Time Series Classification On Auslan

Metrics

Accuracy

NLL

Results

Performance results of various models on this benchmark

			Paper Title
FCN-SNLST	0.993	-	Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor Projections
GP-Sig-LSTM	0.983	0.106	Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances
GP-Sig-GRU	0.978	0.123	Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances
SNLST	0.969	-	Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor Projections
MALSTM-FCN	0.96	-	Multivariate LSTM-FCNs for Time Series Classification
GP-GRU	0.949	0.248	Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances
GP-Sig	0.925	0.550	Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances
GP-LSTM	0.880	0.650	Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances
GP-KConv1D	0.784	1.900	Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances

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Time Series Classification On Auslan | SOTA | HyperAI