DAGER: Deep Age, Gender and Emotion Recognition Using Convolutional Neural Networks Dehghan A, Ortiz E G, Shu G, et al. Dager: Deep age, gender and emotion recognition using convolutional neural netwo...7、Deep Learning for Solar Power Forecasting – An Approach Using Autoencoder and LSTM ...
Jung-Hua Wang, Chun-Yun Chung, 1998. "Optimal clustering using neural networks"Systems, Man, and Cybernetics, 1998. 1998 IEEE International Conference, 2(11-14): 1625-1630.Optimal clustering using neural network - Wang, Peng - 1998 () Citation Context ...12… , , , k} . 5. Classify...
Kurt I, Ture M, Kurum AT (2008) Comparing performances of logistic regression, classification and regression tree, and neural networks for predicting coronary artery disease. Expert Syst Appl 34(1):366–374 ArticleGoogle Scholar LaFreniere D, Zulkernine F, Barber D, Martin K (2016) Using mac...
3b. Instead of directly calculating the principal components using matrix operations and existing data, the principal components can also be obtained through training in neural networks, as discussed earlier. Figure 3c shows results obtained from an idealized neural network using Sanger’s rule, using...
Next, we showed that chooseR generalizes across widely-used clustering workflows by applying it to the same dataset using the Seurat package and the scVI workflow integrated into Scanpy [10,11]. Whereas Seurat uses PCA for dimensionality reduction, scVI uses deep neural networks to encode the tran...
Our proposal consists of using a nested layer model in which an unsupervised artificial neural network method is used as the first layer to perform tasks of clustering time series corresponding to the statistics of traffic accidents in Mexico for a parti
Efficient integration of heterogeneous single-cell transcriptomes using Scanorama. Nat. Biotechnol. 37, 685–691 (2019). Article Google Scholar Li J., Chen S., Pan X., Yuan Y., & Shen H.-B. Cell clustering for spatial transcriptomics data with graph neural networks. Zenodo https://doi....
scGNN (single cell graph neural networks) for single cell clustering and imputation using graph neural networks - juexinwang/scGNN
Neural networks are learned through covariates of observations, and the final layer of neural networks is allocated to one of the k-clusters through softmax layers. The empirical lifetime distribution is obtained from the Kaplan–Meier estimator using the assigned clusters, and the model is trained...
previous article we solved a similar problem using convolutional networks. However, in supervised learning, we were looking for specific features which are characteristic of this specific task. In contrast, in unsupervised learning we have to compress data with minimal information lost. ...