Time Series Classification On Cmusubject16
Métriques
Accuracy
NLL
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | Accuracy | NLL | Paper Title | Repository |
|---|---|---|---|---|
| GP-Sig-LSTM | 1.000 | 0.088 | Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances | - |
| MALSTM-FCN | 1 | - | Multivariate LSTM-FCNs for Time Series Classification | - |
| GP-GRU | 0.993 | 0.089 | Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances | - |
| GP-Sig | 0.979 | 0.089 | 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-Sig-GRU | 1.000 | 0.040 | Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances | - |
| GP-LSTM | 0.924 | 0.270 | Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances | - |
| SNLST | 1 | - | Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor Projections | - |
| GP-KConv1D | 0.897 | 0.255 | Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances | - |
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