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

Raw task performance on both experiments, with respect to conditions. by Anis Najar (9745561)

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Principal Component Analysis (PCA) of the independent dataset from the HPC cohort. by Erica A. K. DePasquale (11851483)

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Completion times for different algorithms. by Jianbin Zheng (587000)

“…Additionally, value function normalization and adaptive learning rate strategies are applied to accelerate convergence. …”

The average cumulative reward of algorithms. by Jianbin Zheng (587000)

“…Additionally, value function normalization and adaptive learning rate strategies are applied to accelerate convergence. …”

Fitness function parameters. by Ankita Sharma (3183054)

Infrared Spectroscopy Coupled with Machine Learning Algorithms to Investigate Vascular Dysfunction in Ovariectomy: An Animal Model Study by Tháfanys S. Travezani (20609398)

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. …”

Simulation settings of rMAPPO algorithm. by Jianbin Zheng (587000)

“…Additionally, value function normalization and adaptive learning rate strategies are applied to accelerate convergence. …”

Venn diagrams showing overlap of the total of overrepresented/underrepresented kinases for both cohorts. by Erica A. K. DePasquale (11851483)

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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. …”

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

Explained variance ration of the PCA algorithm. by Abeer Aljohani (18497914)

Performance of PCA and Joint Autoencoder (JAE) denoising algorithms. by Ege Altan (11768156)

Iteration versus fitness function. by B. Venkatesh (17202747)

As for Fig 2, we present failure rates as a function of the cohort size (vertical axis) versus the number of distractors (horizontal axis), for the Smyth and McClave baseline algorithm from [76]. by Daniela Huppenkothen (9174507)

“…<p>We explore the behaviour of the baseline algorithm both in terms of whether it can recover maximally diverse cohorts (upper four panels), and whether it can recover imbalanced cohorts. …”

Objective function for pressure control system. by Fazal E. Nasir (14992506)

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

NLDock: a Fast Nucleic Acid–Ligand Docking Algorithm for Modeling RNA/DNA–Ligand Complexes by Yuyu Feng (11371729)

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Filled contour plot based on histograms of the occurence-frequency of binned values of and as a function of rate ratio . by Paul Kinsler (3975620)