The training process for MV data. (a) Training accuracy as a function of epochs. (b) Training loss as a function of epochs. by Hiam Alquran (17636906)

Convergence curve of unimodal functions compared with the PSO family algorithms. by Yong Gao (29929)

Subjects:

Convergence curve of unimodal functions compared with the meta-heuristic algorithms. by Yong Gao (29929)

Subjects:

Recombinant mapping narrows region of interest to identify a loss-of-function allele of <i>mttp.</i> by McKenna Feltes (11242085)

Subjects:

Experimental results of various loss functions. by Dongshan Jiang (21866339)

“…Then, a CGBD module is proposed in the neck structure to reconstruct the ordinary convolution to improve the feature extraction ability of the model for small targets. Finally, the SIoU loss function is used to replace CIoU to accelerate the convergence of the model. …”

Probably pathogenic variants (algorithms). by Felipe Fidalgo (642532)

Training test results of different algorithms on natural scene exposure dataset. by Hao Wu (65943)

Subjects:

Slicing aided hyper inference algorithm. by Chunhua Yang (346871)

Subjects:

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

Subjects:

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

Subjects:

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

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

Wavelet transform and scaling function. by Menaa Nawaz (14304920)

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

Subjects:

SwinT-YOLOv4 loss function curve. by Kewei Lu (138871)

Limitation of the conventional DBT reconstruction algorithm. by Yunsu Choi (11993484)

ADT: A Generalized Algorithm and Program for Beyond Born–Oppenheimer Equations of “<i>N</i>” Dimensional Sub-Hilbert Space by Koushik Naskar (7510592)

“…For the numerical case, user can directly provide <i>ab initio</i> data (adiabatic PESs and NACTs) as input files to this software or can generate those input files through in-built python codes interfacing MOLPRO followed by ADT calculation. …”

Training and test results of different algorithms on uneven exposure data set of electrical equipment nameplate. by Hao Wu (65943)

Subjects:

S1 Dataset - by Hao Wu (65943)

Subjects:

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

Subjects:

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

Subjects: