Showing 1,241 - 1,260 results of 21,342 for search '(( significant decrease decrease ) OR ( significant ((sex decrease) OR (greater decrease)) ))', query time: 0.53s Refine Results
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  15. 1255

    S1 File - by Ahmed Akib Jawad Karim (20427740)

    Published 2025
    “…Furthermore underlined by the considerable decrease in model size without appreciable performance loss is the lower computational resources needed for training and deployment, hence facilitating greater applicability. …”
  16. 1256

    Confusion matrix for ClinicalBERT model. by Ahmed Akib Jawad Karim (20427740)

    Published 2025
    “…Furthermore underlined by the considerable decrease in model size without appreciable performance loss is the lower computational resources needed for training and deployment, hence facilitating greater applicability. …”
  17. 1257

    Confusion matrix for LastBERT model. by Ahmed Akib Jawad Karim (20427740)

    Published 2025
    “…Furthermore underlined by the considerable decrease in model size without appreciable performance loss is the lower computational resources needed for training and deployment, hence facilitating greater applicability. …”
  18. 1258

    Student model architecture. by Ahmed Akib Jawad Karim (20427740)

    Published 2025
    “…Furthermore underlined by the considerable decrease in model size without appreciable performance loss is the lower computational resources needed for training and deployment, hence facilitating greater applicability. …”
  19. 1259

    Configuration of the LastBERT model. by Ahmed Akib Jawad Karim (20427740)

    Published 2025
    “…Furthermore underlined by the considerable decrease in model size without appreciable performance loss is the lower computational resources needed for training and deployment, hence facilitating greater applicability. …”
  20. 1260

    Confusion matrix for DistilBERT model. by Ahmed Akib Jawad Karim (20427740)

    Published 2025
    “…Furthermore underlined by the considerable decrease in model size without appreciable performance loss is the lower computational resources needed for training and deployment, hence facilitating greater applicability. …”