In context of pattern classification, such an algorithm could be useful to determine if a sample belongs to one class or the other.To put the perceptron algorithm into the broader context of machine learning: The perceptron belongs to the category of supervised learning algorithms, single-layer ...
To put the perceptron algorithm into the broader context of machine learning: The perceptron belongs to the category of supervised learning algorithms, single-layer binary linear classifiers to be more specific. In brief, the task is to predict to which of two possible categories a certain data p...
The hyperparameters here are not only model-specific parameters but also the common neural network parameters that must be tuned, such as the number of neurons in the neural network layer, activation function selection, weight decay, and learning rate. Note that hyperparameter tuning is an ...
Then, we estimate the mixture proportions \({\alpha }_{n,k}\) and nuisance parameters of a multi-layer perceptron \({f}_{{\sigma }_{b}}\) (and hold all other variables fixed) by optimizing the following objective function: $$L\left({{{\bf{b}}}_{n}\right)=\mathop{\sum ...
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We train an encoder model (predictor, P) based on a fully connected multi-layer perceptron (MLP)55 on bulk RNA-seq data to estimate the correlation of drug response and bulk gene expressions. Parameters inside P are optimized with the classification loss (i.e., cross-entropy) between the ...
[251] adopts multiple-layer perceptron bagging to identify regulons, DeepDRIM [252] utilizes supervised deep neural network to reconstruct gene regulatory networks. In particular, DeepDRIM is shown to be tolerant to dropout events in scRNA-seq and identify distinct regulatory networks of B cells ...
we used automated cell type annotation to label cells to explore whether we could seed data interpretation with a first proposal for cell types. Cell type predictions from a multi-layer perceptron model trained on different data sets identified similar cell types to the labels from the curated ann...
In addition to the systematical difference among omics layers, single-cell data are often complicated by batch effect within the same layer. For example, the SHARE-seq data was processed in four libraries, one of which showed batch effect compared to the other three in scRNA-seq (Supplementary...
We trained a Multi-layer Perceptron (MLP) classifier using the annotated representatives’ cells and used it to predict the cell types of the rest of the non-representative samples’ cells generated by the deep learning model. As shown in Fig. 2b, cell clustering remains intact in the semi-...