How do I add referenced results? If a table has references, you can use the parse references feature to get more results from other papers. When editing multiple results from the same table you can click the "Change all" button to copy the current value to all other records from that table.If you're feeling lucky, Cmd+Click a cell in a table to get the first result automatically.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. Finally, we explore and give some challenges and open problems for the optimization in machine learning. Next, we summarize the applications and developments of optimization methods in some popular machine learning fields. Then, we introduce the principles and progresses of commonly used optimization methods. In this paper, we first describe the optimization problems in machine learning. The systematic retrospect and summary of the optimization methods from the perspective of machine learning are of great significance, which can offer guidance for both developments of optimization and machine learning research. A lot of work on solving optimization problems or improving optimization methods in machine learning has been proposed successively. With the exponential growth of data amount and the increase of model complexity, optimization methods in machine learning face more and more challenges. Optimization, as an important part of machine learning, has attracted much attention of researchers. Machine learning develops rapidly, which has made many theoretical breakthroughs and is widely applied in various fields. Solve problems with many objectives.A Survey of Optimization Methods from a Machine Learning Perspective Objective: Use Global Optimization Toolbox functionality to solve problems where classical algorithms fail or work inefficiently. Interpret the output from the solver and diagnose the progress of an optimization. Objective: Select an appropriate solver and algorithm by considering the type of optimization problem to be solved.
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