifolds on the input space.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
