95 citations · 339 across the 39 of their papers we have counts for
15 papers · 1 filter
KENN: Enhancing Deep Neural Networks by Leveraging Knowledge for Time Series Forecasting
Muhammad Ali Chattha, Ludger van Elst, Muhammad Imran Malik +2
End-to-end data-driven machine learning methods often have exuberant requirements in terms of quality and quantity of training data which are often impractical to fulfill in real-w…
TimeREISE: Time-series Randomized Evolving Input Sample Explanation
Dominique Mercier, Andreas Dengel, Sheraz Ahmed
Deep neural networks are one of the most successful classifiers across different domains. However, due to their limitations concerning interpretability their use is limited in safe…
A Reinforcement Learning Approach for Sequential Spatial Transformer Networks
Fatemeh Azimi, Federico Raue, Joern Hees +1
Spatial Transformer Networks (STN) can generate geometric transformations which modify input images to improve the classifier's performance. In this work, we combine the idea of ST…
PatchX: Explaining Deep Models by Intelligible Pattern Patches for Time-series Classification
Dominique Mercier, Andreas Dengel, Sheraz Ahmed
The classification of time-series data is pivotal for streaming data and comes with many challenges. Although the amount of publicly available datasets increases rapidly, deep neur…
Contextual Classification Using Self-Supervised Auxiliary Models for Deep Neural Networks
Sebastian Palacio, Philipp Engler, Jörn Hees +1
Classification problems solved with deep neural networks (DNNs) typically rely on a closed world paradigm, and optimize over a single objective (e.g., minimization of the cross-ent…
Benchmarking adversarial attacks and defenses for time-series data
Shoaib Ahmed Siddiqui, Andreas Dengel, Sheraz Ahmed
The adversarial vulnerability of deep networks has spurred the interest of researchers worldwide. Unsurprisingly, like images, adversarial examples also translate to time-series da…