Coulomb potential learning

By Michael P. Perrone, Leon N. Cooper. 1998

Subfields: Computational Chemistry & Drug Discovery, AI for Physical Sciences

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Chemistry & Drug Discovery

1. Sepp Hochreiter
2. Günter Klambauer
3. Thomas Unterthiner

In AI for Physical Sciences

1. Sepp Hochreiter

Citations in the corpus

• Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields (2017, background): “Coulomb Potential Learning was proposed to store arbitrary many patterns in a potential field with perfect recall and without spurious patterns [35].”