Web Science and Digital Libraries Research Group

Contrastive learning models proposed in recent years (Figure 1 from A Simple Framework for Contrastive Learning of Visual Representations)  Supervised learning has been widely used in machine learning, which requires the input data (for example, images) be annotated (for example, classes). We can use supervised learning to build models to find the relationship between input data and annotated labels and then use the models to predict labels for future inputs. This is the way in traditional machine learning, such as decision tree, logistic regression, SVM, and neural networks. It's costly to create annotations, and as a result for most of the circumstances  annotation is not available . We need a method which can make use of  tons of data in real life without annotation , which is called unsupervised learning . Conventional u nsupervised learning includes clustering (for example, k-means ), dimension reduction (for example, p rincipal component analysis ) and so...

  This Summer, I was accepted as a graduated student at the Quality Management System (QMS), a subdivision of Cloud Hardware and Infrastructure Engineering (CHIE) organization, at  Microsoft Corporation  located at Redmond, Washington, USA. Microsoft is an international organization, whose mission is to empower every person and every organization on the planet to achieve more. This year, Microsoft continued its student internship program for Summer 2022. Approximately 4000 students joined Microsoft in the USA to work on various projects during this Summer. Due to the current situation of the COVID-19 pandemic, most internships were an hybrid of both onsite and remote work. My internship was a 12 weeks program which started on May 23rd, 2022. During this internship, I worked mostly remotely as a data scientist intern under the supervision of Nikolaj Lunoee  and Kapil Jain . However, I visited the Redmond campus during the week July 25th through July 29th, 2022. Throug...