task_path
stringlengths 3
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⌀ | dataset
stringlengths 1
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⌀ | model_name
stringlengths 1
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⌀ | paper_url
stringlengths 21
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⌀ | metric_name
stringlengths 1
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⌀ | metric_value
stringlengths 1
9.22k
⌀ |
|---|---|---|---|---|---|
Explanatory Visual Question Answering > FS-MEVQA
|
SME
|
GPT-4-1106-Vision-Preview
|
https://arxiv.org/abs/2303.08774v5
|
SPICE
|
37.67
|
Explanatory Visual Question Answering > FS-MEVQA
|
SME
|
GPT-4-1106-Vision-Preview
|
https://arxiv.org/abs/2303.08774v5
|
Detection
|
7.00
|
Explanatory Visual Question Answering > FS-MEVQA
|
SME
|
GPT-4-1106-Vision-Preview
|
https://arxiv.org/abs/2303.08774v5
|
ACC
|
42.30
|
Explanatory Visual Question Answering > FS-MEVQA
|
SME
|
GPT-4-1106-Vision-Preview
|
https://arxiv.org/abs/2303.08774v5
|
#Learning Samples (N)
|
16
|
Explanatory Visual Question Answering > FS-MEVQA
|
SME
|
Gemini-1.5 Pro
|
https://arxiv.org/abs/2403.05530v5
|
BLEU-4
|
41.87
|
Explanatory Visual Question Answering > FS-MEVQA
|
SME
|
Gemini-1.5 Pro
|
https://arxiv.org/abs/2403.05530v5
|
METEOR
|
34.61
|
Explanatory Visual Question Answering > FS-MEVQA
|
SME
|
Gemini-1.5 Pro
|
https://arxiv.org/abs/2403.05530v5
|
ROUGE-L
|
55.90
|
Explanatory Visual Question Answering > FS-MEVQA
|
SME
|
Gemini-1.5 Pro
|
https://arxiv.org/abs/2403.05530v5
|
CIDEr
|
276.14
|
Explanatory Visual Question Answering > FS-MEVQA
|
SME
|
Gemini-1.5 Pro
|
https://arxiv.org/abs/2403.05530v5
|
SPICE
|
40.58
|
Explanatory Visual Question Answering > FS-MEVQA
|
SME
|
Gemini-1.5 Pro
|
https://arxiv.org/abs/2403.05530v5
|
Detection
|
1.40
|
Explanatory Visual Question Answering > FS-MEVQA
|
SME
|
Gemini-1.5 Pro
|
https://arxiv.org/abs/2403.05530v5
|
ACC
|
40.88
|
Explanatory Visual Question Answering > FS-MEVQA
|
SME
|
Gemini-1.5 Pro
|
https://arxiv.org/abs/2403.05530v5
|
#Learning Samples (N)
|
16
|
Explanatory Visual Question Answering > FS-MEVQA
|
SME
|
Qwen-VL-Max
|
https://arxiv.org/abs/2308.12966v3
|
BLEU-4
|
24.30
|
Explanatory Visual Question Answering > FS-MEVQA
|
SME
|
Qwen-VL-Max
|
https://arxiv.org/abs/2308.12966v3
|
METEOR
|
23.40
|
Explanatory Visual Question Answering > FS-MEVQA
|
SME
|
Qwen-VL-Max
|
https://arxiv.org/abs/2308.12966v3
|
ROUGE-L
|
34.52
|
Explanatory Visual Question Answering > FS-MEVQA
|
SME
|
Qwen-VL-Max
|
https://arxiv.org/abs/2308.12966v3
|
CIDEr
|
201.47
|
Explanatory Visual Question Answering > FS-MEVQA
|
SME
|
Qwen-VL-Max
|
https://arxiv.org/abs/2308.12966v3
|
SPICE
|
26.13
|
Explanatory Visual Question Answering > FS-MEVQA
|
SME
|
Qwen-VL-Max
|
https://arxiv.org/abs/2308.12966v3
|
Detection
|
1.05
|
Explanatory Visual Question Answering > FS-MEVQA
|
SME
|
Qwen-VL-Max
|
https://arxiv.org/abs/2308.12966v3
|
ACC
|
40.33
|
Explanatory Visual Question Answering > FS-MEVQA
|
SME
|
Qwen-VL-Max
|
https://arxiv.org/abs/2308.12966v3
|
#Learning Samples (N)
|
16
|
Explanatory Visual Question Answering > FS-MEVQA
|
SME
|
GLM-4V
|
https://arxiv.org/abs/2311.03079v2
|
BLEU-4
|
14.45
|
Explanatory Visual Question Answering > FS-MEVQA
|
SME
|
GLM-4V
|
https://arxiv.org/abs/2311.03079v2
|
METEOR
|
17.53
|
Explanatory Visual Question Answering > FS-MEVQA
|
SME
|
GLM-4V
|
https://arxiv.org/abs/2311.03079v2
|
ROUGE-L
|
24.28
|
Explanatory Visual Question Answering > FS-MEVQA
|
SME
|
GLM-4V
|
https://arxiv.org/abs/2311.03079v2
|
CIDEr
|
127.37
|
Explanatory Visual Question Answering > FS-MEVQA
|
SME
|
GLM-4V
|
https://arxiv.org/abs/2311.03079v2
|
SPICE
|
17.70
|
Explanatory Visual Question Answering > FS-MEVQA
|
SME
|
GLM-4V
|
https://arxiv.org/abs/2311.03079v2
|
Detection
|
0.89
|
Explanatory Visual Question Answering > FS-MEVQA
|
SME
|
GLM-4V
|
https://arxiv.org/abs/2311.03079v2
|
ACC
|
34.23
|
Explanatory Visual Question Answering > FS-MEVQA
|
SME
|
GLM-4V
|
https://arxiv.org/abs/2311.03079v2
|
#Learning Samples (N)
|
16
|
Explanatory Visual Question Answering > FS-MEVQA
|
SME
|
VCIN
|
http://openaccess.thecvf.com//content/ICCV2023/html/Xue_Variational_Causal_Inference_Network_for_Explanatory_Visual_Question_Answering_ICCV_2023_paper.html
|
BLEU-4
|
9.17
|
Explanatory Visual Question Answering > FS-MEVQA
|
SME
|
VCIN
|
http://openaccess.thecvf.com//content/ICCV2023/html/Xue_Variational_Causal_Inference_Network_for_Explanatory_Visual_Question_Answering_ICCV_2023_paper.html
|
METEOR
|
19.82
|
Explanatory Visual Question Answering > FS-MEVQA
|
SME
|
VCIN
|
http://openaccess.thecvf.com//content/ICCV2023/html/Xue_Variational_Causal_Inference_Network_for_Explanatory_Visual_Question_Answering_ICCV_2023_paper.html
|
ROUGE-L
|
33.34
|
Explanatory Visual Question Answering > FS-MEVQA
|
SME
|
VCIN
|
http://openaccess.thecvf.com//content/ICCV2023/html/Xue_Variational_Causal_Inference_Network_for_Explanatory_Visual_Question_Answering_ICCV_2023_paper.html
|
CIDEr
|
4.28
|
Explanatory Visual Question Answering > FS-MEVQA
|
SME
|
VCIN
|
http://openaccess.thecvf.com//content/ICCV2023/html/Xue_Variational_Causal_Inference_Network_for_Explanatory_Visual_Question_Answering_ICCV_2023_paper.html
|
SPICE
|
13.39
|
Explanatory Visual Question Answering > FS-MEVQA
|
SME
|
VCIN
|
http://openaccess.thecvf.com//content/ICCV2023/html/Xue_Variational_Causal_Inference_Network_for_Explanatory_Visual_Question_Answering_ICCV_2023_paper.html
|
Detection
|
0.28
|
Explanatory Visual Question Answering > FS-MEVQA
|
SME
|
VCIN
|
http://openaccess.thecvf.com//content/ICCV2023/html/Xue_Variational_Causal_Inference_Network_for_Explanatory_Visual_Question_Answering_ICCV_2023_paper.html
|
ACC
|
17.77
|
Explanatory Visual Question Answering > FS-MEVQA
|
SME
|
VCIN
|
http://openaccess.thecvf.com//content/ICCV2023/html/Xue_Variational_Causal_Inference_Network_for_Explanatory_Visual_Question_Answering_ICCV_2023_paper.html
|
#Learning Samples (N)
|
16
|
Explanatory Visual Question Answering > FS-MEVQA
|
SME
|
REX
|
https://arxiv.org/abs/2203.06107v1
|
BLEU-4
|
0.00
|
Explanatory Visual Question Answering > FS-MEVQA
|
SME
|
REX
|
https://arxiv.org/abs/2203.06107v1
|
METEOR
|
4.37
|
Explanatory Visual Question Answering > FS-MEVQA
|
SME
|
REX
|
https://arxiv.org/abs/2203.06107v1
|
ROUGE-L
|
23.23
|
Explanatory Visual Question Answering > FS-MEVQA
|
SME
|
REX
|
https://arxiv.org/abs/2203.06107v1
|
CIDEr
|
0.89
|
Explanatory Visual Question Answering > FS-MEVQA
|
SME
|
REX
|
https://arxiv.org/abs/2203.06107v1
|
SPICE
|
0.00
|
Explanatory Visual Question Answering > FS-MEVQA
|
SME
|
REX
|
https://arxiv.org/abs/2203.06107v1
|
Detection
|
0.00
|
Explanatory Visual Question Answering > FS-MEVQA
|
SME
|
REX
|
https://arxiv.org/abs/2203.06107v1
|
ACC
|
17.77
|
Explanatory Visual Question Answering > FS-MEVQA
|
SME
|
REX
|
https://arxiv.org/abs/2203.06107v1
|
#Learning Samples (N)
|
16
|
Multivariate Time Series Forecasting
|
MIMIC-III
|
GraFITi
|
https://arxiv.org/abs/2305.12932v2
|
MSE
|
0.396 ± 0.030
|
Multivariate Time Series Forecasting
|
MIMIC-III
|
FLD
|
https://arxiv.org/abs/2405.03582v2
|
MSE
|
0.444 ± 0.027
|
Multivariate Time Series Forecasting
|
MIMIC-III
|
GRU-ODE-Bayes
|
https://arxiv.org/abs/1905.12374v2
|
MSE
|
0.480 ± 0.010
|
Multivariate Time Series Forecasting
|
MIMIC-III
|
GRU-ODE-Bayes
|
https://arxiv.org/abs/1905.12374v2
|
NegLL
|
0.83
|
Multivariate Time Series Forecasting
|
MIMIC-III
|
Neural Flows
|
https://arxiv.org/abs/2110.13040v1
|
MSE
|
0.490 ± 0.004
|
Multivariate Time Series Forecasting
|
MIMIC-III
|
T-LSTM
|
https://doi.org/10.1145/3097983.3097997
|
MSE
|
0.790 ± 0.060
|
Multivariate Time Series Forecasting
|
MIMIC-III
|
T-LSTM
|
https://doi.org/10.1145/3097983.3097997
|
NegLL
|
1.02
|
Multivariate Time Series Forecasting
|
Weather
|
GLinear
|
https://arxiv.org/abs/2501.01087v3
|
MSE
|
0.0716
|
Multivariate Time Series Forecasting
|
ExtMarker
|
UORO
|
https://arxiv.org/abs/2106.01100v6
|
MAE
|
0.845
|
Multivariate Time Series Forecasting
|
ExtMarker
|
UORO
|
https://arxiv.org/abs/2106.01100v6
|
RMSE
|
1.275
|
Multivariate Time Series Forecasting
|
ExtMarker
|
UORO
|
https://arxiv.org/abs/2106.01100v6
|
normalized RMSE
|
0.2824
|
Multivariate Time Series Forecasting
|
ExtMarker
|
UORO
|
https://arxiv.org/abs/2106.01100v6
|
Maximum error
|
8.81
|
Multivariate Time Series Forecasting
|
ExtMarker
|
UORO
|
https://arxiv.org/abs/2106.01100v6
|
Jitter
|
0.9672
|
Multivariate Time Series Forecasting
|
Traffic
|
GLinear
|
https://arxiv.org/abs/2501.01087v3
|
MSE
|
0.3222
|
Multivariate Time Series Forecasting
|
ETTh1 (720) Multivariate
|
TSMixer
|
https://arxiv.org/abs/2306.09364v4
|
MSE
|
0.444
|
Multivariate Time Series Forecasting
|
MuJoCo
|
Latent ODE (ODE enc)
|
https://arxiv.org/abs/1907.03907v1
|
MSE (10^-2, 50% missing)
|
1.258
|
Multivariate Time Series Forecasting
|
MuJoCo
|
Latent ODE (RNN enc.)
|
https://arxiv.org/abs/1806.07366v5
|
MSE (10^-2, 50% missing)
|
1.377
|
Multivariate Time Series Forecasting
|
MuJoCo
|
RNN-VAE
|
https://arxiv.org/abs/1806.07366v5
|
MSE (10^-2, 50% missing)
|
1.782
|
Multivariate Time Series Forecasting
|
MuJoCo
|
RNN GRU-D
|
http://arxiv.org/abs/1606.01865v2
|
MSE (10^-2, 50% missing)
|
5.833
|
Multivariate Time Series Forecasting
|
MuJoCo
|
ODE-RNN
|
https://arxiv.org/abs/1907.03907v1
|
MSE (10^-2, 50% missing)
|
26.463
|
Multivariate Time Series Forecasting
|
MuJoCo
|
RNN ∆t
| null |
MSE (10^-2, 50% missing)
|
30.571
|
Multivariate Time Series Forecasting
|
ETTh1 (96) Multivariate
|
TSMixer
|
https://arxiv.org/abs/2306.09364v4
|
MSE
|
0.368
|
Multivariate Time Series Forecasting
|
ETTh1 (96) Multivariate
|
TSMixer
|
https://arxiv.org/abs/2306.09364v4
|
MAE
|
0.398
|
Multivariate Time Series Forecasting
|
ETTh1 (96) Multivariate
|
PRformer
|
https://arxiv.org/abs/2408.10483v1
|
MSE
|
0.354
|
Multivariate Time Series Forecasting
|
ETTh1 (96) Multivariate
|
PRformer
|
https://arxiv.org/abs/2408.10483v1
|
MAE
|
0.383
|
Multivariate Time Series Forecasting
|
ETTh1 (96) Multivariate
|
MMFNet
|
https://arxiv.org/abs/2410.02070v1
|
MSE
|
0.359
|
Multivariate Time Series Forecasting
|
USHCN-Daily
|
FLD
|
https://arxiv.org/abs/2405.03582v2
|
MSE
|
0.258
|
Multivariate Time Series Forecasting
|
USHCN-Daily
|
GraFITi
|
https://arxiv.org/abs/2305.12932v2
|
MSE
|
0.27
|
Multivariate Time Series Forecasting
|
USHCN-Daily
|
GRU-ODE-Bayes
|
https://arxiv.org/abs/1905.12374v2
|
MSE
|
0.43
|
Multivariate Time Series Forecasting
|
USHCN-Daily
|
BRITS
|
http://arxiv.org/abs/1805.10572v1
|
MSE
|
0.53
|
Multivariate Time Series Forecasting
|
USHCN-Daily
|
T-LSTM
|
https://doi.org/10.1145/3097983.3097997
|
MSE
|
0.59
|
Multivariate Time Series Forecasting
|
USHCN-Daily
|
Sequential VAE
|
http://arxiv.org/abs/1609.09869v2
|
MSE
|
0.83
|
Multivariate Time Series Forecasting
|
USHCN-Daily
|
NeuralODE-VAE-Mask
|
https://arxiv.org/abs/1806.07366v5
|
MSE
|
0.83
|
Multivariate Time Series Forecasting
|
USHCN-Daily
|
NeuralODE-VAE
|
https://arxiv.org/abs/1806.07366v5
|
MSE
|
0.96
|
Multivariate Time Series Forecasting
|
ETTh2 (720) Multivariate
|
MMFNet
|
https://arxiv.org/abs/2410.02070v1
|
MSE
|
0.376
|
Multivariate Time Series Forecasting
|
PhysioNet Challenge 2012
|
Latent ODE + Poisson
|
https://arxiv.org/abs/1907.03907v1
|
mse (10^-3)
|
2.208
|
Multivariate Time Series Forecasting
|
PhysioNet Challenge 2012
|
Latent ODE + Poisson
|
https://arxiv.org/abs/1907.03907v1
|
MSE stdev
|
0.05
|
Multivariate Time Series Forecasting
|
PhysioNet Challenge 2012
|
Latent ODE (ODE enc)
|
https://arxiv.org/abs/1907.03907v1
|
mse (10^-3)
|
2.231
|
Multivariate Time Series Forecasting
|
PhysioNet Challenge 2012
|
Latent ODE (ODE enc)
|
https://arxiv.org/abs/1907.03907v1
|
MSE stdev
|
0.029
|
Multivariate Time Series Forecasting
|
PhysioNet Challenge 2012
|
RNN-VAE
|
https://arxiv.org/abs/1806.07366v5
|
mse (10^-3)
|
3.055
|
Multivariate Time Series Forecasting
|
PhysioNet Challenge 2012
|
RNN-VAE
|
https://arxiv.org/abs/1806.07366v5
|
MSE stdev
|
0.145
|
Multivariate Time Series Forecasting
|
PhysioNet Challenge 2012
|
Latent ODE (RNN enc.)
|
https://arxiv.org/abs/1806.07366v5
|
mse (10^-3)
|
3.162
|
Multivariate Time Series Forecasting
|
PhysioNet Challenge 2012
|
Latent ODE (RNN enc.)
|
https://arxiv.org/abs/1806.07366v5
|
MSE stdev
|
0.052
|
Multivariate Time Series Forecasting
|
AEP
|
LSTM-SC
|
https://peerj.com/articles/cs-1487/
|
12 steps MAPE
|
2.58
|
Multivariate Time Series Forecasting
|
AEP
|
LSTM-SC
|
https://peerj.com/articles/cs-1487/
|
12 steps RMSE
|
549.92
|
Multivariate Time Series Forecasting
|
ETTh1 (192) Multivariate
|
TSMixer
|
https://arxiv.org/abs/2306.09364v4
|
MSE
|
0.399
|
Multivariate Time Series Forecasting
|
ETTh1 (192) Multivariate
|
TSMixer
|
https://arxiv.org/abs/2306.09364v4
|
MAE
|
0.418
|
Multivariate Time Series Forecasting
|
ETTh1 (192) Multivariate
|
PRformer
|
https://arxiv.org/abs/2408.10483v1
|
MSE
|
0.397
|
Multivariate Time Series Forecasting
|
ETTh1 (192) Multivariate
|
PRformer
|
https://arxiv.org/abs/2408.10483v1
|
MAE
|
0.410
|
Multivariate Time Series Forecasting
|
ETTh1 (192) Multivariate
|
MMFNet
|
https://arxiv.org/abs/2410.02070v1
|
MSE
|
0.396
|
Multivariate Time Series Forecasting
|
ETTh1 (336) Multivariate
|
MMFNet
|
https://arxiv.org/abs/2410.02070v1
|
MSE
|
0.409
|
Multivariate Time Series Forecasting
|
ETTh1 (336) Multivariate
|
TSMixer
|
https://arxiv.org/abs/2306.09364v4
|
MSE
|
0.421
|
Multivariate Time Series Forecasting
|
ETTh1 (48) Multivariate
|
GLinear
|
https://arxiv.org/abs/2501.01087v3
|
MSE
|
0.3142
|
Multivariate Time Series Forecasting
|
BPI challenge '12
|
QuerySelector
|
https://arxiv.org/abs/2107.08687v2
|
Accuracy
|
0.79
|
Multivariate Time Series Forecasting
|
BPI challenge '12
|
LSTM
|
http://arxiv.org/abs/1612.02130v2
|
Accuracy
|
0.76
|
Multivariate Time Series Forecasting
|
Electricity
|
GLinear
|
https://arxiv.org/abs/2501.01087v3
|
MSE
|
0.0883
|
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