Deep Learning
Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium State
Spiking neural networks (SNNs) are brain-inspired models that enable energy-efficient implementation on neuromorphic hardware. However, the supervised training of SNNs remains a hard problem due to the discontinuity of the spiking neuron model.
Fictitious Play for Mean Field Games: Continuous Time Analysis and Applications
In such framework, all players are identical, anonymous ( i.e., they are not identifiable) and have symmetric interests.
Supplementary information for SE(3)-equivariant prediction of molecular wavefunctions and electronic densities A Detailed Background
A.1 Quantum chemistry At the center of quantum chemistry methods lies the electronic Schrödinger equation ˆ H
SE(3)-equivariant prediction of molecular wavefunctions and electronic densities
This accuracy makes it possible to derive properties such as energies and forces directly from the wavefunction in an end-to-end manner.
Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions
Integrating functions on discrete domains into neural networks is key to developing their capability to reason about discrete objects.