The run time for each algorithm in seconds. by Edward Antonian (21453161)

“…Finally, we use the Laplace approximation to determine a lower bound for the out-of-sample prediction error and derive a scalable expression for the marginal variance of each prediction. These methods are tested on both real and synthetic data, with the former taken from a network of air quality monitoring stations across California. …”

Data and code resources. by Sam Hall-McMaster (10343795)

“…Decoding from functional magnetic resonance imaging data revealed that solutions from the SF&GPI algorithm were activated on test tasks in visual and prefrontal cortex. …”

Comparison of grayscale conversion algorithms. by Tianke Fang (22648035)

Comparison of noise reduction algorithms. by Tianke Fang (22648035)

Temporal response patterns determined by the GMM clustering algorithm across varying primary stimulus parameters (amplitude or onset ramp velocity) of all test stimuli. by Dinh K. Tang (20469082)

Algorithm testing. by Ziad M. Ali (12516532)

“…The accuracy of the proposed equivalent circuits is demonstrated on two solar cells/modules, RTC-F and MSX-60, showing equal or better performance than the standard PV_DDM equivalent circuit. Further testing on a commercial solar panel under different irradiance and temperature conditions confirms the applicability of the proposed models. …”

The genome coding scheme. by Wenbing Shi (5806160)

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Dataset for Fine-Tuning Code Generation Models: Kannada-English Algorithmic Statements and Python Code by Goutami Sooda (20714729)

Pseudo code. by Xuwei Liu (6034592)

“…On the basis of the traditional quota formulation model based on statistical theory, artificial neural networks are introduced, and regularization techniques and particle swarm optimization algorithms are taken to optimize the model performance. …”

Algorithm testing environment settings. by Hua Song (113155)

The speciation of ANEAT model evolution. by Wenbing Shi (5806160)

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The fitness of ANEAT model evolution. by Wenbing Shi (5806160)

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The fitness of NANEAT model evolution. by Wenbing Shi (5806160)

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The speciation of NANEAT model evolution. by Wenbing Shi (5806160)

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Comparison of optical and silicon-based neural network implementations. by Reyhane Ahmadi (14857363)

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The construction process of the CGO prediction model. by Wenbing Shi (5806160)

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Performance of models by different algorithms. by Yong Zhang (5893)

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Pseudo-code for the study design model. by Jianpeng Zhang (528185)

“…The results showed that the proposed model only took about 8 seconds to complete training and testing, while all other models required about 10 seconds. …”

The structure of the NANEAT model corresponds to the optimal species genome. by Wenbing Shi (5806160)

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The structure of the ANEAT model corresponds to the optimal species genome. by Wenbing Shi (5806160)

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