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Models/KNN

KNN

Reported on 20 benchmarks across 6 tasks · 3 papers · 3 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Computer Vision7 results

  • Person Re-IdentificationoneSports Sensors Dataset
    LogLoss· 2020-11-02
    0.05735
    best: 0.01588 (SVM)
    SOTA
    Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports DatasetarXiv:2011.00958
  • Skills EvaluationoneSports Sensors Dataset
    LogLoss· 2020-11-02
    0.442
    best: 0.311 (SVM)
    SOTA
    Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports DatasetarXiv:2011.00958
  • Person Re-IdentificationoneSports Sensors Dataset
    Accuracy· 2020-11-02
    41.5
    best: 52.1 (Random Forest)
    Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports DatasetarXiv:2011.00958
  • Person Re-IdentificationoneSports Sensors Dataset
    ROC AUC· 2020-11-02
    0.84
    best: 0.919 (Random Forest)
    Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports DatasetarXiv:2011.00958
  • Skills EvaluationoneSports Sensors Dataset
    Accuracy· 2020-11-02
    74.1
    best: 85.6 (SVM)
    Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports DatasetarXiv:2011.00958
  • Skills EvaluationoneSports Sensors Dataset
    ROC AUC· 2020-11-02
    0.899
    best: 0.945 (SVM)
    Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports DatasetarXiv:2011.00958
  • Image ClassificationonSVHN
    Percentage error· 2015-11-19
    77.93
    best: 1 (E2E-M3)
    Unsupervised Representation Learning with Deep Convolutional Generative Adversarial NetworksarXiv:1511.06434

Methodology7 results

  • General ClassificationonXOR
    Accuracy· 2020-05-27
    93.1045
    SOTA
    SafeML: Safety Monitoring of Machine Learning Classifiers through Statistical Difference MeasurearXiv:2005.13166
  • Feature EngineeringonPEMS-SF
    L2 Loss (10^-4)
    4.58
    best: 3.54 (NAOMI)
  • Feature EngineeringonBasketball Players Movement
    OOB Rate (10^−3)
    0.128
    best: 4.703 (GRUI)
  • Feature EngineeringonBasketball Players Movement
    Path Difference
    0.746
    best: 0.571 (BRITS (SingleRes))
  • Feature EngineeringonBasketball Players Movement
    Path Length
    0.921
    best: 1.141 (GRUI)
  • Feature EngineeringonBasketball Players Movement
    Player Distance
    0.403
    best: 0.427 (MaskGAN)
  • Feature EngineeringonBasketball Players Movement
    Step Change (10^−3)
    13.24
    best: 14.95 (GRUI)

Time Series6 results

  • ImputationonPEMS-SF
    L2 Loss (10^-4)
    4.58
    best: 3.54 (NAOMI)
  • ImputationonBasketball Players Movement
    OOB Rate (10^−3)
    0.128
    best: 4.703 (GRUI)
  • ImputationonBasketball Players Movement
    Path Difference
    0.746
    best: 0.571 (BRITS (SingleRes))
  • ImputationonBasketball Players Movement
    Path Length
    0.921
    best: 1.141 (GRUI)
  • ImputationonBasketball Players Movement
    Player Distance
    0.403
    best: 0.427 (MaskGAN)
  • ImputationonBasketball Players Movement
    Step Change (10^−3)
    13.24
    best: 14.95 (GRUI)