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Models/GP-VAE (B-NLST)

GP-VAE (B-NLST)

Reported on 10 benchmarks across 2 tasks · 1 paper · 10 SOTA

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

Time Series5 results

  • ImputationonSprites
    MSE· 2020-06-12
    0.002
    SOTA
    Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor ProjectionsarXiv:2006.07027
  • ImputationonHMNIST
    AUROC· 2020-06-12
    0.962
    SOTA
    Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor ProjectionsarXiv:2006.07027
  • ImputationonHMNIST
    MSE· 2020-06-12
    0.092
    SOTA
    Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor ProjectionsarXiv:2006.07027
  • ImputationonHMNIST
    NLL· 2020-06-12
    0.251
    SOTA
    Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor ProjectionsarXiv:2006.07027
  • ImputationonPhysioNet Challenge 2012
    AUROC· 2020-06-12
    0.743
    SOTA
    Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor ProjectionsarXiv:2006.07027

Methodology5 results

  • Feature EngineeringonSprites
    MSE· 2020-06-12
    0.002
    SOTA
    Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor ProjectionsarXiv:2006.07027
  • Feature EngineeringonHMNIST
    AUROC· 2020-06-12
    0.962
    SOTA
    Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor ProjectionsarXiv:2006.07027
  • Feature EngineeringonHMNIST
    MSE· 2020-06-12
    0.092
    SOTA
    Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor ProjectionsarXiv:2006.07027
  • Feature EngineeringonHMNIST
    NLL· 2020-06-12
    0.251
    SOTA
    Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor ProjectionsarXiv:2006.07027
  • Feature EngineeringonPhysioNet Challenge 2012
    AUROC· 2020-06-12
    0.743
    SOTA
    Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor ProjectionsarXiv:2006.07027