activity
20202026
most citedTowards Demystifying Serverless Machine Learning Training

106 citations · 167 across the 26 of their papers we have counts for

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Showing 2026Show all

5 papers · 1 filter

cs.CR2026

Fetch Me If You Can: A Comprehensive Analysis of Fetch Metadata

Pedro Bernardo, Alex Ploner, Marco Squarcina +2

Effective server-side defenses against cross-site attacks depend on understanding the context in which browser requests are generated. However, servers often lack the information n…

cs.DC2026

Mosaic: GPU Sharing with Latency Guarantees through Kernel-Level Interference Prediction

Foteini Strati, Ethan Graham, Leo Stephan +2

GPUs are increasingly in demand for AI workloads, yet often remain substantially underutilized, motivating workload colocation. However, colocation introduces interference that can…

cs.LG2026

Zephon: Elastic Determinism for Online, Stateful Foundation Model Data Loading Pipelines

Maximilian Böther, Josh Wills, Ties Robroek +14

Deterministic data loading is important for foundation model development: model researchers need confidence that differences they observe across costly ablations are caused by the…

cs.CV2026

DataComp-VLM: Improved Open Datasets for Vision-Language Models

Matteo Farina, Vishaal Udandarao, Thao Nguyen +33

Building performant Vision-Language Models (VLMs) requires carefully curating large-scale training datasets, yet the community lacks systematic benchmarks for evaluating such curat…

cs.AI2026

ELT-Bench-Verified: Benchmark Quality Issues Underestimate AI Agent Capabilities

Christopher Zanoli, Andrea Giovannini, Tengjun Jin +2

Constructing Extract-Load-Transform (ELT) pipelines is a labor-intensive data engineering task and a high-impact target for AI automation. On ELT-Bench, the first benchmark for end…