task_path stringlengths 3 199 ⌀ | dataset stringlengths 1 128 ⌀ | model_name stringlengths 1 223 ⌀ | paper_url stringlengths 21 601 ⌀ | metric_name stringlengths 1 50 ⌀ | metric_value stringlengths 1 9.22k ⌀ |
|---|---|---|---|---|---|
Imputation | HMNIST | GP-VAE (B-NLST) | https://arxiv.org/abs/2006.07027v2 | MSE | 0.092 |
Imputation | HMNIST | GP-VAE (B-NLST) | https://arxiv.org/abs/2006.07027v2 | AUROC | 0.962 |
Imputation | PhysioNet Challenge 2012 | GP-VAE (B-NLST) | https://arxiv.org/abs/2006.07027v2 | AUROC | 0.743 |
Imputation > Multivariate Time Series Imputation | METR-LA | COSTI | https://arxiv.org/abs/2501.19364v1 | 1 step MAE | 1.76 |
Imputation > Multivariate Time Series Imputation | UCI localization data | BRITS | http://arxiv.org/abs/1805.10572v1 | MAE (10% missing) | 0.219 |
Imputation > Multivariate Time Series Imputation | UCI localization data | M-RNN | http://arxiv.org/abs/1711.08742v1 | MAE (10% missing) | 0.248 |
Imputation > Multivariate Time Series Imputation | UCI localization data | ImputeTS | http://doi.org/10.32614/RJ-2017-009 | MAE (10% missing) | 0.363 |
Imputation > Multivariate Time Series Imputation | UCI localization data | MICE | https://doi.org/10.1002/sim.4067 | MAE (10% missing) | 0.477 |
Imputation > Multivariate Time Series Imputation | UCI localization data | Matrix Factorization (MF) | null | MAE (10% missing) | 0.879 |
Imputation > Multivariate Time Series Imputation | Electricity | SAITS | https://arxiv.org/abs/2202.08516v5 | MAE (100 steps, 10% data missing) | 0.735 |
Imputation > Multivariate Time Series Imputation | PhysioNet Challenge 2012 | SAITS | https://arxiv.org/abs/2202.08516v5 | MAE (10% of data as GT) | 0.186 |
Imputation > Multivariate Time Series Imputation | PhysioNet Challenge 2012 | BRITS | http://arxiv.org/abs/1805.10572v1 | MAE (10% of data as GT) | 0.281 |
Imputation > Multivariate Time Series Imputation | PhysioNet Challenge 2012 | ImputeTS | http://doi.org/10.32614/RJ-2017-009 | MAE (10% of data as GT) | 0.390 |
Imputation > Multivariate Time Series Imputation | PhysioNet Challenge 2012 | M-RNN | http://arxiv.org/abs/1711.08742v1 | MAE (10% of data as GT) | 0.451 |
Imputation > Multivariate Time Series Imputation | PhysioNet Challenge 2012 | MICE | https://doi.org/10.1002/sim.4067 | MAE (10% of data as GT) | 0.634 |
Imputation > Multivariate Time Series Imputation | PhysioNet Challenge 2012 | Latent ODE (ODE enc) | https://arxiv.org/abs/1907.03907v1 | mse (10^-3) | 2.118 |
Imputation > Multivariate Time Series Imputation | PhysioNet Challenge 2012 | Latent ODE + Poisson | https://arxiv.org/abs/1907.03907v1 | mse (10^-3) | 2.789 |
Imputation > Multivariate Time Series Imputation | PhysioNet Challenge 2012 | Latent ODE (RNN enc.) | https://arxiv.org/abs/1806.07366v5 | mse (10^-3) | 3.907 |
Imputation > Multivariate Time Series Imputation | PhysioNet Challenge 2012 | RNN-VAE | https://arxiv.org/abs/1806.07366v5 | mse (10^-3) | 5.930 |
Imputation > Multivariate Time Series Imputation | PEMS-SF | NAOMI | https://arxiv.org/abs/1901.10946v3 | L2 Loss (10^-4) | 3.54 |
Imputation > Multivariate Time Series Imputation | PEMS-SF | BRITS (SingleRes) | http://arxiv.org/abs/1805.10572v1 | L2 Loss (10^-4) | 4.51 |
Imputation > Multivariate Time Series Imputation | PEMS-SF | KNN | null | L2 Loss (10^-4) | 4.58 |
Imputation > Multivariate Time Series Imputation | PEMS-SF | MaskGAN | https://openreview.net/forum?id=ByOExmWAb | L2 Loss (10^-4) | 6.02 |
Imputation > Multivariate Time Series Imputation | PEMS-SF | GRUI | http://papers.nips.cc/paper/7432-multivariate-time-series-imputation-with-generative-adversarial-networks | L2 Loss (10^-4) | 15.24 |
Imputation > Multivariate Time Series Imputation | Basketball Players Movement | NAOMI | https://arxiv.org/abs/1901.10946v3 | Path Length | 0.573 |
Imputation > Multivariate Time Series Imputation | Basketball Players Movement | NAOMI | https://arxiv.org/abs/1901.10946v3 | OOB Rate (10^−3) | 1.733 |
Imputation > Multivariate Time Series Imputation | Basketball Players Movement | NAOMI | https://arxiv.org/abs/1901.10946v3 | Step Change (10^−3) | 2.565 |
Imputation > Multivariate Time Series Imputation | Basketball Players Movement | NAOMI | https://arxiv.org/abs/1901.10946v3 | Path Difference | 0.581 |
Imputation > Multivariate Time Series Imputation | Basketball Players Movement | NAOMI | https://arxiv.org/abs/1901.10946v3 | Player Distance | 0.423 |
Imputation > Multivariate Time Series Imputation | Basketball Players Movement | BRITS (SingleRes) | http://arxiv.org/abs/1805.10572v1 | Path Length | 0.702 |
Imputation > Multivariate Time Series Imputation | Basketball Players Movement | BRITS (SingleRes) | http://arxiv.org/abs/1805.10572v1 | OOB Rate (10^−3) | 3.874 |
Imputation > Multivariate Time Series Imputation | Basketball Players Movement | BRITS (SingleRes) | http://arxiv.org/abs/1805.10572v1 | Step Change (10^−3) | 4.811 |
Imputation > Multivariate Time Series Imputation | Basketball Players Movement | BRITS (SingleRes) | http://arxiv.org/abs/1805.10572v1 | Path Difference | 0.571 |
Imputation > Multivariate Time Series Imputation | Basketball Players Movement | BRITS (SingleRes) | http://arxiv.org/abs/1805.10572v1 | Player Distance | 0.417 |
Imputation > Multivariate Time Series Imputation | Basketball Players Movement | MaskGAN | http://arxiv.org/abs/1801.07736v3 | Path Length | 0.793 |
Imputation > Multivariate Time Series Imputation | Basketball Players Movement | MaskGAN | http://arxiv.org/abs/1801.07736v3 | OOB Rate (10^−3) | 4.592 |
Imputation > Multivariate Time Series Imputation | Basketball Players Movement | MaskGAN | http://arxiv.org/abs/1801.07736v3 | Step Change (10^−3) | 9.622 |
Imputation > Multivariate Time Series Imputation | Basketball Players Movement | MaskGAN | http://arxiv.org/abs/1801.07736v3 | Path Difference | 0.680 |
Imputation > Multivariate Time Series Imputation | Basketball Players Movement | MaskGAN | http://arxiv.org/abs/1801.07736v3 | Player Distance | 0.427 |
Imputation > Multivariate Time Series Imputation | Basketball Players Movement | KNN | null | Path Length | 0.921 |
Imputation > Multivariate Time Series Imputation | Basketball Players Movement | KNN | null | OOB Rate (10^−3) | 0.128 |
Imputation > Multivariate Time Series Imputation | Basketball Players Movement | KNN | null | Step Change (10^−3) | 13.24 |
Imputation > Multivariate Time Series Imputation | Basketball Players Movement | KNN | null | Path Difference | 0.746 |
Imputation > Multivariate Time Series Imputation | Basketball Players Movement | KNN | null | Player Distance | 0.403 |
Imputation > Multivariate Time Series Imputation | Basketball Players Movement | GRUI | http://papers.nips.cc/paper/7432-multivariate-time-series-imputation-with-generative-adversarial-networks | Path Length | 1.141 |
Imputation > Multivariate Time Series Imputation | Basketball Players Movement | GRUI | http://papers.nips.cc/paper/7432-multivariate-time-series-imputation-with-generative-adversarial-networks | OOB Rate (10^−3) | 4.703 |
Imputation > Multivariate Time Series Imputation | Basketball Players Movement | GRUI | http://papers.nips.cc/paper/7432-multivariate-time-series-imputation-with-generative-adversarial-networks | Step Change (10^−3) | 14.95 |
Imputation > Multivariate Time Series Imputation | Basketball Players Movement | GRUI | http://papers.nips.cc/paper/7432-multivariate-time-series-imputation-with-generative-adversarial-networks | Path Difference | 0.690 |
Imputation > Multivariate Time Series Imputation | Basketball Players Movement | GRUI | http://papers.nips.cc/paper/7432-multivariate-time-series-imputation-with-generative-adversarial-networks | Player Distance | 0.398 |
Imputation > Multivariate Time Series Imputation | Beijing Multi-Site Air-Quality Dataset | GRIN | https://arxiv.org/abs/2108.00298v3 | MAE (PM2.5) | 10.51 |
Imputation > Multivariate Time Series Imputation | Beijing Multi-Site Air-Quality Dataset | BRITS | http://arxiv.org/abs/1805.10572v1 | MAE (PM2.5) | 11.56 |
Imputation > Multivariate Time Series Imputation | Beijing Multi-Site Air-Quality Dataset | STMVL | https://www.microsoft.com/en-us/research/publication/st-mvl-filling-missing-values-in-geo-sensory-time-series-data/ | MAE (PM2.5) | 12.12 |
Imputation > Multivariate Time Series Imputation | Beijing Multi-Site Air-Quality Dataset | M-RNN | http://arxiv.org/abs/1711.08742v1 | MAE (PM2.5) | 14.24 |
Imputation > Multivariate Time Series Imputation | Beijing Multi-Site Air-Quality Dataset | ImputeTS | http://doi.org/10.32614/RJ-2017-009 | MAE (PM2.5) | 19.58 |
Imputation > Multivariate Time Series Imputation | Beijing Multi-Site Air-Quality Dataset | MICE | https://doi.org/10.1002/sim.4067 | MAE (PM2.5) | 27.42 |
Imputation > Multivariate Time Series Imputation | MuJoCo | Latent ODE (ODE enc) | https://arxiv.org/abs/1907.03907v1 | MSE (10^2, 50% missing) | 0.285 |
Imputation > Multivariate Time Series Imputation | MuJoCo | Latent ODE (RNN enc.) | https://arxiv.org/abs/1806.07366v5 | MSE (10^2, 50% missing) | 0.447 |
Imputation > Multivariate Time Series Imputation | MuJoCo | ODE-RNN | https://arxiv.org/abs/1907.03907v1 | MSE (10^2, 50% missing) | 0.665 |
Imputation > Multivariate Time Series Imputation | MuJoCo | RNN GRU-D | http://arxiv.org/abs/1606.01865v2 | MSE (10^2, 50% missing) | 0.748 |
Imputation > Multivariate Time Series Imputation | MuJoCo | RNN ∆t | null | MSE (10^2, 50% missing) | 0.785 |
Imputation > Multivariate Time Series Imputation | MuJoCo | RNN-VAE | https://arxiv.org/abs/1806.07366v5 | MSE (10^2, 50% missing) | 6.100 |
Imputation > Multivariate Time Series Imputation | KDD CUP Challenge 2018 | E^2GAN | https://doi.org/10.24963/ijcai.2019/429 | MSE (10% missing) | 0.334 |
Imputation > Multivariate Time Series Imputation | KDD CUP Challenge 2018 | GAN-2-stage | http://papers.nips.cc/paper/7432-multivariate-time-series-imputation-with-generative-adversarial-networks | MSE (10% missing) | 0.355 |
Imputation > Multivariate Time Series Imputation | KDD CUP Challenge 2018 | GAIN | http://arxiv.org/abs/1806.02920v1 | MSE (10% missing) | 0.378 |
Imputation > Multivariate Time Series Imputation | KDD CUP Challenge 2018 | MICE | https://doi.org/10.1002/sim.4067 | MSE (10% missing) | 0.468 |
Video Quality Assessment | LIVE-YT-HFR | ST-GREED | https://arxiv.org/abs/2010.13715v2 | SRCC | 0.8822 |
Video Quality Assessment | LIVE-YT-HFR | GREED-VMAF | https://arxiv.org/abs/2109.12785v1 | SRCC | 0.8658 |
Video Quality Assessment | LIVE-YT-HFR | GSTI | https://arxiv.org/abs/2006.11424v2 | SRCC | 0.8064 |
Video Quality Assessment | MSU NR VQA Database | MDTVSFA | https://arxiv.org/abs/2011.04263v2 | SRCC | 0.9289 |
Video Quality Assessment | MSU NR VQA Database | MDTVSFA | https://arxiv.org/abs/2011.04263v2 | PLCC | 0.9431 |
Video Quality Assessment | MSU NR VQA Database | MDTVSFA | https://arxiv.org/abs/2011.04263v2 | KLCC | 0.7883 |
Video Quality Assessment | MSU NR VQA Database | MDTVSFA | https://arxiv.org/abs/2011.04263v2 | Type | NR |
Video Quality Assessment | MSU NR VQA Database | DBCNN | https://arxiv.org/abs/1907.02665v1 | SRCC | 0.9220 |
Video Quality Assessment | MSU NR VQA Database | DBCNN | https://arxiv.org/abs/1907.02665v1 | PLCC | 0.9222 |
Video Quality Assessment | MSU NR VQA Database | DBCNN | https://arxiv.org/abs/1907.02665v1 | KLCC | 0.7750 |
Video Quality Assessment | MSU NR VQA Database | DBCNN | https://arxiv.org/abs/1907.02665v1 | Type | NR |
Video Quality Assessment | MSU NR VQA Database | UNIQUE | http://arxiv.org/abs/1810.06631v2 | SRCC | 0.9148 |
Video Quality Assessment | MSU NR VQA Database | UNIQUE | http://arxiv.org/abs/1810.06631v2 | PLCC | 0.9238 |
Video Quality Assessment | MSU NR VQA Database | UNIQUE | http://arxiv.org/abs/1810.06631v2 | KLCC | 0.7648 |
Video Quality Assessment | MSU NR VQA Database | UNIQUE | http://arxiv.org/abs/1810.06631v2 | Type | NR |
Video Quality Assessment | MSU NR VQA Database | LI | https://arxiv.org/abs/2108.08505v2 | SRCC | 0.9131 |
Video Quality Assessment | MSU NR VQA Database | LI | https://arxiv.org/abs/2108.08505v2 | PLCC | 0.9270 |
Video Quality Assessment | MSU NR VQA Database | LI | https://arxiv.org/abs/2108.08505v2 | KLCC | 0.7640 |
Video Quality Assessment | MSU NR VQA Database | LI | https://arxiv.org/abs/2108.08505v2 | Type | NR |
Video Quality Assessment | MSU NR VQA Database | LINEARITY | https://arxiv.org/abs/2008.03889v1 | SRCC | 0.9104 |
Video Quality Assessment | MSU NR VQA Database | LINEARITY | https://arxiv.org/abs/2008.03889v1 | PLCC | 0.9106 |
Video Quality Assessment | MSU NR VQA Database | LINEARITY | https://arxiv.org/abs/2008.03889v1 | KLCC | 0.7589 |
Video Quality Assessment | MSU NR VQA Database | LINEARITY | https://arxiv.org/abs/2008.03889v1 | Type | NR |
Video Quality Assessment | MSU NR VQA Database | VSFA | https://arxiv.org/abs/1908.00375v3 | SRCC | 0.9049 |
Video Quality Assessment | MSU NR VQA Database | VSFA | https://arxiv.org/abs/1908.00375v3 | PLCC | 0.9180 |
Video Quality Assessment | MSU NR VQA Database | VSFA | https://arxiv.org/abs/1908.00375v3 | KLCC | 0.7483 |
Video Quality Assessment | MSU NR VQA Database | VSFA | https://arxiv.org/abs/1908.00375v3 | Type | NR |
Video Quality Assessment | MSU NR VQA Database | MUSIQ | https://arxiv.org/abs/2108.05997v1 | SRCC | 0.9004 |
Video Quality Assessment | MSU NR VQA Database | MUSIQ | https://arxiv.org/abs/2108.05997v1 | PLCC | 0.9068 |
Video Quality Assessment | MSU NR VQA Database | MUSIQ | https://arxiv.org/abs/2108.05997v1 | KLCC | 0.7433 |
Video Quality Assessment | MSU NR VQA Database | MUSIQ | https://arxiv.org/abs/2108.05997v1 | Type | NR |
Video Quality Assessment | MSU NR VQA Database | DOVER | https://arxiv.org/abs/2211.04894v3 | SRCC | 0.8871 |
Video Quality Assessment | MSU NR VQA Database | DOVER | https://arxiv.org/abs/2211.04894v3 | PLCC | 0.9099 |
Video Quality Assessment | MSU NR VQA Database | DOVER | https://arxiv.org/abs/2211.04894v3 | KLCC | 0.7216 |
Video Quality Assessment | MSU NR VQA Database | DOVER | https://arxiv.org/abs/2211.04894v3 | Type | NR |
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