Datasets:

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[ { "index": 1, "records": [ { "column": 20, "dataset": "Cityscapes test", "metric": "Mean IoU (class)", "model": "Dense Prediction Cell", "row": 4, "task": "Semantic Segmentation", "value": "82.7" } ] }, { "index": 2, "records": [ { "column": 8, "dataset": "PASCAL-Person-Part", "metric": "mIoU", "model": "DPC", "row": 4, "task": "Human Part Segmentation", "value": "71.34" } ] }, { "index": 3, "records": [ { "column": 21, "dataset": "PASCAL VOC 2012 test", "metric": "Mean IoU", "model": "DPC", "row": 6, "task": "Semantic Segmentation", "value": "87.9" } ] } ]

[ { "index": 2, "records": [ { "column": 2, "dataset": "MOT16", "metric": "MOTA", "model": "MOTDT", "row": 9, "task": "Online Multi-Object Tracking", "value": "47.6" } ] } ]

[ { "index": 0, "records": [ { "column": 1, "dataset": "Atari-57", "metric": "Medium Human-Normalized Score", "model": "PopArt-IMPALA", "row": 3, "task": "Atari Games", "value": "110.7" }, { "column": 6, "dataset": "Dmlab-30", "metric": "Medium Human-Normalized Score", "model": "PopArt-IMPALA", "row": 3, "task": "Visual Navigation", "value": "72.8%" } ] } ]

[ { "index": 1, "records": [ { "column": 16, "dataset": "Human3.6M", "metric": "Average MPJPE (mm)", "model": "Synthetic Occlusion Aug+ Vol Heatmaps", "row": 15, "task": "3D Human Pose Estimation", "value": "54.2" } ] } ]

[ { "index": 3, "records": [ { "column": 7, "dataset": "COCO test-dev", "metric": "box AP", "model": "ResNet-50-FPN Mask R-CNN + KL Loss + var voting + soft-NMS", "row": 12, "task": "Object Detection", "value": "40.4" } ] } ]

[ { "index": 0, "records": [ { "column": 22, "dataset": "GTAV-to-Cityscapes Labels", "metric": "mIoU", "model": "CLAN", "row": 15, "task": "Synthetic-to-Real Translation", "value": "43.2" } ] } ]

[ { "index": 1, "records": [ { "column": 1, "dataset": "UCI POWER", "metric": "NLL", "model": "FFJORD", "row": 3, "task": "Density Estimation", "value": "-0.46" }, { "column": 2, "dataset": "UCI GAS", "metric": "NLL", "model": "FFJORD", "row": 3, "task": "Density Estimation", "value": "-8.59" }, { "column": 3, "dataset": "UCI HEPMASS", "metric": "NLL", "model": "FFJORD", "row": 3, "task": "Density Estimation", "value": "14.92" }, { "column": 4, "dataset": "UCI MINIBOONE", "metric": "NLL", "model": "FFJORD", "row": 3, "task": "Density Estimation", "value": "10.43" }, { "column": 5, "dataset": "BSDS300", "metric": "NLL", "model": "FFJORD", "row": 3, "task": "Density Estimation", "value": "-157.4" }, { "column": 6, "dataset": "MNIST", "metric": "NLL", "model": "FFJORD", "row": 3, "task": "Density Estimation", "value": "0.99" }, { "column": 7, "dataset": "CIFAR-10", "metric": "NLL", "model": "FFJORD", "row": 3, "task": "Density Estimation", "value": "3.4" } ] }, { "index": 2, "records": [ { "column": 1, "dataset": "MNIST", "metric": "Negative ELBO", "model": "FFJORD", "row": 5, "task": "Density Estimation", "value": "82.82" }, { "column": 2, "dataset": "OMNIGLOT", "metric": "Negative ELBO", "model": "FFJORD", "row": 5, "task": "Density Estimation", "value": "98.33" }, { "column": 3, "dataset": "Freyfaces", "metric": "Negative ELBO", "model": "FFJORD", "row": 5, "task": "Density Estimation", "value": "4.39" }, { "column": 4, "dataset": "Caltech-101", "metric": "Negative ELBO", "model": "FFJORD", "row": 5, "task": "Density Estimation", "value": "104.03" } ] } ]

[ { "index": 1, "records": [ { "column": 1, "dataset": "USPS", "metric": "NMI", "model": "SR-K-means", "row": 3, "task": "Image Clustering", "value": "0.936" }, { "column": 2, "dataset": "USPS", "metric": "Accuracy", "model": "SR-K-means", "row": 3, "task": "Image Clustering", "value": "0.974" }, { "column": 3, "dataset": "MNIST-test", "metric": "NMI", "model": "SR-K-means", "row": 3, "task": "Image Clustering", "value": "0.873" }, { "column": 4, "dataset": "MNIST-test", "metric": "Accuracy", "model": "SR-K-means", "row": 3, "task": "Image Clustering", "value": "0.863" }, { "column": 5, "dataset": "MNIST-full", "metric": "NMI", "model": "SR-K-means", "row": 3, "task": "Image Clustering", "value": "0.866" }, { "column": 6, "dataset": "MNIST-full", "metric": "Accuracy", "model": "SR-K-means", "row": 3, "task": "Image Clustering", "value": "0.939" }, { "column": 7, "dataset": "YouTube Faces DB", "metric": "NMI", "model": "SR-K-means", "row": 3, "task": "Image Clustering", "value": "0.806" }, { "column": 8, "dataset": "YouTube Faces DB", "metric": "Accuracy", "model": "SR-K-means", "row": 3, "task": "Image Clustering", "value": "0.605" }, { "column": 9, "dataset": "CMU-PIE", "metric": "NMI", "model": "SR-K-means", "row": 3, "task": "Image Clustering", "value": "0.945" }, { "column": 10, "dataset": "CMU-PIE", "metric": "Accuracy", "model": "SR-K-means", "row": 3, "task": "Image Clustering", "value": "0.902" }, { "column": 11, "dataset": "FRGC", "metric": "NMI", "model": "SR-K-means", "row": 3, "task": "Image Clustering", "value": "0.487" }, { "column": 12, "dataset": "FRGC", "metric": "Accuracy", "model": "SR-K-means", "row": 3, "task": "Image Clustering", "value": "0.413" } ] } ]

[ { "index": 0, "records": [ { "column": 2, "dataset": "Quora Question Pairs", "metric": "Accuracy", "model": "BERT (single model)", "row": 6, "task": "Question Answering", "value": "72.1" } ] }, { "index": 1, "records": [ { "column": 4, "dataset": "SQuAD1.1 dev", "metric": "F1", "model": "BERT base (single)", "row": 8, "task": "Question Answering", "value": "88.5" }, { "column": 1, "dataset": "SQuAD1.1 dev", "metric": "EM", "model": "BERT base (single)", "row": 10, "task": "Question Answering", "value": "80.8" }, { "column": 1, "dataset": "SQuAD1.1 dev", "metric": "EM", "model": "BERT large (single)", "row": 11, "task": "Question Answering", "value": "84.1" }, { "column": 2, "dataset": "SQuAD1.1 dev", "metric": "F1", "model": "BERT large (single)", "row": 11, "task": "Question Answering", "value": "90.9" }, { "column": 1, "dataset": "SQuAD1.1 dev", "metric": "EM", "model": "BERT large (+TriviaQA)", "row": 13, "task": "Question Answering", "value": "84.2" }, { "column": 2, "dataset": "SQuAD1.1 dev", "metric": "F1", "model": "BERT large (+TriviaQA)", "row": 13, "task": "Question Answering", "value": "91.1" } ] }, { "index": 2, "records": [ { "column": 1, "dataset": "SQuAD2.0 dev", "metric": "EM", "model": "BERT large", "row": 10, "task": "Question Answering", "value": "78.7" }, { "column": 2, "dataset": "SQuAD2.0 dev", "metric": "F1", "model": "BERT large", "row": 10, "task": "Question Answering", "value": "81.9" } ] }, { "index": 3, "records": [ { "column": 1, "dataset": "SWAG", "metric": "Dev", "model": "BERT Base", "row": 4, "task": "Common Sense Reasoning", "value": "81.6" }, { "column": 1, "dataset": "SWAG", "metric": "Dev", "model": "BERT Large", "row": 5, "task": "Common Sense Reasoning", "value": "86.6" }, { "column": 2, "dataset": "SWAG", "metric": "Test", "model": "BERT Large", "row": 5, "task": "Common Sense Reasoning", "value": "86.3" } ] }, { "index": 6, "records": [ { "column": 2, "dataset": "CoNLL 2003 (English)", "metric": "F1", "model": "BERT Large", "row": 5, "task": "Named Entity Recognition", "value": "92.8" }, { "column": 2, "dataset": "CoNLL 2003 (English)", "metric": "F1", "model": "BERT Base", "row": 6, "task": "Named Entity Recognition", "value": "92.4" } ] } ]

[ { "index": 1, "records": [ { "column": 1, "dataset": "MUTAG", "metric": "Accuracy", "model": "SF + RFC", "row": 6, "task": "Graph Classification", "value": "88.4" }, { "column": 2, "dataset": "PTC", "metric": "Accuracy", "model": "SF + RFC", "row": 6, "task": "Graph Classification", "value": "62.8" }, { "column": 3, "dataset": "ENZYMES", "metric": "Accuracy", "model": "SF + RFC", "row": 6, "task": "Graph Classification", "value": "43.7" }, { "column": 4, "dataset": "PROTEINS", "metric": "Accuracy", "model": "SF + RFC", "row": 6, "task": "Graph Classification", "value": "73.6" }, { "column": 5, "dataset": "D&D", "metric": "Accuracy", "model": "SF + RFC", "row": 6, "task": "Graph Classification", "value": "24.6" }, { "column": 6, "dataset": "NCI1", "metric": "Accuracy", "model": "SF + RFC", "row": 6, "task": "Graph Classification", "value": "75.2" } ] } ]

[ { "index": 0, "records": [ { "column": 1, "dataset": "OMNIGLOT - 1-Shot, 20-way", "metric": "Accuracy", "model": "MAML++", "row": 11, "task": "Few-Shot Image Classification", "value": "97.65" }, { "column": 2, "dataset": "OMNIGLOT - 5-Shot, 20-way", "metric": "Accuracy", "model": "MAML++", "row": 11, "task": "Few-Shot Image Classification", "value": "99.33" } ] }, { "index": 1, "records": [ { "column": 2, "dataset": "Mini-Imagenet 5-way (1-shot)", "metric": "Accuracy", "model": "MAML++", "row": 12, "task": "Few-Shot Image Classification", "value": "52.40" }, { "column": 3, "dataset": "Mini-Imagenet 5-way (5-shot)", "metric": "Accuracy", "model": "MAML++", "row": 12, "task": "Few-Shot Image Classification", "value": "67.15" } ] }, { "index": 3, "records": [ { "column": 1, "dataset": "OMNIGLOT - 1-Shot, 5-way", "metric": "Accuracy", "model": "MAML++", "row": 11, "task": "Few-Shot Image Classification", "value": "99.47" }, { "column": 2, "dataset": "OMNIGLOT - 5-Shot, 5-way", "metric": "Accuracy", "model": "MAML++", "row": 11, "task": "Few-Shot Image Classification", "value": "99.85" } ] } ]

[ { "index": 1, "records": [ { "column": 2, "dataset": "Criteo", "metric": "AUC", "model": "AutoInt", "row": 10, "task": "Click-Through Rate Prediction", "value": "0.8061" }, { "column": 3, "dataset": "Criteo", "metric": "Log Loss", "model": "AutoInt", "row": 10, "task": "Click-Through Rate Prediction", "value": "0.4454" }, { "column": 4, "dataset": "Avazu", "metric": "AUC", "model": "AutoInt", "row": 10, "task": "Click-Through Rate Prediction", "value": "0.7752" }, { "column": 5, "dataset": "Avazu", "metric": "LogLoss", "model": "AutoInt", "row": 10, "task": "Click-Through Rate Prediction", "value": "0.3823" }, { "column": 6, "dataset": "KDD12", "metric": "AUC", "model": "AutoInt", "row": 10, "task": "Click-Through Rate Prediction", "value": "0.7881" }, { "column": 7, "dataset": "KDD12", "metric": "Log Loss", "model": "AutoInt", "row": 10, "task": "Click-Through Rate Prediction", "value": "0.1545" }, { "column": 8, "dataset": "MovieLens 1M", "metric": "AUC", "model": "AutoInt", "row": 10, "task": "Click-Through Rate Prediction", "value": "0.846" }, { "column": 9, "dataset": "MovieLens 1M", "metric": "Log Loss", "model": "AutoInt", "row": 10, "task": "Click-Through Rate Prediction", "value": "0.3784" } ] } ]

[ { "index": 0, "records": [ { "column": 3, "dataset": "Urban100 sigma25", "metric": "PSNR", "model": "N3Net", "row": 7, "task": "Grayscale Image Denoising", "value": "30.19" }, { "column": 4, "dataset": "Urban100 sigma25", "metric": "SSIM", "model": "N3Net", "row": 7, "task": "Grayscale Image Denoising", "value": "0.892" } ] }, { "index": 2, "records": [ { "column": 9, "dataset": "Set12 sigma25", "metric": "PSNR", "model": "N3Net", "row": 1, "task": "Grayscale Image Denoising", "value": "30.55" }, { "column": 9, "dataset": "Set12 sigma50", "metric": "PSNR", "model": "N3Net", "row": 2, "task": "Grayscale Image Denoising", "value": "27.43" }, { "column": 9, "dataset": "Set12 sigma70", "metric": "PSNR", "model": "N3Net", "row": 3, "task": "Grayscale Image Denoising", "value": "25.9" }, { "column": 9, "dataset": "BSD68 sigma25", "metric": "PSNR", "model": "N3Net", "row": 4, "task": "Grayscale Image Denoising", "value": "29.3" }, { "column": 9, "dataset": "BSD68 sigma50", "metric": "PSNR", "model": "N3Net", "row": 5, "task": "Grayscale Image Denoising", "value": "26.39" }, { "column": 9, "dataset": "BSD68 sigma70", "metric": "PSNR", "model": "N3Net", "row": 6, "task": "Grayscale Image Denoising", "value": "25.14" }, { "column": 9, "dataset": "Urban100 sigma50", "metric": "PSNR", "model": "N3Net", "row": 8, "task": "Grayscale Image Denoising", "value": "26.82" }, { "column": 9, "dataset": "Urban100 sigma70", "metric": "PSNR", "model": "N3Net", "row": 9, "task": "Grayscale Image Denoising", "value": "25.15" } ] }, { "index": 4, "records": [ { "column": 7, "dataset": "Set5 - 2x upscaling", "metric": "PSNR", "model": "N3Net", "row": 1, "task": "Image Super-Resolution", "value": "37.57" }, { "column": 7, "dataset": "Set5 - 3x upscaling", "metric": "PSNR", "model": "N3Net", "row": 2, "task": "Image Super-Resolution", "value": "33.84" }, { "column": 7, "dataset": "Set5 - 4x upscaling", "metric": "PSNR", "model": "N3Net", "row": 3, "task": "Image Super-Resolution", "value": "31.5" } ] } ]

[ { "index": 0, "records": [ { "column": 1, "dataset": "Atari 2600 Gravitar", "metric": "Score", "model": "RND", "row": 1, "task": "Atari Games", "value": "3906" }, { "column": 2, "dataset": "Atari 2600 Montezuma's Revenge", "metric": "Score", "model": "RND", "row": 1, "task": "Atari Games", "value": "8152" }, { "column": 3, "dataset": "Atari 2600 Pitfall!", "metric": "Score", "model": "RND", "row": 1, "task": "Atari Games", "value": "-3" }, { "column": 4, "dataset": "Atari 2600 Private Eye", "metric": "Score", "model": "RND", "row": 1, "task": "Atari Games", "value": "8666" }, { "column": 5, "dataset": "Atari 2600 Solaris", "metric": "Score", "model": "RND", "row": 1, "task": "Atari Games", "value": "3282" }, { "column": 6, "dataset": "Atari 2600 Venture", "metric": "Score", "model": "RND", "row": 1, "task": "Atari Games", "value": "1859" } ] } ]

[ { "index": 0, "records": [ { "column": 9, "dataset": "BIT", "metric": "Accuracy", "model": "H-LSTCM", "row": 13, "task": "Human Interaction Recognition", "value": "94.03" } ] }, { "index": 1, "records": [ { "column": 7, "dataset": "UT", "metric": "Accuracy", "model": "H-LSTCM", "row": 18, "task": "Human Interaction Recognition", "value": "98.33" } ] }, { "index": 2, "records": [ { "column": 6, "dataset": "Collective Activity", "metric": "Accuracy", "model": "H-LSTCM", "row": 20, "task": "Group Activity Recognition", "value": "83.75" } ] }, { "index": 3, "records": [ { "column": 9, "dataset": "Volleyball", "metric": "Accuracy", "model": "H-LSTCM", "row": 10, "task": "Group Activity Recognition", "value": "88.4" } ] } ]

[ { "index": 1, "records": [ { "column": 1, "dataset": "SNLI", "metric": "% Test Accuracy", "model": "Bi-LSTM sentence encoder (max-pooling)", "row": 18, "task": "Natural Language Inference", "value": "84.5" }, { "column": 2, "dataset": "MultiNLI", "metric": "Matched", "model": "Bi-LSTM sentence encoder (max-pooling)", "row": 18, "task": "Natural Language Inference", "value": "70.7" }, { "column": 3, "dataset": "MultiNLI", "metric": "Mismatched", "model": "Bi-LSTM sentence encoder (max-pooling)", "row": 18, "task": "Natural Language Inference", "value": "71.1" }, { "column": 1, "dataset": "SNLI", "metric": "% Test Accuracy", "model": "Stacked Bi-LSTMs (shortcut connections, max-pooling)", "row": 22, "task": "Natural Language Inference", "value": "84.8" }, { "column": 2, "dataset": "MultiNLI", "metric": "Matched", "model": "Stacked Bi-LSTMs (shortcut connections, max-pooling)", "row": 22, "task": "Natural Language Inference", "value": "71.4" }, { "column": 3, "dataset": "MultiNLI", "metric": "Mismatched", "model": "Stacked Bi-LSTMs (shortcut connections, max-pooling)", "row": 22, "task": "Natural Language Inference", "value": "72.2" }, { "column": 1, "dataset": "SNLI", "metric": "% Test Accuracy", "model": "Stacked Bi-LSTMs (shortcut connections, max-pooling, attention)", "row": 24, "task": "Natural Language Inference", "value": "84.4" }, { "column": 2, "dataset": "MultiNLI", "metric": "Matched", "model": "Stacked Bi-LSTMs (shortcut connections, max-pooling, attention)", "row": 24, "task": "Natural Language Inference", "value": "70.7" }, { "column": 3, "dataset": "MultiNLI", "metric": "Mismatched", "model": "Stacked Bi-LSTMs (shortcut connections, max-pooling, attention)", "row": 24, "task": "Natural Language Inference", "value": "70.5" } ] }, { "index": 2, "records": [ { "column": 1, "dataset": "FNC-1", "metric": "Weighted Accuracy", "model": "Bi-LSTM (max-pooling, attention)", "row": 17, "task": "Fake News Detection", "value": "82.23" }, { "column": 2, "dataset": "FNC-1", "metric": "Per-class Accuracy (Unrelated)", "model": "Bi-LSTM (max-pooling, attention)", "row": 17, "task": "Fake News Detection", "value": "96.74" }, { "column": 3, "dataset": "FNC-1", "metric": "Per-class Accuracy (Discuss)", "model": "Bi-LSTM (max-pooling, attention)", "row": 17, "task": "Fake News Detection", "value": "81.52" }, { "column": 4, "dataset": "FNC-1", "metric": "Per-class Accuracy (Agree)", "model": "Bi-LSTM (max-pooling, attention)", "row": 17, "task": "Fake News Detection", "value": "51.34" }, { "column": 5, "dataset": "FNC-1", "metric": "Per-class Accuracy (Disagree)", "model": "Bi-LSTM (max-pooling, attention)", "row": 17, "task": "Fake News Detection", "value": "10.33" } ] } ]

[ { "index": 0, "records": [ { "column": 2, "dataset": "Wiki-Vote", "metric": "AUC", "model": "Asymmetric Transitivity Preservation", "row": 9, "task": "Link Prediction", "value": "94.81" }, { "column": 3, "dataset": "Cit-HepPH", "metric": "AUC", "model": "Asymmetric Transitivity Preservation", "row": 9, "task": "Link Prediction", "value": "89.16" }, { "column": 4, "dataset": "Gnutella", "metric": "AUC", "model": "Asymmetric Transitivity Preservation", "row": 9, "task": "Link Prediction", "value": "93.14" } ] } ]

[ { "index": 4, "records": [ { "column": 1, "dataset": "CommonsenseQA", "metric": "Accuracy", "model": "BERT-LARGE", "row": 6, "task": "Common Sense Reasoning", "value": "55.9" } ] } ]

[ { "index": 2, "records": [ { "column": 1, "dataset": "FB15k-237", "metric": "Hits@10", "model": "Structure-Aware Convolutional Networks", "row": 8, "task": "Link Prediction", "value": "0.54" }, { "column": 2, "dataset": "FB15k-237", "metric": "Hits@3", "model": "Structure-Aware Convolutional Networks", "row": 8, "task": "Link Prediction", "value": "0.39" }, { "column": 3, "dataset": "FB15k-237", "metric": "Hits@1", "model": "Structure-Aware Convolutional Networks", "row": 8, "task": "Link Prediction", "value": "0.26" }, { "column": 4, "dataset": "FB15k-237", "metric": "MRR", "model": "Structure-Aware Convolutional Networks", "row": 8, "task": "Link Prediction", "value": "0.35" }, { "column": 5, "dataset": "WN18RR", "metric": "Hits@10", "model": "Structure-Aware Convolutional Networks", "row": 8, "task": "Link Prediction", "value": "0.54" }, { "column": 6, "dataset": "WN18RR", "metric": "Hits@3", "model": "Structure-Aware Convolutional Networks", "row": 8, "task": "Link Prediction", "value": "0.48" }, { "column": 7, "dataset": "WN18RR", "metric": "Hits@1", "model": "Structure-Aware Convolutional Networks", "row": 8, "task": "Link Prediction", "value": "0.43" }, { "column": 8, "dataset": "WN18RR", "metric": "MRR", "model": "Structure-Aware Convolutional Networks", "row": 8, "task": "Link Prediction", "value": "0.47" } ] } ]

[ { "index": 1, "records": [ { "column": 1, "dataset": "YAGO39K", "metric": "MRR", "model": "TransC (bern)", "row": 11, "task": "Link Prediction", "value": "0.11199999999999999" }, { "column": 2, "dataset": "YAGO39K", "metric": "MRR", "model": "TransC (bern)", "row": 11, "task": "Link Prediction", "value": "0.42" }, { "column": 3, "dataset": "YAGO39K", "metric": "Hits@1", "model": "TransC (bern)", "row": 11, "task": "Link Prediction", "value": "0.298" }, { "column": 4, "dataset": "YAGO39K", "metric": "Hits@3", "model": "TransC (bern)", "row": 11, "task": "Link Prediction", "value": "0.502" }, { "column": 5, "dataset": "YAGO39K", "metric": "Hits@10", "model": "TransC (bern)", "row": 11, "task": "Link Prediction", "value": "0.698" }, { "column": 6, "dataset": "YAGO39K", "metric": "Accuracy", "model": "TransC (bern)", "row": 11, "task": "Triple Classification", "value": "93.8" }, { "column": 7, "dataset": "YAGO39K", "metric": "Precision", "model": "TransC (bern)", "row": 11, "task": "Triple Classification", "value": "94.8" }, { "column": 8, "dataset": "YAGO39K", "metric": "Recall", "model": "TransC (bern)", "row": 11, "task": "Triple Classification", "value": "92.7" }, { "column": 9, "dataset": "YAGO39K", "metric": "F1-Score", "model": "TransC (bern)", "row": 11, "task": "Triple Classification", "value": "93.7" } ] } ]

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