15 papers · ranked by Valyu relevance
Aghiles Kebaili, Jérôme Lapuyade-Lahorgue, Su Ruan, Cecilia Di Ruberto + 4 more
Deep learning has become a popular tool for medical image analysis, but the limited availability of training data remains a major challenge, particularly in the medical field where data acquisition can be costly and subject to privacy regulations. Data augmentation techniques offer a solution by artificially increasing…
Zineb Sordo, Eric Chagnon, Zixi Hu, Jeffrey J. Donatelli + 6 more
'Peter Andeer' 'Peter S. Nico' 'Trent Northen' 'Daniela Ushizima' 'Raimondo Schettini' 'Guanghui (Richard) Wang'] Generative AI (genAI) has emerged as a powerful tool for synthesizing diverse and complex image data, offering new possibilities for scientific imaging applications. This review presents a comprehensive…
Amira Alakhdar, Barnabas Poczos, Newell Washburn
in De Novo Drug Design Authors: Amira Alakhdar, Barnabas Poczos, Newell Washburn Diffusion models have emerged as powerful tools for molecular generation, particularly in the context of 3D molecular structures. Inspired by nonequilibrium statistical physics, these models can generate 3D molecular structures with…
Minshuo Chen, Song Mei, Jianqing Fan, Mengdi Wang
Diffusion models, a powerful and universal generative artificial intelligence technology, have achieved tremendous success and opened up new possibilities in diverse applications. In these applications, diffusion models provide flexible high-dimensional data modeling, and act as a sampler for generating new samples…
Qiuhua Yi, Xiangfan Chen, Chenwei Zhang, Zehai Zhou + 3 more
'Xiangjie Kong' 'Arkaitz Zubiaga'] Diffusion models are a kind of math-based model that were first applied to image generation. Recently, they have drawn wide interest in natural language generation (NLG), a sub-field of natural language processing (NLP), due to their capability to generate varied and high-quality text…
Hao Zhang, Yang Liu, Xiaoyan Liu, Cheng Wang + 1 more
Background Molecular biology is crucial for drug discovery, protein design, and human health. Due to the vastness of the drug-like chemical space, depending on biomedical experts to manually design molecules is exceedingly expensive. Utilizing generative methods with deep learning technology offers an effective…
Furkan Luleci, F. Necati Catbas
The use of deep generative models (DGMs) such as variational autoencoders, autoregressive models, flow-based models, energy-based models, generative adversarial networks, and diffusion models has been advantageous in various disciplines due to their high data generative skills. Using DGMs has become one of the most…
Luca Ambrogioni, Francesco Ginelli
Generative diffusion models have achieved spectacular performance in many areas of machine learning and generative modeling. While the fundamental ideas behind these models come from non-equilibrium physics, variational inference, and stochastic calculus, in this paper we show that many aspects of these models can be…
Alice Lacan, Romain André, Michèle Sebag, Blaise Hanczar
Background RNA-seq data is used for precision medicine (e.g., cancer predictions), which benefits from deep learning approaches to analyze complex gene expression data. However, transcriptomics datasets often have few samples compared to deep learning standards. Synthetic data generation is thus being explored to…
Yan Liu, Tao Jiang, Rui Li, Lingling Yuan + 3 more
'Chen Li' 'Xiaoyan Li'] Diffusion models, a class of deep learning models based on probabilistic generative processes, progressively transform data into noise and then reconstruct the original data through an inverse process. Recently, diffusion models have gained attention in microscopic image analysis for their…
Yihuan Tian, Tao Yu, Zuling Cheng, Sunjung Lee + 1 more
To promote the inheritance of traditional culture, a variety of emerging methods rooted in machine learning and deep learning have been introduced. Dunhuang patterns, an important part of traditional Chinese culture, are difficult to collect in large numbers due to their limited availability. However, existing…
Zilai Li, Rongkai Zhang, Guanghui Wang
Diffusion models are among the most common techniques used for image generation, having achieved state-of-the-art performance by implementing auto-regressive algorithms. However, multi-step inference processes are typically slow and require extensive computational resources. To address this issue, we propose the use of…
Ziwei Luo, Fredrik Gustafsson, Zheng Zhao, Jens Sjölund + 1 more
'Thomas Schön'] Diffusion models (DMs) have achieved remarkable progress in generative modelling, particularly in enhancing image quality to conform to human preferences. Recently, these models have also been applied to low-level computer vision for photo-realistic image restoration (IR) in tasks such as image…
Jakob Benjamin Wessel, Callum J. R. Murphy-Barltrop, Emma S. Simpson
With the recent development of new geometric and angular-radial frameworks for multivariate extremes, reliably simulating from angular variables in moderate-to-high dimensions is of increasing importance. Empirical approaches have the benefit of simplicity, and work reasonably well in low dimensions, but as the number…
EVAN SCOPE CRAFTS, UMBERTO VILLA
In recent years, the ascendance of diffusion modeling as a state-of-the-art generative modeling approach has spurred significant interest in their use as priors in Bayesian inverse problems. However, it is unclear how to optimally integrate a diffusion model trained on the prior distribution with a given likelihood…