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20242026
most citedNo Trick, No Treat: Pursuits and Challenges Towards Simulation-free Training of Neural Samplers

2 citations · 2 across the 8 of their papers we have counts for

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cs.LG2024

BNEM: A Boltzmann Sampler Based on Bootstrapped Noised Energy Matching

RuiKang OuYang, Bo Qiang, José Miguel Hernández-Lobato

Developing an efficient sampler capable of generating independent and identically distributed (IID) samples from a Boltzmann distribution is a crucial challenge in scientific resea…

cs.LG2024

L2G2G: a Scalable Local-to-Global Network Embedding with Graph Autoencoders

Ruikang Ouyang, Andrew Elliott, Stratis Limnios +2

For analysing real-world networks, graph representation learning is a popular tool. These methods, such as a graph autoencoder (GAE), typically rely on low-dimensional representati…