Variations of loss functions versus epoch number. by Xueliang Guo (4797057)

Comparison of performance obtained by our approach with other state-of-the-art algorithms. by Sajad Mousavi (6672425)

Confusion matrices of machine learning models trained on data oversampled by SMOTE and data trained with class weights given to the loss function. by Lucas Teoh (12878206)

Listed python codes of our algorithm with explanations (Python 3.9 recommended). by Takayuki Niizato (162226)

Search Algorithms and Loss Functions for Bayesian Clustering by David B. Dahl (11761055)

“…<p>We propose a randomized greedy search algorithm to find a point estimate for a random partition based on a loss function and posterior Monte Carlo samples. …”

APSO–CNN and FNN algorithms’ loss function training sets. by Ahmed Bahaa (12887063)

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APSO–CNN and FNN algorithms’ loss function validation sets. by Ahmed Bahaa (12887063)

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Loss function values of target detection algorithms during training. by Guodong Su (7876934)

“…<p>Loss function values of target detection algorithms during training.…”

Python-based Monte Carlo Simulation Tool by Yangfan Jiang (9372782)

Comparison results of the loss functions. by Jia Li (160557)

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Loss function variation curve. by Chunhua Yang (346871)

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APSO–WOA–CNN and FNN algorithms’ loss function validation sets. by Ahmed Bahaa (12887063)

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APSO–WOA–CNN and FNN algorithms’ loss function training sets. by Ahmed Bahaa (12887063)

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Changes in loss values for different algorithms. by Xiaojun Wang (50332)

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Recursive attention function and loss function change. by Wang Linfeng (17102068)

Comparison of the curves of the DCIOU loss function and the CIOU loss function. by Xunqian Xu (21261899)

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Loss Function Comparison. by Hongwei Yue (574068)

Loss Function Comparison. by Hongwei Yue (574068)

Python-Based Algorithm for Estimating NRTL Model Parameters with UNIFAC Model Simulation Results by Se-Hee Jo (20554623)

“…This algorithm conducts a series of procedures: (1) fragmentation of the molecules into functional groups from SMILES, (2) calculation of activity coefficients under predetermined temperature and mole fraction conditions by employing universal quasi-chemical functional group activity coefficient (UNIFAC) model, and (3) regression of NRTL model parameters by employing UNIFAC model simulation results in the differential evolution algorithm (DEA) and Nelder–Mead method (NMM). …”

Algorithm framework of this paper. by Xiaoqin Wu (470428)

“…<div><p>A brain tumor magnetic resonance image processing algorithm can help doctors to diagnose and treat the patient’s condition, which has important application significance in clinical medicine. …”