HyperAI

Time Series Classification On Physionet

Metriken

AUPRC

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Vergleichstabelle
ModellnameAUPRC
as-easy-as-apc-leveraging-self-supervised53.3
as-easy-as-apc-leveraging-self-supervised53.7
as-easy-as-apc-leveraging-self-supervised55.1
set-functions-for-time-series-1-
latent-odes-for-irregularly-sampled-time-
set-functions-for-time-series-1-
latent-odes-for-irregularly-sampled-time-
as-easy-as-apc-leveraging-self-supervised50.4
as-easy-as-apc-leveraging-self-supervised-
set-functions-for-time-series-1-
set-functions-for-time-series-1-
as-easy-as-apc-leveraging-self-supervised-
set-functions-for-time-series-1-
multi-time-attention-networks-for-irregularly-1-
recurrent-neural-networks-for-multivariate-
as-easy-as-apc-leveraging-self-supervised52
latent-odes-for-irregularly-sampled-time-
as-easy-as-apc-leveraging-self-supervised53.8
interpolation-prediction-networks-for-1-
Modell 20-
set-functions-for-time-series-1-
as-easy-as-apc-leveraging-self-supervised50.3
as-easy-as-apc-leveraging-self-supervised51.4
as-easy-as-apc-leveraging-self-supervised53.5
multi-time-attention-networks-for-irregularly-1-
as-easy-as-apc-leveraging-self-supervised-
self-supervised-transformer-for-multivariate-
as-easy-as-apc-leveraging-self-supervised53.1