15 citations · 19 across the 4 of their papers we have counts for
5 papers
Solving In-Table Prediction Problems by Deep Neural Networks with Performance Evaluation Using Synthetic Data
Xiao Zhao, Daniela Oelke
Tabular deep learning (TDL) leverages neural networks (NN) to extract patterns from tabular data. Traditional TDL methods follow a supervised learning paradigm, where a target feat…
Visual Explanations with Attributions and Counterfactuals on Time Series Classification
Udo Schlegel, Daniela Oelke, Daniel A. Keim +1
With the rising necessity of explainable artificial intelligence (XAI), we see an increase in task-dependent XAI methods on varying abstraction levels. XAI techniques on a global l…
An Empirical Study of Explainable AI Techniques on Deep Learning Models For Time Series Tasks
Udo Schlegel, Daniela Oelke, Daniel A. Keim +1
Decision explanations of machine learning black-box models are often generated by applying Explainable AI (XAI) techniques. However, many proposed XAI methods produce unverified ou…
Towards a Rigorous Evaluation of XAI Methods on Time Series
Udo Schlegel, Hiba Arnout, Mennatallah El-Assady +2
Explainable Artificial Intelligence (XAI) methods are typically deployed to explain and debug black-box machine learning models. However, most proposed XAI methods are black-boxes…
Understanding Bias in Machine Learning
Jindong Gu, Daniela Oelke
Bias is known to be an impediment to fair decisions in many domains such as human resources, the public sector, health care etc. Recently, hope has been expressed that the use of m…