![]() If the benchmark doesn’t exist, a “new” icon will appear signifying a new leaderboard.If a benchmark already exists for a dataset/task pair you enter, you’ll see a link appear.Note that you can use parentheses to highlight details, for example: BERT Large (12 layers), FoveaBox (ResNeXt-101), EfficientNet-B7 (NoisyStudent). What are the model naming conventions? Model name should be straightforward, as presented in the paper. ImageNet on Image Classification already exists with metrics Top 1 Accuracy and Top 5 Accuracy. You should check if a benchmark already exists to prevent duplication if it doesn’t exist you can create a new dataset. Then choose a task, dataset and metric name from the Papers With Code taxonomy. You can manually edit the incorrect or missing fields. How do I add a new result from a table? Click on a cell in a table on the left hand side where the result comes from. Help! Don’t worry! If you make mistakes we can revert them: everything is versioned! So just tell us on the Slack channel if you’ve accidentally deleted something (and so on) - it’s not a problem at all, so just go for it! ![]() I’m editing for the first time and scared of making mistakes. Where do referenced results come from? If we find referenced results in a table to other papers, we show a parsed reference box that editors can use to annotate to get these extra results from other papers. Where do suggested results come from? We have a machine learning model running in the background that makes suggestions on papers. Blue is a referenced result that originates from a different paper. What do the colors mean? Green means the result is approved and shown on the website. A result consists of a metric value, model name, dataset name and task name. What are the colored boxes on the right hand side? These show results extracted from the paper and linked to tables on the left hand side. It shows extracted results on the right hand side that match the taxonomy on Papers With Code. What is this page? This page shows tables extracted from arXiv papers on the left-hand side. More scalable, achieving up to 102 transactions per second, as opposed to We find that while the one based on Hyperledger Fabric may have moreįavorable trust assumptions in certain settings, the one based on Trillian is Ledger and another based on the Trillian verifiable log-backed map, andĮvaluate their performance on simulated workloads based on real-world data Weīuild two prototype systems, one based on the Hyperledger Fabric distributed Organizations, while allowing published statistics to be publicly verified. Furthermore, our system protects the privacy of individuals and Their sensitive personal data, and enables auditors to detect violations of We propose a system, VAMS, that allows individuals to check accesses to Growing need for effective ways to hold data processors accountable for theirĪctions, while protecting the privacy of individuals and the integrity of theirĭata. Is considered for sharing (e.g., communication records and medical records),Īnd as it is increasingly used for making important decisions, there is a ![]() The sharing of personal data has the potential to bring substantial benefitsīoth to individuals and society, but these can be achieved only if people haveĬonfidence their data will not be used inappropriately. VAMS: Verifiable Auditing of Access to Confidential Data
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