16 papers · ranked by Valyu relevance
Athanasios Voulodimos, Nikolaos Doulamis, Anastasios Doulamis, Eftychios Protopapadakis
'Eftychios Protopapadakis'] Over the last years deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields, with computer vision being one of the most prominent cases. This review paper provides a brief overview of some of the most significant deep…
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…
Jakub M. Tomczak
There are multiple classes of (deep) generative models, namely, mixture models, Probabilistic Circuits, Autoregressive Models (and their special example, Large Language Models), Flow-based Models, Latent Variable Models, GANs, Hybrid (or Joint) Models, Score-based Generative Models, Diffusion-based Models, and…
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…
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…
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…
Alain Oliviero-Durmus, Yazid Janati, Eric Moulines, Marcelo Pereyra + 1 more
'Sebastian Reich'] This special issue addresses Bayesian inverse problems using data-driven priors derived from deep generative models (DGMs) and the convergence of generative modelling techniques and Bayesian inference methods. Conventional Bayesian priors often fail to accurately capture the properties and the…
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…
Marco Zorzi, Alberto Testolin, Ivilin P. Stoianov
Deep unsupervised learning in stochastic recurrent neural networks with many layers of hidden units is a recent breakthrough in neural computation research. These networks build a hierarchy of progressively more complex distributed representations of the sensory data by fitting a hierarchical generative model. In this…
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…
Changwei Gong, Changhong Jing, Xuhang Chen, Chi Man Pun + 6 more
'Guoli Huang' 'Ashirbani Saha' 'Martin Nieuwoudt' 'Han-Xiong Li' 'Yong Hu' 'Shuqiang Wang'] Recent years have witnessed a significant advancement in brain imaging techniques that offer a non-invasive approach to mapping the structure and function of the brain. Concurrently, generative artificial intelligence (AI) has…
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…
Chensi Cao, Feng Liu, Hai Tan, Deshou Song + 5 more
'Weizhong Li' 'Yiming Zhou' 'Xiaochen Bo' 'Zhi Xie'] Advances in biological and medical technologies have been providing us explosive volumes of biological and physiological data, such as medical images, electroencephalography, genomic and protein sequences. Learning from these data facilitates the understanding of…
Aman Singh, Tokunbo Ogunfunmi, Sotiris Kotsiantis
Autoencoders are a self-supervised learning system where, during training, the output is an approximation of the input. Typically, autoencoders have three parts: Encoder (which produces a compressed latent space representation of the input data), the Latent Space (which retains the knowledge in the input data with…
Miguel Pérez-Enciso, Laura M. Zingaretti
Deep learning (DL) has emerged as a powerful tool to make accurate predictions from complex data such as image, text, or video. However, its ability to predict phenotypic values from molecular data is less well studied. Here, we describe the theoretical foundations of DL and provide a generic code that can be easily…
Mahta Ramezanian-Panahi, Germán Abrevaya, Jean-Christophe Gagnon-Audet, Vikram Voleti + 2 more
'Jean-Christophe Gagnon-Audet' 'Vikram Voleti' 'Irina Rish' 'Guillaume Dumas'] This review article gives a high-level overview of the approaches across different scales of organization and levels of abstraction. The studies covered in this paper include fundamental models in computational neuroscience, nonlinear…