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Showing 61 - 80 results of 151 for search '(((( element learner algorithm ) OR ( complement box algorithm ))) OR ( neural coding algorithm ))', query time: 0.37s Refine Results
  1. 61
    The structure of the data sample.

    The structure of the data sample. by Wenbing Shi (5806160)

    Published 2025
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  2. 62
    The genome recombination of neuroevolution.

    The genome recombination of neuroevolution. by Wenbing Shi (5806160)

    Published 2025
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  3. 63
    The principle of sample data augmentation.

    The principle of sample data augmentation. by Wenbing Shi (5806160)

    Published 2025
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  4. 64
    The fitness of NANEAT model evolution.

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

    Published 2025
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  5. 65
    The speciation of NANEAT model evolution.

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

    Published 2025
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  6. 66
    Data and R code related to the submitted manuscrit entitled "In situ imagery unravels resources exploitation strategies among ant communities in a tropical agrosystem"

    Data and R code related to the submitted manuscrit entitled "In situ imagery unravels resources exploitation strategies among ant communities in a tropical agrosystem" by Dominique Carval (20964191)

    Published 2025
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  7. 67
    Data and code from: AI-based image profiling and detection for the beetle byte quintet using Vision Transformer (ViT) in advanced stored product infestation monitoring

    Data and code from: AI-based image profiling and detection for the beetle byte quintet using Vision Transformer (ViT) in advanced stored product infestation monitoring by Alison Gerken (17362585)

    Published 2025
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  8. 68
    Monotone Cubic B-Splines with a Neural-Network Generator

    Monotone Cubic B-Splines with a Neural-Network Generator by Lijun Wang (176511)

    Published 2024
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  9. 69
    Correlation matrices of handcrafted features before and after feature selection. The figure presents two heatmaps illustrating the correlation among 52 handcrafted features derived from the 3-level wavelet transform (WT) at four resolutions. These features were used as inputs for traditional ML algorithms. The left map demonstrates the correlation matrix of 52 features crafted from 4 resolutions of the 3-level WT, which are used as inputs for traditional ML algorithms. The heat map (on the right) is the correlation matrix after feature selection, in which features whose Pearson’s correlation coefficient is higher than 0.8 are considered highly correlated. We keep only one representative feature and remove its highly correlated features.

    Correlation matrices of handcrafted features before and after feature selection. The figure presents two heatmaps illustrating the correlation among 52 handcrafted features derived... by Quang Dung Dinh (20995766)

    Published 2025
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  10. 70
    Box-and-whisker plots showing the distributions of (a)Paddick Conformity Index (PCI), (b) Homogeneity Index (HI), (c) Gradient Index (GI), and (d) Normal Tissue Integral Dose (NTID) for the three algorithms (MC: salmon, AXB: light green, AAA: sky blue).

    Box-and-whisker plots showing the distributions of (a)Paddick Conformity Index (PCI), (b) Homogeneity Index (HI), (c) Gradient Index (GI), and (d) Normal Tissue Integral Dose (NTID... by Kainan Shao (8999378)

    Published 2025
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  11. 71
    Genosophus: A Dynamical-Systems Diagnostic Engine for Neural Representation Analysis

    Genosophus: A Dynamical-Systems Diagnostic Engine for Neural Representation Analysis by Alan Glanz (22109698)

    Published 2025
    “…<h2><b>Genosophus: A Dynamical-Systems Diagnostic Engine for Neural Representation Analysis</b></h2><p dir="ltr"><b>© 2025 Alan J. …”
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  12. 72
    Code for MR-based diagnosis of PCNSL

    Code for MR-based diagnosis of PCNSL by Paul Naser (17776497)

    Published 2025
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  13. 73
    <b>Microscopy data, analysis code, and segmentation models for Phenotypic drug susceptibility testing for </b><b><i>Mycobacterium tuberculosis </i></b><b>variant</b><b><i> bovis </i></b><b>BCG in </b><b>12-hour </b>

    <b>Microscopy data, analysis code, and segmentation models for Phenotypic drug susceptibility testing for </b><b><i>Mycobacterium tuberculosis </i></b><b>variant</b><b><i> bovis </... by Buu Minh Tran (19530787)

    Published 2025
    “…This study used microfluidic chips, microscopy, and deep neural network algorithms to monitor the growth of <i>Mycobacterium bovis</i> Bacillus Calmette–Guérin (BCG) and <i>Mycobacterium smegmatis</i> in the presence of antibiotics. …”
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  14. 74
    Research data for paper "Loss shaping enhances exact gradient learning with Eventprop in Spiking Neural Networks"

    Research data for paper "Loss shaping enhances exact gradient learning with Eventprop in Spiking Neural Networks" by Thomas Nowotny (4462033)

    Published 2025
    “…<p dir="ltr">The data in this repository was generated in the context of training spiking neural networks for keyword recognition using the Eventprop algorithm. …”
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  15. 75
    Video 1_TDE-3: an improved prior for optical flow computation in spiking neural networks.mp4

    Video 1_TDE-3: an improved prior for optical flow computation in spiking neural networks.mp4 by Matthew Yedutenko (5142461)

    Published 2025
    “…However, on the algorithmic level, this design leads to a loss of direction-selectivity of individual TDEs in textured environments. …”
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  16. 76
    Data Sheet 1_TDE-3: an improved prior for optical flow computation in spiking neural networks.pdf

    Data Sheet 1_TDE-3: an improved prior for optical flow computation in spiking neural networks.pdf by Matthew Yedutenko (5142461)

    Published 2025
    “…However, on the algorithmic level, this design leads to a loss of direction-selectivity of individual TDEs in textured environments. …”
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  17. 77
    <b>BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification</b>

    <b>BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification</b> by BRISC Dataset (22559540)

    Published 2025
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  18. 78
    The simulation of OSCNN involves loading data from a laser and SLM and simulating different optical free space components.

    The simulation of OSCNN involves loading data from a laser and SLM and simulating different optical free space components. by Reyhane Ahmadi (14857363)

    Published 2024
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  19. 79
    Different implementations on MNIST object detection accuracy (%) with input image size.

    Different implementations on MNIST object detection accuracy (%) with input image size. by Reyhane Ahmadi (14857363)

    Published 2024
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  20. 80
    Outline of OSCNN.

    Outline of OSCNN. by Reyhane Ahmadi (14857363)

    Published 2024
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