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20232025
most citedPriority-Aware Preemptive Scheduling for Mixed-Priority Workloads in MoE Inference

2 citations · 4 across the 8 of their papers we have counts for

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cs.LG2025

KVComm: Enabling Efficient LLM Communication through Selective KV Sharing

Xiangyu Shi, Marco Chiesa, Gerald Q. Maguire +1

Large Language Models (LLMs) are increasingly deployed in multi-agent systems, where effective inter-model communication is crucial. Existing communication protocols either rely on…

cs.LG2025★ 2 cited

Priority-Aware Preemptive Scheduling for Mixed-Priority Workloads in MoE Inference

Mohammad Siavashi, Faezeh Keshmiri Dindarloo, Dejan Kostic +1

Large Language Models have revolutionized natural language processing, yet serving them efficiently in data centers remains challenging due to mixed workloads comprising latency-se…

cs.LG2025

Deriving Coding-Specific Sub-Models from LLMs using Resource-Efficient Pruning

Laura Puccioni, Alireza Farshin, Mariano Scazzariello +3

Large Language Models (LLMs) have demonstrated their exceptional performance in various complex code generation tasks. However, their broader adoption is limited by significant com…

cs.LG2024

Robust Generalization of Graph Neural Networks for Carrier Scheduling

Daniel F. Perez-Ramirez, Carlos Pérez-Penichet, Nicolas Tsiftes +3

Battery-free sensor tags are devices that leverage backscatter techniques to communicate with standard IoT devices, thereby augmenting a network's sensing capabilities in a scalabl…

cs.LG2023★ 1 cited

FMM-Head: Enhancing Autoencoder-based ECG anomaly detection with prior knowledge

Giacomo Verardo, Magnus Boman, Samuel Bruchfeld +4

Detecting anomalies in electrocardiogram data is crucial to identifying deviations from normal heartbeat patterns and providing timely intervention to at-risk patients. Various Aut…