Search Results for author:
Found 3 papers, 2 papers with code
No Trick, No Treat: Pursuits and Challenges Towards Simulation-free Training of Neural Samplers
We consider the sampling problem, where the aim is to draw samples from a distribution whose density is known only up to a normalization constant.
BNEM: A Boltzmann Sampler Based on Bootstrapped Noised Energy Matching
Developing an efficient sampler capable of generating independent and identically distributed (IID) samples from a Boltzmann distribution is a crucial challenge in scientific research, e. g. molecular dynamics.
L2G2G: a Scalable Local-to-Global Network Embedding with Graph Autoencoders
For analysing real-world networks, graph representation learning is a popular tool.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
