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sgdm  (MathWorks Inc)


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    MathWorks Inc sgdm
    Sgdm, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/sgdm/10__1016_slash_j__bspc__2024__107250-208-11-2
    Average 90 stars, based on 1 article reviews
    sgdm - by Bioz Stars, 2026-09
    90/100 stars

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    other:

    Article Title: The spatial structure of neural encoding in mouse posterior cortex during navigation
    Article Snippet: The network was trained using stochastic gradient descent with momentum (batch size, 512; learning rate 0.03; L2 regularization, 0.0001, otherwise default settings of sgdm in MATLAB).

    Article Title: The spatial structure of neural encoding in mouse posterior cortex during navigation
    Article Snippet: The network was trained using stochastic gradient descent with momentum (batch size, 64; learning rate 0.01; L2 regularization, 0.001, otherwise default settings of sgdm in MATLAB).

    Article Title: Automated MRI quantification of pediatric abdominal adipose tissue using convolutional neural networks and novel total intensity maps
    Article Snippet: Within the MATLAB tools, three optimizers were available for training of CNNs: SGDM, Adam and RMSprop.



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    Tested CNN Architectures and Training Details

    Journal: IEEE access : practical innovations, open solutions

    Article Title: Cross-organ, cross-modality transfer learning: feasibility study for segmentation and classification

    doi: 10.1109/access.2020.3038909

    Figure Lengend Snippet: Tested CNN Architectures and Training Details

    Article Snippet: For other hyperparameters, we used the default values of MATLAB for SGDM and Adam optimizer. table ft1 table-wrap mode="anchored" t5 TABLE II caption a7 Base Network SegNet-VGG16 DeepLabv3plus-ResNet18 Training on Intermediate Target Intermediate Target Choice of hyperpameters Optimizer SGDM ADAM Max epoch 32 50 32 50 Minibatch Size 4 4 128 128 Learning Rate le-3 le-3 le-5 le-4 Drop Factor No No No 0.9 Drop Period [Epoch] N/A N/A N/A 1 Open in a separate window Tested CNN Architectures and Training Details We let the weights of the network updated for every iteration during training.

    Techniques: Biomarker Discovery