1 citations · 1 across the 7 of their papers we have counts for
7 papers
PerturbCellRL: Aligning Distributions and Grounding Biology via Post-Training Perturbation Generators
Dongxia Wu, Mingyu Li, Yuhui Zhang +4
Single-cell perturbation models can reduce costly wet-lab screening by predicting how cells respond transcriptionally to interventions. Recent advances in flow-matching have enable…
V-GRPO: Online Reinforcement Learning for Denoising Generative Models Is Easier than You Think
Bingda Tang, Yuhui Zhang, Xiaohan Wang +3
Aligning denoising generative models with human preferences or verifiable rewards remains a key challenge. While policy-gradient online reinforcement learning (RL) offers a princip…
Fine-tuning MLLMs Without Forgetting Is Easier Than You Think
He Li, Yuhui Zhang, Xiaohan Wang +2
The paper demonstrate that simple adjustments of the fine-tuning recipes of multimodal large language models (MLLM) are sufficient to mitigate catastrophic forgetting. On visual qu…
CellFluxRL: Biologically-Constrained Virtual Cell Modeling via Reinforcement Learning
Dongxia Wu, Shiye Su, Yuhui Zhang +4
Building virtual cells with generative models to simulate cellular behavior in silico is emerging as a promising paradigm for accelerating drug discovery. However, prior image-base…
Uncertainty-Aware Distribution-to-Distribution Flow Matching for Scientific Imaging
Dongxia Wu, Yuhui Zhang, Serena Yeung-Levy +2
Distribution-to-distribution generative models support scientific imaging tasks ranging from modeling cellular perturbation responses to translating medical images across condition…
TULiP: Test-time Uncertainty Estimation via Linearization and Weight Perturbation
Yuhui Zhang, Dongshen Wu, Yuichiro Wada +1
A reliable uncertainty estimation method is the foundation of many modern out-of-distribution (OOD) detectors, which are critical for safe deployments of deep learning models in th…
Robust VAEs via Generating Process of Noise Augmented Data
Hiroo Irobe, Wataru Aoki, Kimihiro Yamazaki +5
Advancing defensive mechanisms against adversarial attacks in generative models is a critical research topic in machine learning. Our study focuses on a specific type of generative…