no code implementations • 10 Feb 2025 • Jiajun He, Yuanqi Du, Francisco Vargas, Dinghuai Zhang, Shreyas Padhy, Ruikang Ouyang, Carla Gomes, José Miguel Hernández-Lobato
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.
no code implementations • 15 Sep 2024 • Ruikang Ouyang, Bo Qiang, Zixing Song, 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 research, e. g. molecular dynamics.
1 code implementation • 2 Feb 2024 • Ruikang Ouyang, Andrew Elliott, Stratis Limnios, Mihai Cucuringu, Gesine Reinert
For analysing real-world networks, graph representation learning is a popular tool.