Activation functions: Sigmoid function and ReLU function. by Wan-Hua Zhang (21601139)

Examples of between- and within-community Beta distributions. by Alexandria M. Jensen (6494957)

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Summary of greedy algorithms presented in this section. Details of approximation factors for FastGreedy and UnitGreedy may be found in [26] and [25], respectively. by Jeffrey Keithley (14626551)

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Overview of the wavess algorithm and R package. by Narmada Sambaturu (4989539)

Analysis of ABIDE dataset: Conventional regression frameworks for optimized algorithmic value. by Alexandria M. Jensen (6494957)

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Comparison of algorithms across metrics in test problems. by Jihui Hu (19812736)

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Function <i>χ</i>_<i>opt</i> superimposed with the phase-field function indicating the channel geometry. by Martina Bukač (20161460)

Distribution of cross correlations in functional connectivity in ABIDE sample. by Alexandria M. Jensen (6494957)

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Performance comparison of different mainstream algorithms. by Haisong Xu (141729)

Percentage totBurden and percentage maxBurden reduced by UnitGreedy, SingletonGreedy, FastGreedy and baselines for = 0.535 in Iowa for FRED (first column) and SafeGraph (second col... by Jeffrey Keithley (14626551)

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Metapopulation model notation. by Jeffrey Keithley (14626551)

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Estimates of for each problem instance. by Jeffrey Keithley (14626551)

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The instance of MaxCasesAverted and MaxPeaksReduced is a graph <i>G</i> constructed from the given instance of Max k-SI. by Jeffrey Keithley (14626551)

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Percentage totBurden and percentage maxBurden reduced by UnitGreedy, FastGreedy and baselines for = 0.525 in Texas for FRED (first column) and SafeGraph (second column). by Jeffrey Keithley (14626551)

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Approximation factors for each problem instance. by Jeffrey Keithley (14626551)

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Percentage totBurden and percentage maxBurden reduced by all approaches for = 0.5345 in New Hampshire for FRED (first column) and SafeGraph (second column). by Jeffrey Keithley (14626551)

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FastGreedy, UnitGreedy, SingletonGreedy, 3-EnumGreedy, and POMS comparison with respect to practical running time (estimated for POMS) to achieve approximation guarantee for New Ha... by Jeffrey Keithley (14626551)

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Comparison of average precision means of different algorithms. by Haisong Xu (141729)

Algorithm comparison experiment on the RSOD dataset. by Pingping Yan (462509)

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The parameters of algorithms. by Le Huu Binh (18340233)

“…However, solving the RNP problem in WMN is difficult because it is NP-hard. As a result, this problem can only be solved using approximate optimization algorithms such as heuristics and meta-heuristics. …”