25 citations · 39 across the 7 of their papers we have counts for
8 papers
Auditing Conformal Prediction for Language Models Under Inference-Time Model Shift
Achref Doula, Max Mühlhäuser
Conformal prediction (CP) for language models is typically evaluated under a matched protocol, in which calibration and deployment use the same post-training inference configuratio…
Diffusion-Based Generation and Imputation of Driving Scenarios from Limited Vehicle CAN Data
Julian Ripper, Ousama Esbel, Rafael Fietzek +2
Training deep learning methods on small time series datasets that also include corrupted samples is challenging. Diffusion models have shown to be effective to generate realistic a…
Whenever, Wherever: Towards Orchestrating Crowd Simulations with Spatio-Temporal Spawn Dynamics
Thomas Kreutz, Max Mühlhäuser, Alejandro Sanchez Guinea
Realistic crowd simulations are essential for immersive virtual environments, relying on both individual behaviors (microscopic dynamics) and overall crowd patterns (macroscopic ch…
A Tale of Two Scales: Reconciling Horizontal and Vertical Scaling for Inference Serving Systems
Kamran Razavi, Mehran Salmani, Max Mühlhäuser +2
Inference serving is of great importance in deploying machine learning models in real-world applications, ensuring efficient processing and quick responses to inference requests. H…
Sponge: Inference Serving with Dynamic SLOs Using In-Place Vertical Scaling
Kamran Razavi, Saeid Ghafouri, Max Mühlhäuser +2
Mobile and IoT applications increasingly adopt deep learning inference to provide intelligence. Inference requests are typically sent to a cloud infrastructure over a wireless netw…
Reconciling High Accuracy, Cost-Efficiency, and Low Latency of Inference Serving Systems
Mehran Salmani, Saeid Ghafouri, Alireza Sanaee +5
The use of machine learning (ML) inference for various applications is growing drastically. ML inference services engage with users directly, requiring fast and accurate responses.…
When the Metaverse Meets Carbon Neutrality: Ongoing Efforts and Directions
Fangming Liu, Qiangyu Pei, Shutong Chen +3
The metaverse has recently gained increasing attention from the public. It builds up a virtual world where we can live as a new role regardless of the role we play in the physical…
Federated Learning Attacks Revisited: A Critical Discussion of Gaps, Assumptions, and Evaluation Setups
Aidmar Wainakh, Ephraim Zimmer, Sandeep Subedi +5
Federated learning (FL) enables a set of entities to collaboratively train a machine learning model without sharing their sensitive data, thus, mitigating some privacy concerns. Ho…