11 citations · 57 across the 17 of their papers we have counts for
11 papers · 1 filter
Stabilizing Multimodal Autoencoders: A Theoretical and Empirical Analysis of Fusion Strategies
Diyar Altinses, Andreas Schwung
In recent years, the development of multimodal autoencoders has gained significant attention due to their potential to handle multimodal complex data types and improve model perfor…
Transfer learning of state-based potential games for process optimization in decentralized manufacturing systems
Steve Yuwono, Dorothea Schwung, Andreas Schwung
This paper presents a novel online transfer learning approach in state-based potential games (TL-SbPGs) for distributed self-optimization in manufacturing systems. The approach tar…
Gradient-based Learning in State-based Potential Games for Self-Learning Production Systems
Steve Yuwono, Marlon Löppenberg, Dorothea Schwung +1
In this paper, we introduce novel gradient-based optimization methods for state-based potential games (SbPGs) within self-learning distributed production systems. SbPGs are recogni…
Information Fusion for Assistance Systems in Production Assessment
Fernando Arévalo, Christian Alison M. Piolo, M. Tahasanul Ibrahim +1
We propose a novel methodology to define assistance systems that rely on information fusion to combine different sources of information while providing an assessment. The main cont…
Anomaly Detection using Ensemble Classification and Evidence Theory
Fernando Arévalo, Tahasanul Ibrahim, Christian Alison M. Piolo +1
Multi-class ensemble classification remains a popular focus of investigation within the research community. The popularization of cloud services has sped up their adoption due to t…
Predicting Rigid Body Dynamics using Dual Quaternion Recurrent Neural Networks with Quaternion Attention
Johannes Pöppelbaum, Andreas Schwung
We propose a novel neural network architecture based on dual quaternions which allow for a compact representation of informations with a main focus on describing rigid body movemen…
Graph neural networks-based Scheduler for Production planning problems using Reinforcement Learning
Mohammed Sharafath Abdul Hameed, Andreas Schwung
Reinforcement learning (RL) is increasingly adopted in job shop scheduling problems (JSSP). But RL for JSSP is usually done using a vectorized representation of machine features as…
Gradient Monitored Reinforcement Learning
Mohammed Sharafath Abdul Hameed, Gavneet Singh Chadha, Andreas Schwung +1
This paper presents a novel neural network training approach for faster convergence and better generalization abilities in deep reinforcement learning. Particularly, we focus on th…
Neural Logic Rule Layers
Jan Niclas Reimann, Andreas Schwung
Despite their great success in recent years, deep neural networks (DNN) are mainly black boxes where the results obtained by running through the network are difficult to understand…
Learning the Non-linearity in Convolutional Neural Networks
Gavneet Singh Chadha, Andreas Schwung
We propose the introduction of nonlinear operation into the feature generation process in convolutional neural networks. This nonlinearity can be implemented in various ways. First…
Generalized Dilation Neural Networks
Gavneet Singh Chadha, Jan Niclas Reimann, Andreas Schwung
Vanilla convolutional neural networks are known to provide superior performance not only in image recognition tasks but also in natural language processing and time series analysis…