XGBOOST model parameters in the process of training the learning curve. by Guanhai Gu (15188006)

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Illustration of the extension process. by Antoine Soulé (10892783)

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Key steps in neurodegeneration detection algorithm. by Andrew S. Clark (16510496)

“…Using the structures obtained through binarizing these images, the algorithm detects which image contains the dendrites (Scale bar = 10 μm). …”

Quantitative comparison of similarity and essential elements of physical fitness between sports types using SHAP value of XGBoost. by Yun-Hwan Lee (18291231)

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The power iteration algorithm demonstrates how complicated dynamic structures arise from topologically connected elements of similar magnitude within the Jacobian matrix. by Bin Du (138774)

“…The left eigenvectors are the modes of the metabolic network. The algorithm left multiplies the Jacobian matrix by a random vector (<i>u</i>_<i>i</i>), normalizes the resulting vector and repeats the process until the vector converges to the eigenvector. …”

Processing parameters. by Jiahao Yong (9772692)

datasheet1_Algorithmic Probability-Guided Machine Learning on Non-Differentiable Spaces.pdf by Santiago Hernández-Orozco (5070209)

“…In an algorithmic space the order of its element is given by its algorithmic probability, which arises naturally from computable processes. …”

datasheet2_Algorithmic Probability-Guided Machine Learning on Non-Differentiable Spaces.zip by Santiago Hernández-Orozco (5070209)

“…In an algorithmic space the order of its element is given by its algorithmic probability, which arises naturally from computable processes. …”

datasheet1_Algorithmic Probability-Guided Machine Learning on Non-Differentiable Spaces.pdf by Santiago Hernández-Orozco (5070209)

“…In an algorithmic space the order of its element is given by its algorithmic probability, which arises naturally from computable processes. …”

datasheet2_Algorithmic Probability-Guided Machine Learning on Non-Differentiable Spaces.zip by Santiago Hernández-Orozco (5070209)

“…In an algorithmic space the order of its element is given by its algorithmic probability, which arises naturally from computable processes. …”

Scatterplots of the first attended locations predicted by the saliency algorithms. by John K. Tsotsos (7435094)

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Models that have been implemented for each layer. by Shayesteh Tabatabaei (12280016)

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Finite elements analysis. by Eduardo Iáñez (430386)

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Finite element models of the pipes. by Delong Huang (10271165)

Finite element model of the finger segment. by Jean Chenevier (4875877)

Comparison of Pearson correlations and F-measure values of NN-RNALoc algorithm with other methods for two datasets. by Negin Sadat Babaiha (16985907)

The actual training effect of four algorithms on original photo data (All images are original images taken by the author). by Xiaojun Wang (50332)

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Process followed to develop and validate the MAMELI algorithm. by Valentina Bollati (106778)

Data_Sheet_1_Modeling Macroscopic Material Behavior With Machine Learning Algorithms Trained by Micromechanical Simulations.CSV by Denise Reimann (7213364)

“…The microstructure is modeled by a representative volume element (RVE), and the anisotropic mechanical behavior of individual grains is described by a crystal plasticity model. …”

Data_Sheet_2_Modeling Macroscopic Material Behavior With Machine Learning Algorithms Trained by Micromechanical Simulations.pdf by Denise Reimann (7213364)

“…The microstructure is modeled by a representative volume element (RVE), and the anisotropic mechanical behavior of individual grains is described by a crystal plasticity model. …”