Zobrazeno 1 - 10
of 1 911
pro vyhledávání: '"KNOWLES, DAVID"'
Autor:
Stirn, Andrew, Knowles, David A.
Current clustering priors for deep latent variable models (DLVMs) require defining the number of clusters a-priori and are susceptible to poor initializations. Addressing these deficiencies could greatly benefit deep learning-based scRNA-seq analysis
Externí odkaz:
http://arxiv.org/abs/2402.04412
Crystal plasticity models are a powerful tool for predicting the deformation behaviour of polycrystalline materials accounting for the underlying grain morphology and texture. These models typically have a large number of parameters, an understanding
Externí odkaz:
http://arxiv.org/abs/2312.12024
The problem of system identification for the Kalman filter, relying on the expectation-maximization (EM) procedure to learn the underlying parameters of a dynamical system, has largely been studied assuming that observations are sampled at equally-sp
Externí odkaz:
http://arxiv.org/abs/2308.11933
This paper demonstrates the utility of organized numerical representations of genes in research involving flat string gene formats (i.e., FASTA/FASTQ5). FASTA/FASTQ files have several current limitations, such as their large file sizes, slow processi
Externí odkaz:
http://arxiv.org/abs/2308.05118
Autor:
Stirn, Andrew, Wessels, Hans-Hermann, Schertzer, Megan, Pereira, Laura, Sanjana, Neville E., Knowles, David A.
Heteroscedastic regression models a Gaussian variable's mean and variance as a function of covariates. Parametric methods that employ neural networks for these parameter maps can capture complex relationships in the data. Yet, optimizing network para
Externí odkaz:
http://arxiv.org/abs/2212.09184