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Showing 1 - 20 results of 9,744 for search '(( alignment network algorithm ) OR ((( data encoding algorithm ) OR ( based models algorithm ))))', query time: 0.48s Refine Results
  1. 1
    Federated averaging algorithm.

    Federated averaging algorithm. by Yan Qin (118276)

    Published 2025
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  2. 2
    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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  3. 3
    UpSet diagram illustrating the overlap of DNBs selected by our method and top-ranked genes by the 13 baseline centrality-based ranking algorithms.

    UpSet diagram illustrating the overlap of DNBs selected by our method and top-ranked genes by the 13 baseline centrality-based ranking algorithms. by Fatemeh Keikha (20917848)

    Published 2025
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  4. 4
    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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  5. 5
    Search algorithm performance comparison.

    Search algorithm performance comparison. by Zhaohui Du (414105)

    Published 2025
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  6. 6
    Parameters of ML algorithms.

    Parameters of ML algorithms. by Md. Maeen Molla (20827041)

    Published 2025
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  7. 7
    Comparative results with existing representative algorithms.

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

    Published 2025
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  8. 8
    Heuristic approaches for configuring network alignment algorithms.

    Heuristic approaches for configuring network alignment algorithms. by Giuseppe Agapito (104634)

    Published 2025
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  9. 9
    Flowchart of algorithm principles.

    Flowchart of algorithm principles. by Wenjuan Zhou (115285)

    Published 2025
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  10. 10
    The workflow of Algorithm 2 with the example in

    The workflow of Algorithm 2 with the example in by Badhan Das (7434362)

    Published 2025
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  11. 11
    Decompression algorithm for the proposed HSCRC encoding.

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

    Published 2024
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  12. 12
    Horizontal comparison of models.

    Horizontal comparison of models. by Hui Yang (91136)

    Published 2024
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  13. 13
    Indicator analysis of integrated models.

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

    Published 2024
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  14. 14
    Comparison of deep partitioning models.

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

    Published 2024
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  15. 15
    CNOT encoded features and CZ encoded features.

    CNOT encoded features and CZ encoded features. by Danish ul Khairi (19660534)

    Published 2025
    “…The experimental results align with the theoretical analysis: the CZ-based NNQEv2 model, when compared to the CNOT-based NNQEv1, consistently showed more stable training dynamics and yielded tighter confidence intervals in cross-validation. …”
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  16. 16
    Schematic diagram of the pillar encoding.

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

    Published 2025
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  17. 17
    LSTM auto encoder algorithm illustration.

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

    Published 2025
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  18. 18
    Adaptive model BD rate analysis.

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

    Published 2024
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  19. 19
    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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  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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