MAS Emergence
A safety perspective on emergence in multi-agent reinforcement-learning
Here you’ll find a curated overview of the papers where I have played a pivotal role, either as the first author or as a contributing author further down the authorship line. My involvement has spanned a variety of activities, from conceptualizing the initial ideas and developing machine learning models, to providing support and insights to my colleagues, or rigorously reviewing and refining the work.
A safety perspective on emergence in multi-agent reinforcement-learning
Exploring Predator-Prey Dynamics in multi-agent reinforcement-learning
Improving primate sounds classification by sublabeling
Social interaction based on surprise minimization
Evaluating A New Data Augmentation Method
Constructing ON from Collaborative Self-Replicators
Elaboration and journal article of the initial paper
Towards Anomaly Detection in Reinforcement Learning
Combining replication and auxiliary task for neural networks.
Attention based audio classification on Mel-Spektrograms
A Deep and Recurrent Architecture
Anomalie based Leak Detection in Water Networks
Acoustic Anomaly Detection for Machine Sounds based on Image Transfer Learning
Analysis of Feature Representations for Anomalous Sound Detection
Convolutional Neural Networks and Data Augmentations on Spectrograms
Service Coverage Analysis for Parked Autonomous Vehicles
PEOC for reliably detecting unencountered states in deep RL
Segmetation of point clouds into primitive building blocks.
Team market value estimation, similarity search and rankings.
Introduction a deep baseline for audio classification.
Introduction of NNs that are able to replicate their own weights.
We propose an approach to annotate trajectories using sequences of spatial perception.