23 citations · 60 across the 36 of their papers we have counts for
4 papers · 1 filter
Neural Langevin Dynamics: towards interpretable Neural Stochastic Differential Equations
Simon M. Koop, Mark A. Peletier, Jacobus W. Portegies +1
Neural Stochastic Differential Equations (NSDE) have been trained as both Variational Autoencoders, and as GANs. However, the resulting Stochastic Differential Equations can be har…
You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets
Tianjin Huang, Tianlong Chen, Meng Fang +8
Recent works have impressively demonstrated that there exists a subnetwork in randomly initialized convolutional neural networks (CNNs) that can match the performance of the fully…
Small time asymptotics of the entropy of the heat kernel on a Riemannian manifold
Vlado Menkovski, Jacobus W. Portegies, Mahefa Ratsisetraina Ravelonanosy
We give an asymptotic expansion of the relative entropy between the heat kernel of a compact Riemannian manifold and the normalized Riemannian volume for small val…
Lottery Pools: Winning More by Interpolating Tickets without Increasing Training or Inference Cost
Lu Yin, Shiwei Liu, Meng Fang +3
Lottery tickets (LTs) is able to discover accurate and sparse subnetworks that could be trained in isolation to match the performance of dense networks. Ensemble, in parallel, is o…