Find below EMERGE’s news articles, press releases, digests of scientific publications, as well as other outreach materials.
European Commission’s CORDIS website highlights EMERGE’s recent publication
13 November 2024
In the paper, EMERGE proposes a collaborative shared awareness as a more reliable, energy-efficient, and ethically tractable framework for the coordination between artificial systems and humans than an artificial general intelligence.
Publication: Enhancing Echo State Networks with Gradient-based Explainability Methods
11 November 2024
In this work, EMERGE partners from the University of Pisa assess whether a weighted average of hidden states can enhance the Echo State Network performance.
EMERGE consortium meeting in Munich
05 November 2024
Partners got together to discuss recent developments from each partner and the plan for the future of the project.
EMERGE partners awarded the Best Paper at DARS’24.
31 October 2024
Their work “Distributed Spatial Awareness for Swarms“ use local observations by robots of each other to build a global and distributed swarm-centric frame of reference.
EMERGE Project video: A new framework for artificial intelligence systems
21 October 2024
EMERGE’s collaborative awareness will allow robotic systems to adapt to the unstructured conditions found in the real world, decreasing deployment and maintenance costs, as well as increasing system robustness and interoperability.
Publication: Adaptive LoRA Merging of Efficient Domain Incremental Learning
14 October 2024
In this work, EMERGE partners from the University of Pisa propose an adaptive LoRA merging approach for DIL tasks that dynamically computes the coefficient for merging, allowing for continuous adaptation to new domains while adjusting the influence of previous ones.
Publication: Towards Deep Continual Workspace Monitoring: Performance Evaluation of CL Strategies for Object Detection in Working Sites
11 October 2024
In this work, EMERGE partners from the University of Pisa utilized a dataset tailored for continual object detection in diverse working environments. Using this dataset, a task-incremental and task-agnostic continual learning scenario was established in which each experience, corresponding to object detection sub-datasets collected from different work sites.
Publication: Reservoir Memory Networks
11 October 2024
In this work, EMERGE partners from the University of Pisa introduce Reservoir Memory Networks (RMNs), a novel class of Reservoir Computing (RC) models that integrate a linear memory cell with a non-linear reservoir to enhance long-term information retention.
Publication: Informed Machine Learning for Complex Data
11 October 2024
In this work, EMERGE partners from the University of Pisa gather valuable contributions and early findings in the field of Informed ML for Complex Data with the objective of showcasing the potential and limitations of new ideas, improvements, or the blending of ML and other research areas in solving real-world problems.
