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Models/GRU

GRU

Reported on 18 benchmarks across 11 tasks · 10 papers · 9 SOTA

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

Time Series10 results

Trajectory PredictiononVi-Fi Multi-modal Dataset
MSE-D· 2024-04-02
28.69
best: 13.42 (OOSTraj)
SOTA
OOSTraj: Out-of-Sight Trajectory Prediction With Vision-Positioning Denoising arXiv:2404.02227
Trajectory PredictiononVi-Fi Multi-modal Dataset
MSE-P· 2024-04-02
28.65
best: 13.83 (OOSTraj)
SOTA
OOSTraj: Out-of-Sight Trajectory Prediction With Vision-Positioning Denoising arXiv:2404.02227
Traffic PredictiononSZ-Taxi
MAE @ 15min· 2018-11-12
2.6814
best: 2.0198 (factorized ST-TGCN)
SOTA
T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction arXiv:1811.05320
Traffic PredictiononSZ-Taxi
MAE @ 30min· 2018-11-12
2.7009
best: 2.2951 (factorized ST-TGCN)
SOTA
T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction arXiv:1811.05320
Traffic PredictiononSZ-Taxi
MAE @ 45min· 2018-11-12
2.7207
best: 2.3689 (factorized ST-TGCN)
SOTA
T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction arXiv:1811.05320
Traffic PredictiononSZ-Taxi
MAE @ 60min· 2018-11-12
2.7431
best: 2.4476 (factorized ST-TGCN)
SOTA
T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction arXiv:1811.05320
Trajectory PredictiononVi-Fi Multi-modal Dataset
SUM· 2024-04-02
57.34
best: 200.9 (ViTag)
OOSTraj: Out-of-Sight Trajectory Prediction With Vision-Positioning Denoising arXiv:2404.02227
Time Series ClassificationonEigenWorms
% Test Accuracy· 2023-09-21
82.1
best: 92.3 (LEM)
Parallelizing non-linear sequential models over the sequence length arXiv:2309.12252
Time Series ClassificationonPhysioNet Challenge 2012
AUPRC· 2021-06-29
51.4
best: 55.1 (GRU-D - APC (n = 1))
As easy as APC: overcoming missing data and class imbalance in time series with self-supervised learning arXiv:2106.15577
Time Series AnalysisonPhysioNet Challenge 2012
F1· 2021-06-29
22.3
best: 87.47 (naive classifier)
As easy as APC: overcoming missing data and class imbalance in time series with self-supervised learning arXiv:2106.15577

Audio3 results

Emotion RecognitiononCREMA-D
Accuracy· 2020-01-13
55.01
best: 94.07 (Vertically long patch ViT)
SOTA
Visually Guided Self Supervised Learning of Speech Representations arXiv:2001.04316
Speech RecognitiononTIMIT
Percentage error· 2018-11-19
16.6
best: 8.3 (wav2vec 2.0)
The PyTorch-Kaldi Speech Recognition Toolkit arXiv:1811.07453
Lung Sound ClassificationonICBHI Respiratory Sound Database
Accurcay
95.7
best: 99.6 (RDLINet)

Speech2 results

Speech Emotion RecognitiononCREMA-D
Accuracy· 2020-01-13
55.01
best: 94.07 (Vertically long patch ViT)
SOTA
Visually Guided Self Supervised Learning of Speech Representations arXiv:2001.04316
Keyword SpottingonGoogle Speech Commands
Google Speech Commands V1 12· uses extra data· 2017-11-20
93.5
best: 98.56 (TripletLoss-res15)
Hello Edge: Keyword Spotting on Microcontrollers arXiv:1711.07128

Music2 results

Music ModelingonJSB Chorales
NLL· 2014-12-11
8.54
SOTA
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling arXiv:1412.3555
Music ModelingonNottingham
NLL· 2018-03-04
3.46
best: 4.05 (RNN)
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling arXiv:1803.01271

Medical1 result

Language ModellingonWikiText-103
Validation perplexity· 2020-05-17
53.78
best: 13.11 (Ensemble of All)
How much complexity does an RNN architecture need to learn syntax-sensitive dependencies?arXiv:2005.08199