2 citations · 2 across the 8 of their papers we have counts for
6 papers · 1 filter
Few-Step Boltzmann Generators via Scalable Likelihood Flow Maps
RuiKang OuYang, Hanlin Yu, Xinyue Ai +7
Recent progress in flow-based generative modeling has led to models that output high-quality samples while using only a small number of function evaluations. However, at present, t…
Learning Energy-Based Models from Stochastic Interpolants using Spatiotemporal Differences
Hanlin Yu, RuiKang OuYang, Partha Kaushik +3
Learning an energy-based model from data samples is a central problem in machine learning. Many recent and popular methods, such as denoising score matching for training energy-bas…
Progressive Tempering Sampler with Diffusion
Severi Rissanen, RuiKang OuYang, Jiajun He +4
Recent research has focused on designing neural samplers that amortize the process of sampling from unnormalized densities. However, despite significant advancements, they still fa…
No Trick, No Treat: Pursuits and Challenges Towards Simulation-free Training of Neural Samplers
Jiajun He, Yuanqi Du, Francisco Vargas +5
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. Recent breakthroughs in generativ…
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…
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…