31 citations · 407 across the 77 of their papers we have counts for
9 papers · 1 filter
Efficiency Will Not Lead to Sustainable Reasoning AI
Philipp Wiesner, Daniel W. O'Neill, Francesca Larosa +1
AI research is increasingly moving toward complex problem solving, where models are optimized not only for pattern recognition but for multi-step reasoning. Historically, computing…
Comparative Analysis of Large Language Models for the Machine-Assisted Resolution of User Intentions
Justus Flerlage, Alexander Acker, Odej Kao
Large Language Models (LLMs) have emerged as transformative tools for natural language understanding and user intent resolution, enabling tasks such as translation, summarization,…
Empirical Evidences for the Effects of Feature Diversity in Open Set Recognition and Continual Learning
Jiawen Xu, Odej Kao
Open set recognition (OSR) and continual learning are two critical challenges in machine learning, focusing respectively on detecting novel classes at inference time and updating m…
Quantifying the Energy Consumption and Carbon Emissions of LLM Inference via Simulations
Miray Özcan, Philipp Wiesner, Philipp Weiß +1
The environmental impact of Large Language Models (LLMs) is rising significantly, with inference now accounting for more than half of their total lifecycle carbon emissions. Howeve…
Moving Beyond Marginal Carbon Intensity: A Poor Metric for Both Carbon Accounting and Grid Flexibility
Philipp Wiesner, Odej Kao
Marginal Carbon Intensity (MCI) has been promoted as an effective metric for carbon-aware computing. Although it is already considered as impractical for carbon accounting purposes…
Choosing the Right Battery Model for Data Center Simulations
Paul Kilian, Philipp Wiesner, Odej Kao
As demand for computing resources continues to rise, the increasing cost of electricity and anticipated regulations on carbon emissions are prompting changes in data center power s…
Towards Machine-Generated Code for the Resolution of User Intentions
Justus Flerlage, Ilja Behnke, Odej Kao
The growing capabilities of Artificial Intelligence (AI), particularly Large Language Models (LLMs), prompt a reassessment of the interaction mechanisms between users and their dev…
Informed Mixing -- Improving Open Set Recognition via Attribution-based Augmentation
Jiawen Xu, Odej Kao, Margret Keuper
Open set recognition (OSR) is devised to address the problem of detecting novel classes during model inference. Even in recent vision models, this remains an open issue which is re…
-GNN: A Robust Ensemble Approach Against Graph Structure Perturbation
Haci Ismail Aslan, Philipp Wiesner, Ping Xiong +1
Graph Neural Networks (GNNs) are playing an increasingly important role in the efficient operation and security of computing systems, with applications in workload scheduling, anom…