Due to patient privacy protection concerns, machine learning research in healthcare has been undeniably slower and limited than in other application domains. High-quality, realistic, synthetic electronic health records (EHRs) can be leveraged to accelerate methodological developments for research purposes while mitigating privacy concerns associated with data sharing. The current state-of-the-art model for synthetic EHR generation is generative adversarial networks, which are notoriously difficult to train and can suffer from mode collapse. Denoising Diffusion Probabilistic Models, a class of generative models inspired by statistical thermodynamics, have recently been shown to generate high-quality synthetic samples in certain domains. It is unknown whether these can generalize to generation of large-scale, high-dimensional EHRs. In this paper, we present a novel generative model based on diffusion models that is the first successful application on electronic health records. Our model proposes a mechanism to perform class-conditional sampling to preserve label information. We also introduce a new sampling strategy to accelerate the inference speed. We empirically show that our model outperforms existing state-of-the-art synthetic EHR generation methods.
Equivariant Asynchronous Diffusion: An Adaptive Denoising Schedule for Accelerated Molecular Conformation Generation
Shanghai Academy of Artificial Intelligence for Science, SAIS（上海人工智能科学研究院，SAIS）；College of Chemistry and Chemical Engineering, Xiamen University（厦门大学化学与化工学院）；Artificial Intelligence Innovation and Incubation (AI 3 ) Institute, Fudan University（复旦大学人工智能创新与孵化（AI 3）研究院）
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）
Score-based diffusion models provide a powerful way to model images using the gradient of the data distribution. Leveraging the learned score function as a prior, here we introduce a way to sample data from a conditional distribution given the measurements, such that the model can be readily used for solving inverse problems in imaging, especially for accelerated MRI. In short, we train a continuous time-dependent score function with denoising score matching. Then, at the inference stage, we iterate between numerical SDE solver and data consistency projection step to achieve reconstruction. Ou…
Denoising Diffusion Probabilistic Models (DDPMs) can generate high-quality samples such as image and audio samples. However, DDPMs require hundreds to thousands of iterations to produce final samples. Several prior works have successfully accelerated DDPMs through adjusting the variance schedule (e.g., Improved Denoising Diffusion Probabilistic Models) or the denoising equation (e.g., Denoising Diffusion Implicit Models (DDIMs)). However, these acceleration methods cannot maintain the quality of samples and even introduce new noise at a high speedup rate, which limit their practicability. To a…
Conditional Denoising Diffusion Model-Based Robust MR Image Reconstruction from Highly Undersampled Data
Department of Mathematics and Systems Engineering（数学与系统工程系）；Department of Biomedical Engineering & Science（生物医学工程与科学系）；Florida Institute of Technology（佛罗里达理工学院）；A. A. Martinos Center for Biomedical Imaging（A.A. Martinos生物医学成像中心）；Massachusetts General Hospital/Harvard Medical School（麻省总医院/哈佛医学院）；Department of Radiology（放射科）；Embry-Riddle Aeronautical University（艾姆伯-里尔航空大学）
Allen Discovery Center at Tufts University（塔夫茨大学阿伦发现中心）；Institute for Theoretical Physics, TU Wien（理论物理研究所，维也纳技术大学）；Wyss Institute for Biologically Inspired Engineering at Harvard University（哈佛大学生物启发工程研究所）
DiffusionLight-Turbo: Accelerated Light Probes for Free via Single-Pass Chrome Ball Inpainting
Tokyo Institute of Technology（东京技术大学）；Siam Commercial Bank（暹罗商业银行）；School of Information Science and Technology, VISTEC（信息科学与技术学院，VISTEC）；Google DeepMind（谷歌DeepMind）
Low-dose computed tomography (LDCT) is an important topic in the field of radiology over the past decades. LDCT reduces ionizing radiation-induced patient health risks but it also results in a low signal-to-noise ratio (SNR) and a potential compromise in the diagnostic performance. In this paper, to improve the LDCT denoising performance, we introduce the conditional denoising diffusion probabilistic model (DDPM) and show encouraging results with a high computational efficiency. Specifically, given the high sampling cost of the original DDPM model, we adapt the fast ordinary differential equat…
Zigzag Diffusion Sampling: Diffusion Models Can Self-Improve via Self-Reflection
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Baidu Inc（百度公司）
Decoupled Data Consistency with Diffusion Purification for Image Restoration
Department of Electrical Engineering and Computer Science, University of Michigan（电气工程与计算机科学系，密歇根大学）；Departments of Computational Mathematics, Science and Engineering, and Biomedical Engineering, Michigan State University（计算数学、科学与工程系，以及生物医学工程系，密歇根州立大学）
CNFP: Optimizing Cloud-Native Network Function Placement with Diffusion Models on the Cloud Continuum
atlanTTic - I&C Lab - Universidade de Vigo（atlanTTic - I&C实验室 - 维戈大学）；Centro Tecnolóxico de Telecomunicacións de Galicia (GRADIANT)（加利西亚电信技术中心（GRADIANT））
Chandra Family Department of Electrical and Computer Engineering The University of Texas at Austin（电气与计算机工程系Chandra家族部门德克萨斯大学奥斯汀分校）；Department of Imaging Physics MD Anderson Cancer Center（成像物理系MD安德森癌症中心）；Department of Diagnostic Medicine The University of Texas at Austin Dell Medical School（诊断医学系德克萨斯大学奥斯汀分校Dell医学院）；Oden Institute for Computational Engineering and Sciences The University of Texas at Austin（计算工程与科学学院Oden研究所德克萨斯大学奥斯汀分校）
Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining
Department of Electrical \& Computer Engineering, University of Minnesota, MN, USA；Center for Magnetic Resonance Research, University of Minnesota, MN, USA
Streaming-dLLM: Accelerating Diffusion LLMs via Suffix Pruning and Dynamic Decoding
School of information（信息学院）；Electronics, Beijing Institute of Technology, Beijing（电子学院，北京理工大学，北京）；Department of Computer Science, City University of Hong Kong, Hong Kong（计算机科学系，香港城市大学，香港）；School of Computer Science, Wuhan University, Wuhan（计算机科学学院，武汉大学，武汉）；School of Economics（经济学院）；Management, Communication University of China, Beijing（管理学院，中国传媒大学，北京）；School of Science（科学学院）；Engineering, The Chinese University of Hong Kong (Shenzhen), Shenzhen（工程学院，香港中文大学（深圳），深圳）
