6 papers
Studying quantization trade-offs for efficient inference deployment in machine translation
Jim Zhao, Sohir Maskey, Koen Oostermeijer +2
Deploying large language models in realistic server environments poses challenges, as the system needs to provide high-quality responses with low latency. Quantization is a common…
The Price of Robustness: Stable Classifiers Need Overparameterization
Jonas von Berg, Adalbert Fono, Massimiliano Datres +2
The relationship between overparameterization, stability, and generalization remains incompletely understood in the setting of discontinuous classifiers. We address this gap by est…
1-Bit Wonder: Improving QAT Performance in the Low-Bit Regime through K-Means Quantization
Sohir Maskey, Constantin Eichenberg, Johannes Messner +1
Quantization-aware training (QAT) is an effective method to drastically reduce the memory footprint of LLMs while keeping performance degradation at an acceptable level. However, t…
Graph Representational Learning: When Does More Expressivity Hurt Generalization?
Sohir Maskey, Raffaele Paolino, Fabian Jogl +2
Graph Neural Networks (GNNs) are powerful tools for learning on structured data, yet the relationship between their expressivity and predictive performance remains unclear. We intr…
Graph Neural Networks for Enhancing Ensemble Forecasts of Extreme Rainfall
Christopher Bülte, Sohir Maskey, Philipp Scholl +2
Climate change is increasing the occurrence of extreme precipitation events, threatening infrastructure, agriculture, and public safety. Ensemble prediction systems provide probabi…
Weisfeiler and Leman Go Loopy: A New Hierarchy for Graph Representational Learning
Raffaele Paolino, Sohir Maskey, Pascal Welke +1
We introduce -loopy Weisfeiler-Leman (-WL), a novel hierarchy of graph isomorphism tests and a corresponding GNN framework, -MPNN, that can count cycles up…