83 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Technical University of MunichDE20 papers
- Huawei Technologies (Germany)DE19 papers
- Technische Universität BerlinDE6 papers
- Huawei Technologies (China)CN4 papers
- ETH ZurichCH3 papers
- Hasso Plattner InstituteDE3 papers
- Universidade Federal do CearáBR3 papers
- University of CoimbraPT3 papers
- Centro de Investigação em Ciências da Informação Tecnologias e ArquiteturaPT2 papers
- Christian-Albrechts-Universität zu KielDE2 papers
- Imperial College LondonGB2 papers
- Iscte – Instituto Universitário de LisboaPT2 papers
7 papers · 1 filter
Greening AI Inference with Accuracy and Latency-aware User Incentives
Vasilios A. Siris, Adamantia Stamou, George D. Stamoulis +2
The widespread use of AI services has raised concerns for its environmental sustainability, towards which recent studies have identified carbon emissions of AI inference as the maj…
Multi-Objective Optimization Using Adaptive Distributed Reinforcement Learning
Jing Tan, Ramin Khalili, Holger Karl
The Intelligent Transportation System (ITS) environment is known to be dynamic and distributed, where participants (vehicle users, operators, etc.) have multiple, changing and poss…
Edge Generation Scheduling for DAG Tasks Using Deep Reinforcement Learning
Binqi Sun, Mirco Theile, Ziyuan Qin +4
Directed acyclic graph (DAG) tasks are currently adopted in the real-time domain to model complex applications from the automotive, avionics, and industrial domains that implement…
FedZero: Leveraging Renewable Excess Energy in Federated Learning
Philipp Wiesner, Ramin Khalili, Dennis Grinwald +3
Federated Learning (FL) is an emerging machine learning technique that enables distributed model training across data silos or edge devices without data sharing. Yet, FL inevitably…
GCNH: A Simple Method For Representation Learning On Heterophilous Graphs
Andrea Cavallo, Claas Grohnfeldt, Michele Russo +2
Graph Neural Networks (GNNs) are well-suited for learning on homophilous graphs, i.e., graphs in which edges tend to connect nodes of the same type. Yet, achievement of consistent…
Accelerating Transfer Learning with Near-Data Computation on Cloud Object Stores
Diana Petrescu, Arsany Guirguis, Do Le Quoc +3
Storage disaggregation underlies today's cloud and is naturally complemented by pushing down some computation to storage, thus mitigating the potential network bottleneck between t…
Self-Attentive Classification-Based Anomaly Detection in Unstructured Logs
Sasho Nedelkoski, Jasmin Bogatinovski, Alexander Acker +2
The detection of anomalies is essential mining task for the security and reliability in computer systems. Logs are a common and major data source for anomaly detection methods in a…