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 ⌀ |
|---|---|---|---|---|---|
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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