13 papers · ranked by Valyu relevance
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…
Nicola Milano, Monica Casella, Raffaella Esposito, Davide Marocco + 1 more
'Gianluca Borghini'] Latent variables analysis is an important part of psychometric research. In this context, factor analysis and other related techniques have been widely applied for the investigation of the internal structure of psychometric tests. However, these methods perform a linear dimensionality reduction…
Shichen Cao, Jingjing Li, Kenric P. Nelson, Mark A. Kon + 1 more
'Pierre Alquier'] We present a coupled variational autoencoder (VAE) method, which improves the accuracy and robustness of the model representation of handwritten numeral images. The improvement is measured in both increasing the likelihood of the reconstructed images and in reducing divergence between the posterior…
Yoshihiro Nagano, Ryo Karakida, Masato Okada
Deep neural networks are good at extracting low-dimensional subspaces (latent spaces) that represent the essential features inside a high-dimensional dataset. Deep generative models represented by variational autoencoders (VAEs) can generate and infer high-quality datasets, such as images. In particular, VAEs can…
Sabrina Rashid, Sohrab Shah, Ziv Bar-Joseph, Ravi Pandya
Intra-tumor heterogeneity is one of the key confounding factors in deciphering tumor evolution. Malignant cells exhibit variations in their gene expression, copy numbers, and mutation even when originating from a single progenitor cell. Single cell sequencing of tumor cells has recently emerged as a viable option for…
Margarita Geleta, Daniel Mas Montserrat, Xavier Giro-i-Nieto, Alexander G. Ioannidis
Modern biobanks provide numerous high-resolution genomic sequences of diverse populations. These datasets enable a better understanding of genotype-phenotype interactions with genome-wide association studies (GWAS) and power a new personalized precision medicine with polygenic risk scores (PRS). In order to account for…
Ahmed M. Daoud, Osama M. Elkomy, Walid I. Khedr, Khalid M. Hosny
In this paper, we propose a new hybrid model, called AVE, that integrates the strengths of Autoencoder (AE) and Variational Autoencoder (VAE) to enhance outlier detection for numerous high-dimensional datasets. The proposed architecture leverages the reconstruction strengths of AE and the regularized latent space of…
Chao Zhang
Variational Autoencoder (VAE) is a generative model from the computer vision community; it learns a latent representation of the images and generates new images in an unsupervised way. Recently, Vanilla VAE has been applied to analyse single-cell datasets, in the hope of harnessing the representation power of latent…
Christopher Heje Grønbech, Maximillian Fornitz Vording, Pascal Timshel, Casper Kaae Sønderby + 2 more
Models for analysing and making relevant biological inferences from massive amounts of complex single-cell transcriptomic data typically require several individual dataprocessing steps, each with their own set of hyperparameter choices. With deep generative models one can work directly with count data, make…
Sergei Popov, Mikhail Lazarev, Vladislav Belavin, Denis Derkach + 2 more
'Andrey Ustyuzhanin' 'Muhammad Aleem'] There are many problems in physics, biology, and other natural sciences in which symbolic regression can provide valuable insights and discover new laws of nature. Widespread deep neural networks do not provide interpretable solutions. Meanwhile, symbolic expressions give us a…
Kuo-Hao Fanchiang, Cheng-Chien Kuo, Anastasios Doulamis
Dry-type power transformers play a critical role in the power system. Detecting various overheating faults in the running state of the power transformer is necessary to avoid the collapse of the power system. In this paper, we propose a novel deep variational autoencoder-based anomaly detection method to recognize the…
Florian Eichin, Maren Hackenberg, Caroline Broichhagen, Antje Kilias + 3 more
Live imaging techniques, such as two-photon imaging, promise novel insights into cellular activity patterns at a high spatial and temporal resolution. While current deep learning approaches typically focus on specific supervised tasks in the analysis of such data, e.g., learning a segmentation mask as a basis for…
Ran Liu, Mehdi Azabou, Max Dabagia, Chi-Heng Lin + 4 more
Meaningful and simplified representations of neural activity can yield insights into how and what information is being processed within a neural circuit. However, without labels, finding representations that reveal the link between the brain and behavior can be challenging. Here, we introduce a novel unsupervised…