computer_science2 papersavg year 2026quality 4/5

direct datasets native entropy scales

Research gap analysis derived from 2 computer_science papers in our local library.

The gap

Entropy scales with the number of classes K, preventing direct comparison of model uncertainty or calibration across different datasets without normalization.; One limitation was the lack of VNF-native datasets for direct comparison.; One limitation was the lack of VNF-native datasets for direct comparison.

Research trend

Emerging — attention growing, methods still coalescing.

Supporting evidence — 3 representative gaps

  • A systematic evaluation of uncertainty quantification techniques in deep learning: a case study in photoplethysmography signal analysis (2026) · doi

    Entropy scales with the number of classes K, preventing direct comparison of model uncertainty or calibration across different datasets without normalization.

    Keywords: entropy scales number classes preventing direct comparison model uncertainty calibration across different datasets without normalization
  • Evaluating Cross-Domain Generalisation of Deep Learning Models in Network Traffic Classification: A Statistical and Experimental Analysis of VNF and PNF (2026) · doi

    One limitation was the lack of VNF-native datasets for direct comparison.

    Keywords: limitation lack native datasets direct comparison
  • Evaluating Cross-Domain Generalisation of Deep Learning Models in Network Traffic Classification: A Statistical and Experimental Analysis of VNF and PNF (2026) · doi

    One limitation was the lack of VNF-native datasets for direct comparison.

    Keywords: limitation lack native datasets direct comparison

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