131 citations · 179 across the 26 of their papers we have counts for
4 papers · 1 filter
Structured Inverse-Free Natural Gradient: Memory-Efficient & Numerically-Stable KFAC
Wu Lin, Felix Dangel, Runa Eschenhagen +4
Second-order methods such as KFAC can be useful for neural net training. However, they are often memory-inefficient since their preconditioning Kronecker factors are dense, and num…
A Computational Framework for Solving Wasserstein Lagrangian Flows
Kirill Neklyudov, Rob Brekelmans, Alexander Tong +3
The dynamical formulation of the optimal transport can be extended through various choices of the underlying geometry (kinetic energy), and the regularization of density paths (pot…
Wasserstein Quantum Monte Carlo: A Novel Approach for Solving the Quantum Many-Body Schrödinger Equation
Kirill Neklyudov, Jannes Nys, Luca Thiede +4
Solving the quantum many-body Schrödinger equation is a fundamental and challenging problem in the fields of quantum physics, quantum chemistry, and material sciences. One of the c…
Quantum HyperNetworks: Training Binary Neural Networks in Quantum Superposition
Juan Carrasquilla, Mohamed Hibat-Allah, Estelle Inack +4
Binary neural networks, i.e., neural networks whose parameters and activations are constrained to only two possible values, offer a compelling avenue for the deployment of deep lea…