Time Series Classification On Kickvspunch
Métriques
Accuracy
NLL
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | Accuracy | NLL | Paper Title | Repository |
|---|---|---|---|---|
| GP-Sig-GRU | 0.820 | 0.493 | Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances | - |
| GP-LSTM | 0.620 | 0.696 | Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances | - |
| MALSTM-FCN | 1 | - | Multivariate LSTM-FCNs for Time Series Classification | - |
| SNLST | 1 | - | Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor Projections | - |
| GP-KConv1D | 0.700 | 0.662 | Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances | - |
| FCN-SNLST | 1 | - | Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor Projections | - |
| GP-GRU | 0.600 | 0.674 | Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances | - |
| GP-Sig | 0.900 | 0.224 | Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances | - |
| GP-Sig-LSTM | 0.900 | 0.301 | Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances | - |
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