9 citations · 21 across the 14 of their papers we have counts for
15 papers
Comprehensive Robustness Analysis of LiDAR-based 3D Object Detection in Autonomous Driving
Adwait Chandorkar, Kai Krink, Yerdana Maulenbay +2
Recent advancements in LiDAR-only 3D object detection have demonstrated improved detection accuracy over benchmark datasets. However, the adversarial robustness of these models rem…
ConTex: Reformulating Counterfactual Generation For Time Series Forecasting
Jan Voets, Hasan Tercan, Tobias Meisen +1
Decision-making with deep learning-based time series forecasting requires not only accurate predictions but also actionable insights. However, current architectures do not inherent…
EXCODER: EXplainable Classification Of DiscretE time series Representations
Yannik Hahn, Antonin Königsfeld, Hasan Tercan +1
Deep learning has significantly improved time series classification, yet the lack of explainability in these models remains a major challenge. While Explainable AI (XAI) techniques…
Graph Query Networks for Object Detection with Automotive Radar
Loveneet Saini, Hasan Tercan, Tobias Meisen
Object detection with 3D radar is essential for 360-degree automotive perception, but radar's long wavelengths produce sparse and irregular reflections that challenge traditional g…
Efficient Inter-Task Attention for Multitask Transformer Models
Christian Bohn, Thomas Kurbiel, Klaus Friedrichs +2
In both Computer Vision and the wider Deep Learning field, the Transformer architecture is well-established as state-of-the-art for many applications. For Multitask Learning, howev…
Out of Distribution Detection for Efficient Continual Learning in Quality Prediction for Arc Welding
Yannik Hahn, Jan Voets, Antonin Koenigsfeld +2
Modern manufacturing relies heavily on fusion welding processes, including gas metal arc welding (GMAW). Despite significant advances in machine learning-based quality prediction,…
Rethinking Backbone Design for Lightweight 3D Object Detection in LiDAR
Adwait Chandorkar, Hasan Tercan, Tobias Meisen
Recent advancements in LiDAR-based 3D object detection have significantly accelerated progress toward the realization of fully autonomous driving in real-world environments. Despit…
Detection Transformers Under the Knife: A Neuroscience-Inspired Approach to Ablations
Nils Hütten, Florian Hölken, Hasan Tercan +1
In recent years, Explainable AI has gained traction as an approach to enhancing model interpretability and transparency, particularly in complex models such as detection transforme…
AttentiveGRU: Recurrent Spatio-Temporal Modeling for Advanced Radar-Based BEV Object Detection
Loveneet Saini, Mirko Meuter, Hasan Tercan +1
Bird's-eye view (BEV) object detection has become important for advanced automotive 3D radar-based perception systems. However, the inherently sparse and non-deterministic nature o…
Task Weighting through Gradient Projection for Multitask Learning
Christian Bohn, Ido Freeman, Hasan Tercan +1
In multitask learning, conflicts between task gradients are a frequent issue degrading a model's training performance. This is commonly addressed by using the Gradient Projection a…
Decision Transformer for Enhancing Neural Local Search on the Job Shop Scheduling Problem
Constantin Waubert de Puiseau, Fabian Wolz, Merlin Montag +3
The job shop scheduling problem (JSSP) and its solution algorithms have been of enduring interest in both academia and industry for decades. In recent years, machine learning (ML)…
Emergent Language: A Survey and Taxonomy
Jannik Peters, Constantin Waubert de Puiseau, Hasan Tercan +4
The field of emergent language represents a novel area of research within the domain of artificial intelligence, particularly within the context of multi-agent reinforcement learni…
Beyond Training: Optimizing Reinforcement Learning Based Job Shop Scheduling Through Adaptive Action Sampling
Constantin Waubert de Puiseau, Christian Dörpelkus, Jannik Peters +2
Learned construction heuristics for scheduling problems have become increasingly competitive with established solvers and heuristics in recent years. In particular, significant imp…
Towards a Deep Learning-based Online Quality Prediction System for Welding Processes
Yannik Hahn, Robert Maack, Guido Buchholz +4
The digitization of manufacturing processes enables promising applications for machine learning-assisted quality assurance. A widely used manufacturing process that can strongly be…
Curriculum Learning in Job Shop Scheduling using Reinforcement Learning
Constantin Waubert de Puiseau, Hasan Tercan, Tobias Meisen
Solving job shop scheduling problems (JSSPs) with a fixed strategy, such as a priority dispatching rule, may yield satisfactory results for several problem instances but, neverthel…