Tabular data has long been overlooked by deep learning research, despite being the most common data type in real-world machine learning applications. While deep learning methods excel on many ML applications, tabular data classification problems are still dominated by Gradient-Boosted Decision Trees. More recently, deep learning-based approaches have been proposed which showed remarkable efficiency and performance improvements. In this seminar, we will discuss this recent literature, exploring the most promising techniques and approaches for handling tabular data in deep learning.
We require that you have taken lectures on
- Machine Learning
- Deep Learning
We strongly recommend that you have heard lectures on
- Automated Machine Learning
Every week all students read the relevant literature. Two students will prepare presentations for the topic of the week and present them in the session. After each presentation, we will have time for a question & discussion round, and all participants are expected to take part in these. Each student has to write a 4-page paper (in the AutoML paper format) about their assigned topic, which will be handed in one week after their presentation.
|Time||Every Tuesday from 14:15 - 16:00|
|Location||in-person; Room SR 00-006, Building 051|
|Organizers||Herilalaina Rakotoarison, Arbër Zela, Fabio Ferreira, Frank Hutter|
A list of relevant papers can be found here.