16 papers · ranked by Valyu relevance
Ran He, Jie Cao, Tieniu Tan
Generative artificial intelligence (GAI) has recently achieved significant success, enabling anyone to create texts, images, videos and even computer codes while providing insights that might not be possible with traditional tools. To stimulate future research, this work provides a brief summary of the ongoing and…
Jakub M. Tomczak
Large Language Models (LLMs) have unlocked a new frontier in AI applications, significantly advancing the field of generative modeling. Beyond assisting with tasks such as preparing a presentation plan, LLMs have fundamentally reshaped human-computer interactions by enabling seamless communication between machines and…
Francesca Pia Panaccione, Sofia Mongardi, Marco Masseroli, Pietro Pinoli + 2 more
'Pietro Pinoli' 'Chandan K. Sen' 'George Truskey'] The advancement of computational genomics has significantly enhanced the use of data-driven solutions in disease prediction and precision medicine. Yet, challenges such as data scarcity, privacy constraints, and biases persist. Synthetic data generation offers a…
Romain Lopez, Adam Gayoso, Nir Yosef
Generative models provide a well-established statistical framework for evaluating uncertainty and deriving conclusions from large data sets especially in the presence of noise, sparsity, and bias. Initially developed for computer vision and natural language processing, these models have been shown to effectively…
Stefan Lenz, Moritz Hess, Harald Binder
Background The best way to calculate statistics from medical data is to use the data of individual patients. In some settings, this data is difficult to obtain due to privacy restrictions. In Germany, for example, it is not possible to pool routine data from different hospitals for research purposes without the consent…
Alexander Ororbia, Daniel Kifer
Neural generative models can be used to learn complex probability distributions from data, to sample from them, and to produce probability density estimates. We propose a computational framework for developing neural generative models inspired by the theory of predictive processing in the brain. According to predictive…
Martin Treppner, Harald Binder, Moritz Hess
Deep generative models can learn the underlying structure, such as pathways or gene programs, from omics data. We provide an introduction as well as an overview of such techniques, specifically illustrating their use with single-cell gene expression data. For example, the low dimensional latent representations offered…
V. Schetinger, S. Di Bartolomeo, M. El‐Assady, A. McNutt + 3 more
'J. P. A. Passos' 'J. L. Adams'] Title: Abstract Generative text-to-image models (as exemplified by DALL-E, MidJourney, and Stable Diffusion) have recently made enormous technological leaps, demonstrating impressive results in many graphical domains-from logo design to digital painting to photographic composition.…
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…
Pietro Barbiero, Ramon Viñas Torné, Pietro Lió
Objective: Modern medicine needs to shift from a wait and react, curative discipline to a preventative, interdisciplinary science aiming at providing personalized, systemic, and precise treatment plans to patients. To this purpose, we propose a “digital twin” of patients modeling the human body as a whole and providing…
Nick Polson, Vadim Sokolov, Ali Mohammad-Djafari
Generative Bayesian Computation (GBC) provides a simulation-based approach to Bayesian inference. A Quantile Neural Network (QNN) is trained to map samples from a base distribution to the posterior distribution. Our method applies equally to parametric and likelihood-free models. By generating a large training dataset…
Hristina Uzunova, Matthias Wilms, Nils D. Forkert, Heinz Handels + 1 more
Purpose This work aims for a systematic comparison of popular shape and appearance models. Here, two statistical and four deep-learning-based shape and appearance models are compared and evaluated in terms of their expressiveness described by their generalization ability and specificity as well as further properties…
Vinicius L. S. Silva, Claire E. Heaney, Yaqi Li, Christopher C. Pain
We propose a novel use of generative adversarial networks (GANs) (i) to make predictions in time (PredGAN) and (ii) to assimilate measurements (DA-PredGAN). In the latter case, we take advantage of the natural adjoint-like properties of generative models and the ability to simulate forwards and backwards in time. GANs…
Chiranjib Chakraborty, Manojit Bhattacharya, Soumen Pal, Md. Aminul Islam
'Md. Aminul Islam'] Dear Editor, Previously, we published a correspondence article in International Journal of Surgery about next-generation drug discovery and development using ChatGPT or Large Language Model (LLM)1. The article was very timely. However, we found that generative artificial intelligence (AI) is…
Thomas Burger
Generative artificial intelligence can be used to create realistic new data, even for complex real-world processes that cannot be exhaustively modeled: the model is simply learned from preexisting data. Generative artificial intelligence is therefore expected to be a game changer in omics research, where data…
M. Pérez-Enciso, L. M. Zingaretti, G. de los Campos
Among the broad area of artificial intelligence (AI), generative AI algorithms have emerged as a revolutionary technology able to produce highly realistic ‘synthetic’ data, akin to standard simulation but with fewer contraints. The main focus of generative AI has been on phenotypes, but here we argue it can serve as…