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Stephan Eismann

3 accepted papers

2021

ATOM3D: Tasks on Molecules in Three Dimensions

NeurIPS 2021poster

Computational methods that operate on three-dimensional (3D) molecular structure have the potential to solve important problems in biology and chemistry. Deep neural networks have gained significant attention, but their widespread adoption in the biomolecular domain has been limited by a lack of eit…

Cited by 147SourcecodeScholar
2021

Learning from Protein Structure with Geometric Vector Perceptrons

ICLR 2021spotlight

Learning on 3D structures of large biomolecules is emerging as a distinct area in machine learning, but there has yet to emerge a unifying network architecture that simultaneously leverages the geometric and relational aspects of the problem domain. To address this gap, we introduce geometric vector…

2019

Learning Neural PDE Solvers with Convergence Guarantees

ICLR 2019poster

Partial differential equations (PDEs) are widely used across the physical and computational sciences. Decades of research and engineering went into designing fast iterative solution methods. Existing solvers are general purpose, but may be sub-optimal for specific classes of problems. In contrast to…

Cited by 157SourcePDFScholar