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 ⌀ |
|---|---|---|---|---|---|
Stochastic Optimization | CIFAR-10 ResNet-18 - 200 Epochs | ADAM | https://arxiv.org/abs/1907.08610v2 | Accuracy | 94.84 |
Stochastic Optimization | AG News | Bert | https://arxiv.org/abs/2011.08042v1 | Accuracy (mean) | 93.86 |
Stochastic Optimization | AG News | Bert | https://arxiv.org/abs/2011.08042v1 | Accuracy (max) | 93.99 |
Stochastic Optimization | CIFAR-10 WRN-28-10 - 200 Epochs | Adam (eps-adjusted) | https://arxiv.org/abs/1912.01823v3 | Accuracy | 96.36 |
Stochastic Optimization | CIFAR-10 WRN-28-10 - 200 Epochs | AvaGrad | https://arxiv.org/abs/1912.01823v3 | Accuracy | 96.2 |
Stochastic Optimization | CIFAR-10 WRN-28-10 - 200 Epochs | SGD | https://arxiv.org/abs/1912.01823v3 | Accuracy | 96.14 |
Stochastic Optimization | CIFAR-10 WRN-28-10 - 200 Epochs | AdaShift | https://arxiv.org/abs/1912.01823v3 | Accuracy | 95.92 |
Stochastic Optimization | CIFAR-10 WRN-28-10 - 200 Epochs | AdamW | https://arxiv.org/abs/1912.01823v3 | Accuracy | 95.89 |
Stochastic Optimization | CIFAR-10 WRN-28-10 - 200 Epochs | AdaBound | https://arxiv.org/abs/1912.01823v3 | Accuracy | 94.6 |
Stochastic Optimization | MNIST | MLP | http://arxiv.org/abs/1705.07795v3 | NLL | 0.0541 |
Stochastic Optimization | ImageNet ResNet-50 - 60 Epochs | Lookahead | https://arxiv.org/abs/1907.08610v2 | Top 1 Accuracy | 75.49% |
Stochastic Optimization | ImageNet ResNet-50 - 60 Epochs | Lookahead | https://arxiv.org/abs/1907.08610v2 | Top 5 Accuracy | 92.53 |
Stochastic Optimization | ImageNet ResNet-50 - 60 Epochs | SGD | https://arxiv.org/abs/1907.08610v2 | Top 1 Accuracy | 75.15% |
Stochastic Optimization | ImageNet ResNet-50 - 60 Epochs | SGD | https://arxiv.org/abs/1907.08610v2 | Top 5 Accuracy | 92.56 |
Stochastic Optimization | CIFAR-10 | Resnet18 | https://arxiv.org/abs/2011.08042v1 | Accuracy (mean) | 85.89 |
Stochastic Optimization | CIFAR-10 | Resnet18 | https://arxiv.org/abs/2011.08042v1 | Accuracy (max) | 86.85 |
Stochastic Optimization | CIFAR-10 | Resnet34 | https://arxiv.org/abs/2011.08042v1 | Accuracy (mean) | 85.75 |
Stochastic Optimization | CIFAR-10 | Resnet34 | https://arxiv.org/abs/2011.08042v1 | Accuracy (max) | 86.14 |
Stochastic Optimization | CIFAR-100 | Resnet18 | https://arxiv.org/abs/2011.08042v1 | Accuracy (mean) | 58.01 |
Stochastic Optimization | CIFAR-100 | Resnet18 | https://arxiv.org/abs/2011.08042v1 | Accuracy (max) | 58.48 |
Stochastic Optimization | CIFAR-100 | Resnet34 | https://arxiv.org/abs/2011.08042v1 | Accuracy (mean) | 53.06 |
Stochastic Optimization | CIFAR-100 | Resnet34 | https://arxiv.org/abs/2011.08042v1 | Accuracy (max) | 54.5 |
Stochastic Optimization | CoLA | Bert | https://arxiv.org/abs/2011.08042v1 | Accuracy (mean) | 87.66 |
Stochastic Optimization | CoLA | Bert | https://arxiv.org/abs/2011.08042v1 | Accuracy (max) | 86.34 |
Stochastic Optimization | ImageNet ResNet-50 - 90 Epochs | AvaGrad | https://arxiv.org/abs/1912.01823v3 | Top 1 Accuracy | 76.51 |
Stochastic Optimization | ImageNet ResNet-50 - 90 Epochs | SGD | https://arxiv.org/abs/1912.01823v3 | Top 1 Accuracy | 75.99 |
Stochastic Optimization | ImageNet ResNet-50 - 90 Epochs | AdamW | https://arxiv.org/abs/1912.01823v3 | Top 1 Accuracy | 72.9 |
Stochastic Optimization | ImageNet ResNet-50 - 90 Epochs | AdaBound | https://arxiv.org/abs/1912.01823v3 | Top 1 Accuracy | 72.01 |
Stochastic Optimization | Penn Treebank (Character Level) 3x1000 LSTM - 500 Epochs | AvaGrad | https://arxiv.org/abs/1912.01823v3 | Bit per Character (BPC) | 1.175 |
Stochastic Optimization | Penn Treebank (Character Level) 3x1000 LSTM - 500 Epochs | AdamW | https://arxiv.org/abs/1912.01823v3 | Bit per Character (BPC) | 1.23 |
Stochastic Optimization | Penn Treebank (Character Level) 3x1000 LSTM - 500 Epochs | AdaShift | https://arxiv.org/abs/1912.01823v3 | Bit per Character (BPC) | 1.274 |
Stochastic Optimization | Penn Treebank (Character Level) 3x1000 LSTM - 500 Epochs | AdaBound | https://arxiv.org/abs/1912.01823v3 | Bit per Character (BPC) | 2.863 |
Stochastic Optimization | CIFAR-100 WRN-28-10 - 200 Epochs | AvaGrad | https://arxiv.org/abs/1912.01823v3 | Accuracy | 81.24 |
Stochastic Optimization | CIFAR-100 WRN-28-10 - 200 Epochs | AdaShift | https://arxiv.org/abs/1912.01823v3 | Accuracy | 81.12 |
Stochastic Optimization | CIFAR-100 WRN-28-10 - 200 Epochs | Adam (eps-adjusted) | https://arxiv.org/abs/1912.01823v3 | Accuracy | 81.04 |
Stochastic Optimization | CIFAR-100 WRN-28-10 - 200 Epochs | SGD | https://arxiv.org/abs/1912.01823v3 | Accuracy | 80.95 |
Stochastic Optimization | CIFAR-100 WRN-28-10 - 200 Epochs | AdamW | https://arxiv.org/abs/1912.01823v3 | Accuracy | 79.87 |
Stochastic Optimization | CIFAR-100 WRN-28-10 - 200 Epochs | AdaBound | https://arxiv.org/abs/1912.01823v3 | Accuracy | 77.24 |
Stochastic Optimization | ImageNet ResNet-50 - 50 Epochs | Lookahead | https://arxiv.org/abs/1907.08610v2 | Top 1 Accuracy | 75.13% |
Stochastic Optimization | ImageNet ResNet-50 - 50 Epochs | SGD | https://arxiv.org/abs/1907.08610v2 | Top 5 Accuracy | 92.15% |
Stochastic Optimization > Distributed Optimization | ^(#$!@#$)(()))****** | 多微电网区域配电系统的多目标分布式优化 | https://arxiv.org/abs/2202.09762v1 | 0..5sec | 10 |
Data Mining > Argument Mining | TACO -- Twitter Arguments from COnversations | TACO | https://arxiv.org/abs/2404.00406v1 | macro F1 | 85.06 |
Data Mining > Argument Mining > Component Classification | CDCP | ResAttArg | https://arxiv.org/abs/2102.12227v3 | Macro F1 | 78.71 |
Data Mining > Argument Mining > Argument Pair Extraction (APE) | RR | MGF | https://aclanthology.org/2021.emnlp-main.319 | Overall F1 | 34.4 |
Data Mining > Argument Mining > Argument Pair Extraction (APE) | RR | MLMC | https://aclanthology.org/2021.acl-long.496 | Overall F1 | 32.81 |
Data Mining > Argument Mining > Argument Pair Extraction (APE) | RR | MT-H-LSTM-CRF | https://aclanthology.org/2020.emnlp-main.569 | Overall F1 | 26.61 |
Data Mining > Argument Mining > Claim Extraction with Stance Classification (CESC) | IAM Dataset | Multi-label | https://arxiv.org/abs/2203.12257v3 | Macro F1 | 60.25 |
Data Mining > Argument Mining > Claim-Evidence Pair Extraction (CEPE) | IAM Dataset | Multi-task | https://arxiv.org/abs/2203.12257v3 | F1 | 35.92 |
Data Mining > Argument Mining > ValNov | ValNov Subtask B | NLP@UIT | https://aclanthology.org/2022.argmining-1.7 | JOINT-F1 | 41.50 |
Data Mining > Argument Mining > ValNov | ValNov Subtask B | NLP@UIT | https://aclanthology.org/2022.argmining-1.7 | NOV-F1 | 38.39 |
Data Mining > Argument Mining > ValNov | ValNov Subtask B | NLP@UIT | https://aclanthology.org/2022.argmining-1.7 | VAL-F1 | 44.60 |
Data Mining > Argument Mining > ValNov | ValNov Subtask B | AXiS@EdUni | https://aclanthology.org/2022.argmining-1.7 | JOINT-F1 | 29.16 |
Data Mining > Argument Mining > ValNov | ValNov Subtask B | AXiS@EdUni | https://aclanthology.org/2022.argmining-1.7 | NOV-F1 | 25.86 |
Data Mining > Argument Mining > ValNov | ValNov Subtask B | AXiS@EdUni | https://aclanthology.org/2022.argmining-1.7 | VAL-F1 | 32.47 |
Data Mining > Argument Mining > ValNov | ValNov Subtask B | Baseline | https://aclanthology.org/2022.argmining-1.7 | JOINT-F1 | 21.46 |
Data Mining > Argument Mining > ValNov | ValNov Subtask B | Baseline | https://aclanthology.org/2022.argmining-1.7 | NOV-F1 | 23.09 |
Data Mining > Argument Mining > ValNov | ValNov Subtask B | Baseline | https://aclanthology.org/2022.argmining-1.7 | VAL-F1 | 19.82 |
Data Mining > Argument Mining > ValNov | ValNov Subtask A | CLTeamL-3 | https://aclanthology.org/2022.argmining-1.7 | JOINT-F1 | 45.16 |
Data Mining > Argument Mining > ValNov | ValNov Subtask A | CLTeamL-3 | https://aclanthology.org/2022.argmining-1.7 | VAL-F1 | 74.64 |
Data Mining > Argument Mining > ValNov | ValNov Subtask A | CLTeamL-3 | https://aclanthology.org/2022.argmining-1.7 | NOV-F1 | 61.75 |
Data Mining > Argument Mining > ValNov | ValNov Subtask A | AXiS@EdUni-1 | https://aclanthology.org/2022.argmining-1.9 | JOINT-F1 | 43.27 |
Data Mining > Argument Mining > ValNov | ValNov Subtask A | AXiS@EdUni-1 | https://aclanthology.org/2022.argmining-1.9 | VAL-F1 | 69.80 |
Data Mining > Argument Mining > ValNov | ValNov Subtask A | AXiS@EdUni-1 | https://aclanthology.org/2022.argmining-1.9 | NOV-F1 | 62.43 |
Data Mining > Argument Mining > ValNov | ValNov Subtask A | ACCEPT-1 | https://aclanthology.org/2022.argmining-1.7 | JOINT-F1 | 43.13 |
Data Mining > Argument Mining > ValNov | ValNov Subtask A | ACCEPT-1 | https://aclanthology.org/2022.argmining-1.7 | VAL-F1 | 59.20 |
Data Mining > Argument Mining > ValNov | ValNov Subtask A | ACCEPT-1 | https://aclanthology.org/2022.argmining-1.7 | NOV-F1 | 70.00 |
Data Mining > Argument Mining > ValNov | ValNov Subtask A | CSS | https://aclanthology.org/2022.argmining-1.7 | JOINT-F1 | 42.40 |
Data Mining > Argument Mining > ValNov | ValNov Subtask A | CSS | https://aclanthology.org/2022.argmining-1.7 | VAL-F1 | 70.76 |
Data Mining > Argument Mining > ValNov | ValNov Subtask A | CSS | https://aclanthology.org/2022.argmining-1.7 | NOV-F1 | 59.86 |
Data Mining > Argument Mining > ValNov | ValNov Subtask A | System Average | https://aclanthology.org/2022.argmining-1.7 | JOINT-F1 | 35.94 |
Data Mining > Argument Mining > ValNov | ValNov Subtask A | System Average | https://aclanthology.org/2022.argmining-1.7 | VAL-F1 | 62.74 |
Data Mining > Argument Mining > ValNov | ValNov Subtask A | System Average | https://aclanthology.org/2022.argmining-1.7 | NOV-F1 | 52.97 |
Data Mining > Argument Mining > ValNov | ValNov Subtask A | NLP@UIT | https://aclanthology.org/2022.argmining-1.7 | JOINT-F1 | 25.89 |
Data Mining > Argument Mining > ValNov | ValNov Subtask A | NLP@UIT | https://aclanthology.org/2022.argmining-1.7 | VAL-F1 | 61.72 |
Data Mining > Argument Mining > ValNov | ValNov Subtask A | NLP@UIT | https://aclanthology.org/2022.argmining-1.7 | NOV-F1 | 43.36 |
Data Mining > Argument Mining > ValNov | ValNov Subtask A | Baseline | https://aclanthology.org/2022.argmining-1.7 | JOINT-F1 | 23.90 |
Data Mining > Argument Mining > ValNov | ValNov Subtask A | Baseline | https://aclanthology.org/2022.argmining-1.7 | VAL-F1 | 59.96 |
Data Mining > Argument Mining > ValNov | ValNov Subtask A | Baseline | https://aclanthology.org/2022.argmining-1.7 | NOV-F1 | 36.12 |
Data Mining > Argument Mining > ValNov | ValNov Subtask A | Harshad | https://aclanthology.org/2022.argmining-1.7 | JOINT-F1 | 17.35 |
Data Mining > Argument Mining > ValNov | ValNov Subtask A | Harshad | https://aclanthology.org/2022.argmining-1.7 | VAL-F1 | 56.31 |
Data Mining > Argument Mining > ValNov | ValNov Subtask A | Harshad | https://aclanthology.org/2022.argmining-1.7 | NOV-F1 | 39.00 |
Data Mining > Opinion Mining | IMDb Movie Reviews | ELECTRA | https://arxiv.org/abs/2308.03235v1 | F1 | 95.6 |
Data Mining > Opinion Mining | IMDb Movie Reviews | ELECTRA | https://arxiv.org/abs/2308.03235v1 | Accuracy | 95.6 |
Data Mining > Opinion Mining | IMDb Movie Reviews | RoBERTa | https://arxiv.org/abs/2308.03235v1 | F1 | 95.3 |
Data Mining > Opinion Mining | IMDb Movie Reviews | RoBERTa | https://arxiv.org/abs/2308.03235v1 | Accuracy | 95.3 |
Data Mining > Opinion Mining | IMDb Movie Reviews | DeBERTa | https://arxiv.org/abs/2308.03235v1 | F1 | 95.1 |
Data Mining > Opinion Mining | IMDb Movie Reviews | DeBERTa | https://arxiv.org/abs/2308.03235v1 | Accuracy | 95.1 |
Data Mining > Opinion Mining | IMDb Movie Reviews | Longformer | https://arxiv.org/abs/2308.03235v1 | Accuracy | 95.0 |
Data Mining > Opinion Mining | IMDb Movie Reviews | XLNet | https://arxiv.org/abs/2308.03235v1 | F1 | 94.9 |
Data Mining > Opinion Mining | IMDb Movie Reviews | XLNet | https://arxiv.org/abs/2308.03235v1 | Accuracy | 94.8 |
Data Mining > Opinion Mining | IMDb Movie Reviews | BART | https://arxiv.org/abs/2308.03235v1 | Accuracy | 94.6 |
Data Mining > Opinion Mining | IMDb Movie Reviews | ConvBERT | https://arxiv.org/abs/2308.03235v1 | F1 | 94.6 |
Data Mining > Opinion Mining | IMDb Movie Reviews | ConvBERT | https://arxiv.org/abs/2308.03235v1 | Accuracy | 94.5 |
Data Mining > Opinion Mining | IMDb Movie Reviews | BERT | https://arxiv.org/abs/2308.03235v1 | F1 | 94.1 |
Data Mining > Opinion Mining | IMDb Movie Reviews | BERT | https://arxiv.org/abs/2308.03235v1 | Accuracy | 94.0 |
Data Mining > Opinion Mining | IMDb Movie Reviews | T5 | https://arxiv.org/abs/2308.03235v1 | F1 | 94.0 |
Data Mining > Opinion Mining | IMDb Movie Reviews | T5 | https://arxiv.org/abs/2308.03235v1 | Accuracy | 93.9 |
Data Mining > Opinion Mining | IMDb Movie Reviews | DistilBERT | https://arxiv.org/abs/2308.03235v1 | F1 | 93.5 |
Data Mining > Opinion Mining | IMDb Movie Reviews | DistilBERT | https://arxiv.org/abs/2308.03235v1 | Accuracy | 93.4 |
Data Mining > Opinion Mining | IMDb Movie Reviews | ALBERT | https://arxiv.org/abs/2308.03235v1 | F1 | 93.0 |
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