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.
|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|
|Web page||Link to the seminar webpage|