27 papers · ranked by Valyu relevance
Yazdani, Shamim, Singh, Akansha + 14 more
In recent years, deep learning based generative models, particularly Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Diffusion Models (DMs), have been instrumental in in generating diverse, high-quality content across various domains, such as image and video synthesis. This capability has…
Haowen Pang, Tiande Zhang, Yanan Wu, Shannan Chen + 9 more
Generative models play a pivotal role in the field of medical imaging. This paper provides an extensive and scholarly review of the application of generative models in medical image creation and translation. In the creation aspect, the goal is to generate new images based on potential conditional variables, while in…
Jian Jiang, Daixin Li, Guilin Wang, Nicole Hayes + 5 more
Generative artificial intelligence (AI) models, a class of AI techniques that learn data distributions to synthesize novel samples, have emerged as impactful tools across scientific disciplines. In recent years, these models have found extensive applications in fields such as natural language processing and biomedical…
Fasih Haider, Edward Moroshko, Yuyang Xue, Sotirios A. Tsaftaris
1## Introduction In recent years, the machine learning field has witnessed a significant increase in the popularity and advancement of generative models (; ; ; ; ; ). These models have significantly advanced approaches to e.g., image generation and natural language processing, demonstrating the ability to create…
Matthew D. Greaves, Leonardo Novelli, Michael Breakspear, Adeel Razi
The term generative model is widely used in human neuroimaging; however, its meaning is often left implicit. Prompted by observations and discussions at the 2025 Organization for Human Brain Mapping (OHBM) Annual Meeting, we surveyed members of the neuroimaging community to examine how generative models are defined…
Kevin Wang, Hongqian Niu, Yixin Wang, Didong Li
Generative networks have shown remarkable success in learning complex data distributions, particularly in generating high-dimensional data from lower-dimensional inputs. While this capability is well-documented empirically, its theoretical underpinning remains unclear. One common theoretical explanation appeals to the…
Yi-Chung Chen, David I. Inouye, Jing Gao
Generative classifiers, which leverage conditional generative models for classification, have recently demonstrated desirable properties such as robustness to distribution shifts. However, recent progress in this area has been largely driven by diffusion-based models, whose substantial computational cost severely…
Hakime Öztürk, Tejumade Afonja, Joonas Jälkö, Ruta Binkyte + 18 more
The synthesis of anonymized data derived from real-world cohorts offers a promising strategy for regulatory-compliant and privacy-preserving biological data sharing, potentially facilitating model development that can improve predictive performance. However, the extent to which generative models can preserve biological…
Authors not listed
Machine learning is increasingly used to predict reaction properties such as barrier heights, reaction energies, rates, or yields, as well as the underlying molecular geometries, including transition state structures. While such predictions have the potential to provide mechanistic insight for high-impact applications…
Tianhua Chen
This book provides a compact, derivation-oriented introduction to the mathematical foundations of modern generative artificial intelligence. Rather than surveying every recent architecture or implementation detail, it develops a coherent route through the ideas connecting major families of generative models, from PCA…
Chenxi Wang, Xiaorong Wang, Peiyang Li, Yi Wang
Generative models have demonstrated remarkable potential in time series analysis tasks, like synthesis, forecasting, imputation, etc. However, offering limited coverage for generative models, existing time series libraries are mainly engineered for discriminative models, with standardized workflows for specific tasks…
Yael Pinchevsky Itan, Yuval Itan, Fotios Drenos
Generative artificial intelligence (AI) is transforming biological and medical research and data analysis. Beyond analyzing existing information, these models can learn complex patterns and generate new data such as realistic protein sequences, genetic variants, or clinical notes. In molecular biology, language-like…
Chisato Kumada, Tomoyuki Hiroyasu, Satoru Hiwa
Structural connectivity (SC) data are crucial for brain network analysis, but SC-based machine learning often suffers from limited data availability, hindering model generalization and robustness. Although data augmentation using deep generative models has attracted increasing attention, it remains unclear how…
Jiaqi Zhu, Xincheng Chen, Yuncheng Wu, Zhaojing Luo + 1 more
Generative models trained on a source domain often produce samples that are poorly aligned with shifted target domains, limiting their effectiveness for target-domain data augmentation. Although target-specific adaptation can reduce this mismatch, it typically requires additional optimization and domain-specific…
Marcelli, Edoardo, Sean O'Hagan, Veronika Ročková
Generative Bayesian Filtering (GBF) provides a powerful and flexible framework for performing posterior inference in complex nonlinear and non-Gaussian state-space models. Our approach extends Generative Bayesian Computation (GBC) to dynamic settings, enabling recursive posterior inference using simulation-based…
Authors not listed
The integration of machine learning methods is transforming many areas of research by, for instance, accelerating molecular dynamics simulations and enabling improved prediction and optimization of chemical reactions. However, despite this progress, the adoption of data-driven approaches in atomic layer deposition…
Authors not listed
Three-dimensional molecular generative models have emerged that produce de novo molecules both unconditionally and conditionally, e.g., within protein pockets. However, steering those models in a specific region of the chemical space that satisfies a set of desired properties remains challenging. In this study, we…
Sihan Xie, Thierry Tribout, Didier Boichard, Blaise Hanczar + 2 more
The development of dense genotyping platforms and high-throughput sequencing technologies has significantly advanced genetic analysis . Today, genomic studies rely on large biobanks that contain vast amounts of genomic data. However, working with such datasets presents several challenges, including high sequencing…
Alejandro Correa Rojo, Yves Moreau, Gökhan Ertaylan
The growing use of synthetic genomic data promises broader data access but raises unresolved concerns about privacy risk. We introduce PRISM-G, a model-agnostic framework that summarizes privacy exposure of synthetic genomes across three complementary components: (i) a proximity view that asks whether synthetic…
Subham Choudhury, Ilias Toumpe, Oussama Gabouj, Jakob Sebastian Behler + 2 more
Generative machine learning methods that utilize neural networks to parameterize large-scale and near-genome-scale kinetic models have yielded significant efficiency gains in model construction, paving the way for high-throughput dynamic metabolism studies in biomedical and biotechnological applications. Nevertheless…
Verena Schöning, Felix Hammann
In population pharmacokinetics (PopPK), non-linear mixed effects (NLME) models are used to simultaneously describe a drug's pharmacokinetics (PK) and dynamics (PD) in a patient population using systems of ordinary differential equations. In this field, machine learning is mainly used for data preparation, hypothesis…
Authors not listed
The ability to generate crystal structures directly from textual descriptions marks a pivotal advancement in materials informatics and underscores the emerging role of large language models (LLMs) in inverse design. In this work, we introduce CrysText, a text-conditioned framework that generates crystal structures in…
Authors not listed
Target-aware molecular generation models have emerged as promising tools for structure-based drug discovery, yet it remains unclear whether they genuinely exploit target information or merely resemble the Texas Sharpshooter fallacy by retrospectively rationalizing outputs. To address this, we introduce TarPass, a…
Authors not listed
Here we use a large language model for the exploration of chemical space of transition metal complexes (TMCs), fine-tuning the open-source Llama-3.2-1B model with a variation of the SMILES string representation tailored for coordination chemistry. We identify several key molecular properties that are critical to…
Authors not listed
Recent advances in generative artificial intelligence have enabled in silico molecular design to become a powerful approach for exploring chemical space toward specific design goals across various domains. However, in actual design workflows, determining the appropriate generation conditions, including generative…
Philippe J. Giabbanelli
Text-to-image generation is a form of generative artificial intelligence (GenAI) that converts textual descriptions into images. Most applications of GenAI in modeling and simulation (M&S) have focused on large language models for documentation, coding, or explanation. By contrast, the potential of image generation…
Authors not listed
Quantum-Aided Drug Design (QuADD) is a platform that utilizes quantum computing to solve a multi-objective optimization problem, producing novel druglike molecules optimized for interactions within a binding pocket. An alternative approach is that of Generative AI, which has emerged as a powerful tool in drug…