Effects of activation function on training accuracy and training loss. by Xinwu Du (14626908)

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WERs for different SE algorithms. by Fahad Khalil Peracha (15453204)

Average running times for the different algorithms. by Ayush Dogra (17054597)

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Qualitative comparisons of the proposed algorithm with 7 state-of-the-art algorithms in 4 representative CT-MRI image pairs. by Ayush Dogra (17054597)

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Qualitative comparisons of the proposed algorithm with 7 state-of-the-art algorithms in 4 representative CT-MRI image pairs. by Ayush Dogra (17054597)

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GA and loss function (Loss_L) parameters are optimized using their search spaces through the hyperparameter tuning. by Burcu Ozek (16949357)

Algorithm pseudocode. by Cheng-jie Chen (22272090)

“…In order to improve the clarity and visual effect of product appearance design, a visual edge feature enhancement method for product appearance design images based on an improved Retinex algorithm is proposed. …”

Algorithmic process of partial convolution. by Cuiying Yu (18437285)

Comparison of correction results of different algorithms on the data set of electrical equipment nameplate exposure. by Hao Wu (65943)

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An Efficient Algorithm for Minimizing Multi Non-Smooth Component Functions by Minh Pham (2458399)

“…<p>Many problems in statistics and machine learning can be formulated as an optimization problem of a finite sum of nonsmooth convex functions. We propose an algorithm to minimize this type of objective functions based on the idea of alternating linearization. …”

Run times of two algorithms. by Gourab Saha (8987405)

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Convergence rate of the proposed algorithm update. by Pingxin Wang (16747662)

Loss function and accuracy during training. by Shuang Guo (294074)

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Precision metric results and curves and loss values for 40 epoch rounds and loss function transformation curves. by Xia Zhao (215170)

Experience distribution diagram of patient survival time and distribution function diagrams of various models function plot of GIED. by Qin Gong (118801)

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Test results of different algorithms. by Ling Zhao (111365)

“…<div><p>To solve the problems of insufficient global optimization ability and easy loss of population diversity in building interior layout design, this study proposes a novel layout optimization model integrating interactive genetic algorithm and improved differential evolutionary algorithm to improve the global optimization ability and maintain population diversity in building layout design. …”

GD and soft loss function (Loss_S) parameters are optimized through the hyperparameter tuning process within their search spaces. by Burcu Ozek (16949357)

The resultant accuracy curves while using the hinge loss function for the best model. by Abdullah Almansour (6273935)

Comparison of mAP@0.5 and Recall for mainstream algorithms. by Junjie Lu (160350)

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Algorithm diagnostic accuracy comparison chart. by Bowen Li (200859)