MuMiN is a misinformation graph dataset containing rich social media data (tweets, replies, users, images, articles, hashtags), spanning 21 million tweets belonging to 26 thousand Twitter threads, each of which have been semantically linked to 13 thousand fact-checked claims across dozens of topics, events and domains, in 41 different languages, spanning more than a decade.
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HiAML Computational Graph (CG) family introduced in "GENNAPE: Towards Generalized Neural Architecture Performance Estimators", accepted to AAAI-23. Contains 4.6k CIFAR-10 networks with an accuracy range of [91.11%, 93.44%].
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Inception Computational Graph (CG) family introduced in "GENNAPE: Towards Generalized Neural Architecture Performance Estimators", accepted to AAAI-23. Contains 580 CIFAR-10 networks with an accuracy range of [89.08%, 94.03%].
Two-Path Computational Graph (CG) family introduced in "GENNAPE: Towards Generalized Neural Architecture Performance Estimators", accepted to AAAI-23. Contains 6.9k CIFAR-10 networks with an accuracy range of [85.53%, 92.34%].
In this work, we propose a novel remote sensing dataset, FireRisk, consisting of 7 fire risk classes with a total of 91 872 labelled images for fire risk assessment. This remote sensing dataset is labelled with the fire risk classes supplied by the Wildfire Hazard Potential (WHP) raster dataset, and remote sensing images are collected using the National Agriculture Imagery Program (NAIP), a high-resolution remote sensing imagery program. On FireRisk, we present benchmark performance for supervised and self-supervised representations, with Masked Autoencoders (MAE) pre-trained on ImageNet1k achieving the highest classification accuracy, 65.29%.
1 PAPER • 1 BENCHMARK