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Showing 1 - 20 results of 9,419 for search '(((( data encoding algorithm ) OR ( data modelling algorithm ))) OR ( element each algorithm ))', query time: 0.54s Refine Results
  1. 1
    Observations that influence the accuracy of the algorithm.

    Observations that influence the accuracy of the algorithm. by Rabnawaz Khan (6509084)

    Published 2025
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  2. 2
    The run time for each algorithm in seconds.

    The run time for each algorithm in seconds. by Edward Antonian (21453161)

    Published 2025
    “…The goal of this paper is to examine several extensions to KGR/GPoG, with the aim of generalising them a wider variety of data scenarios. The first extension we consider is the case of graph signals that have only been partially recorded, meaning a subset of their elements is missing at observation time. …”
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  3. 3
    Federated averaging algorithm.

    Federated averaging algorithm. by Yan Qin (118276)

    Published 2025
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  4. 4
    Comparison of ResNet Classification Algorithm Expansion Experiments with Similar Data Enhancement Models.

    Comparison of ResNet Classification Algorithm Expansion Experiments with Similar Data Enhancement Models. by Chao Tang (10925)

    Published 2025
    “…<p>Comparison of ResNet Classification Algorithm Expansion Experiments with Similar Data Enhancement Models.…”
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  5. 5
    Search algorithm performance comparison.

    Search algorithm performance comparison. by Zhaohui Du (414105)

    Published 2025
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  6. 6
    Hair removal preprocessing using the DullRazor algorithm before image generation.

    Hair removal preprocessing using the DullRazor algorithm before image generation. by Mujung Kim (22367223)

    Published 2025
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  7. 7
    Flowchart of algorithm principles.

    Flowchart of algorithm principles. by Wenjuan Zhou (115285)

    Published 2025
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  8. 8
    Comparative results with existing representative algorithms.

    Comparative results with existing representative algorithms. by Yan Qin (118276)

    Published 2025
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  9. 9
    Horizontal comparison of models.

    Horizontal comparison of models. by Hui Yang (91136)

    Published 2024
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  10. 10
    Decompression algorithm for the proposed HSCRC encoding.

    Decompression algorithm for the proposed HSCRC encoding. by R. Sakthivel (2589547)

    Published 2024
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  11. 11
    Indicator analysis of integrated models.

    Indicator analysis of integrated models. by Hui Yang (91136)

    Published 2024
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  12. 12
    Comparison of deep partitioning models.

    Comparison of deep partitioning models. by Hui Yang (91136)

    Published 2024
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  13. 13
    Adaptive model BD rate analysis.

    Adaptive model BD rate analysis. by Hui Yang (91136)

    Published 2024
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  14. 14
    Minimal data set definition.

    Minimal data set definition. by Hui Yang (91136)

    Published 2024
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  15. 15
    LSTM auto encoder algorithm illustration.

    LSTM auto encoder algorithm illustration. by Hao Zhang (15339)

    Published 2025
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  16. 16
    Optimal Channel Encoding/Decoding Algorithm Design with MATLAB

    Optimal Channel Encoding/Decoding Algorithm Design with MATLAB by Saraf Mohaimen Chowdhury (21977243)

    Published 2025
    “…<p dir="ltr">An optimal channel encoding and decoding algorithm has been designed and implemented in MATLAB to improve data transmission efficiency and adaptability in communication systems.…”
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  17. 17
    Model Performance Comparison on European and Asian Electricity Market Data.

    Model Performance Comparison on European and Asian Electricity Market Data. by Nasir Nauman (22272299)

    Published 2025
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  18. 18
    Schematic diagram of the pillar encoding.

    Schematic diagram of the pillar encoding. by Xinpeng Yao (18882573)

    Published 2025
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  19. 19
    Comparison of model distances.

    Comparison of model distances. by Pan Wang (198097)

    Published 2025
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  20. 20
    The encoder employs deformable self-attention to transform multi-scale features into encoded multi-scale features. Query selection is used to derive the object queries from these encoded multi-scale features. Each encoded image feature undergoes classification, and the top-K features are selected as the content embeddings for the object queries. Additionally, bounding boxes are extracted from the top-K segmentation masks and are subsequently converted into positional embeddings. Following this, the object queries perform self-attention and deformable cross-attention with the encoded multi-scale features to produce the output embeddings. MLP, multi-layer perceptron.

    The encoder employs deformable self-attention to transform multi-scale features into encoded multi-scale features. Query selection is used to derive the object queries from these e... by Owen M. O’Connor (11965460)

    Published 2025
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