12 citations · 39 across the 25 of their papers we have counts for
17 papers · 1 filter
PerturbPFN: Probing the Limits of Synthetic Priors in Drug Perturbation Modelling
Yuche Gao, José Miguel Hernández-Lobato, Siyuan Guo
Predicting cellular responses to unseen chemical perturbations is challenging due to unknown targets and mechanisms, high-dimensional expression responses, and limited experimental…
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…
Towards Diverse Scientific Hypothesis Search with Large Language Models
Haorui Wang, Parshin Shojaee, Kazem Meidani +7
Large language models (LLMs) are on the rise for accelerating scientific discovery, most recently in advanced tasks such as generating valid scientific hypotheses. Yet in many disc…
On conditional diffusion models for PDE simulations
Aliaksandra Shysheya, Cristiana Diaconu, Federico Bergamin +4
Modelling partial differential equations (PDEs) is of crucial importance in science and engineering, and it includes tasks ranging from forecasting to inverse problems, such as dat…
Training Neural Samplers with Reverse Diffusive KL Divergence
Jiajun He, Wenlin Chen, Mingtian Zhang +2
Training generative models to sample from unnormalized density functions is an important and challenging task in machine learning. Traditional training methods often rely on the re…
Batched Bayesian optimization by maximizing the probability of including the optimum
Jenna Fromer, Runzhong Wang, Mrunali Manjrekar +3
Batched Bayesian optimization (BO) can accelerate molecular design by efficiently identifying top-performing compounds from a large chemical library. Existing acquisition strategie…