6 citations · 10 across the 20 of their papers we have counts for
9 papers · 1 filter
Assessing generative modeling approaches for free energy estimates in condensed matter
Maximilian Schebek, Jiajun He, Emil Hoffmann +3
The accurate estimation of free energy differences between two states is a long-standing challenge in molecular simulations. Traditional approaches generally rely on sampling multi…
Training-Free Vector Quantization via Gaussian VAEs
Tongda Xu, Wendi Zheng, Jiajun He +4
Vector-quantized variational autoencoders (VQ-VAEs) are discrete autoencoders that compress images into discrete tokens. However, they are difficult to train due to discretization.…
CREPE: Controlling Diffusion with Replica Exchange
Jiajun He, Paul Jeha, Peter Potaptchik +5
Inference-time control of diffusion models aims to steer model outputs to satisfy new constraints without retraining. Previous approaches have mostly relied on heuristic guidance o…
RNE: plug-and-play diffusion inference-time control and energy-based training
Jiajun He, José Miguel Hernández-Lobato, Yuanqi Du +1
Diffusion models generate data by removing noise gradually, which corresponds to the time-reversal of a noising process. However, access to only the denoising kernels is often insu…
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…
FEAT: Free energy Estimators with Adaptive Transport
Jiajun He, Yuanqi Du, Francisco Vargas +4
We present Free energy Estimators with Adaptive Transport (FEAT), a novel framework for free energy estimation -- a critical challenge across scientific domains. FEAT leverages lea…
Accelerated Parallel Tempering via Neural Transports
Leo Zhang, Peter Potaptchik, Jiajun He +5
Markov Chain Monte Carlo (MCMC) algorithms are essential tools in computational statistics for sampling from unnormalised probability distributions, but can be fragile when targeti…
DiffRatio: Training One-Step Diffusion Models Without Teacher Supervision
Wenlin Chen, Mingtian Zhang, Jiajun He +4
Score-based distillation methods (e.g., variational score distillation) train one-step diffusion models by first pre-training a teacher score model and then distilling it into a on…
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…