Search Results for "labeling"

We’ve made it our mission to pull in event tickets from every corner of the internet, showing you them all on the same screen so you can compare them and get to your game/concert/show as quickly as possible. Of course, a big problem with most corners of the internet is labeling. One of our most consistently frustrating issues is trying to figure out whether two ticket listings are for the same real-life event (that is, without enlisting the help of our army of interns). To pick an example completely at random, Cirque du Soleil has a show running in New York called “Zarkana”. When we scour the web to find tickets for sale, mostly those tickets are identified by a title, date, time, and venue. ...

...It provides modular tools for running reproducible experiments across different datasets, sampling strategies, and machine learning models. The system allows researchers to study how models can improve labeling efficiency by selectively querying the most informative data points rather than relying on uniformly sampled training sets. The main experiment runner (run_experiment.py) supports a wide range of configurations, including batch sizes, dataset subsets, model selection, and data preprocessing options. It includes several established active learning strategies such as uncertainty sampling, k-center greedy selection, and bandit-based methods, while also allowing for custom algorithm implementations. ...

This is the code used in the following paper: Andy Diwen Zhu, Wenqing Lin, Sibo Wang, and Xiaokui Xiao. Reachability queries on large dynamic graphs: a total order approach. In Proceedings of the 2014 ACM SIGMOD international conference on Management of data (SIGMOD '14). Please cite the paper if you choose to use the code.

This projects hosts the worlds fastest algorithms for the labeling of point-features. It especially looks for algorithms thar are free of constraints, applicable to many scenarios (e.g., visualization, maps...) and do not need any preprocessing.