Loss functions between PCA-DNN model and PCA-LSTM model. by Cihun-Siyong Alex Gong (11874066)

Testing function diagram of QIMPN-PCA-DC. by Jun Zhao (59250)

Reconstructing <i>sparse</i>, binary patterns using message passing algorithms and PCA. by Sebastian Goldt (14522594)

“…<p>We plot the mse per pattern obtained by the AMP algorithm, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1010813#pcbi.1010813.e072" target="_blank">Eq (32)</a>, as a function of the effective noise Δ (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1010813#pcbi.1010813.e032" target="_blank">9</a>), for random (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1010813#pcbi.1010813.e044" target="_blank">15</a>) and informed (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1010813#pcbi.1010813.e045" target="_blank">16</a>) initialisations. …”

The image of the Aekley function. by Zhengyu Xu (8550660)

The loss function of network structures, LPC-DNN, LPC-PCA-DNN, LPC-LSTM, LPC-PCA-LSTM, MFCC-DNN, MFCC-PCA-DNN, MFCC-LSTM and MFCC-PCA-LSTM, as the epoch increases. by Cihun-Siyong Alex Gong (11874066)

Functional PCA With Covariate-Dependent Mean and Covariance Structure by Fei Ding (134577)

“…<p>Incorporating covariates into functional principal component analysis (PCA) can substantially improve the representation efficiency of the principal components and predictive performance. …”

Dataset for 'An Automated Workflow of Deep Learning Ensemble Model with Principal Component Analysis for Enhanced Material Characterisation in Energy Dispersive Spectroscopy' by Fanzhi Su (21005174)

Comparison of different algorithms. by Dawei Wang (471687)

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AUC scores of anomaly detection algorithms. by GaoXiang Zhao (21499525)

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Recall scores of anomaly detection algorithms. by GaoXiang Zhao (21499525)

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The Precision-Recall curve of both the models. by Aastha Vatsyayan (9981916)

“…</p><p>Results</p><p>Using an independent validation dataset of ACMG & AMP classified variants, as well as a patient set of functionally validated variants, we showed how both algorithms perform and can be used to classify large numbers of variants in clinical as well as research settings.…”

Continuous Probability Distributions generated by the PIPE Algorithm by LUIS G.B. PINHO (14073372)

“…<div><p>Abstract We investigate the use of the Probabilistic Incremental Programming Evolution (PIPE) algorithm as a tool to construct continuous cumulative distribution functions to model given data sets. …”

Flowchart for Algorithm 1. by Sagvan Y. Musa (17797611)

Flowchart for Algorithm 2. by Sagvan Y. Musa (17797611)

Results of the application of different clustering algorithms to average functional connectivity from healthy subjects. by Francisco Páscoa dos Santos (16510676)

“…<p>A) Resulting cluster inertia from applying the k-means algorithm described in the methods to empirical averaged functional connectivity from healthy subjects, with different numbers of clusters. …”

Multi-algorithm comparison figure. by Dawei Wang (471687)

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Gillespie algorithm simulation parameters. by Nicholas H. Vitale (20469289)

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Feature selection algorithm. by Mahmoud Zeydabadinezhad (12289570)

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A Genetic Algorithm Approach for Compact Wave Function Representations in Spin-Adapted Bases by Maru Song (22593561)

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Knowledge graph model rating function. by Chunjuan Li (7890182)