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South Korea

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Preprint Sep 2026

PEAT: Pseudo-Error Assessment for GPU Kernel Validation in DNN Training

The applicability of PEAT is demonstrated by presenting the results and analysis using GPUs from the two most popular vendors, NVIDIA V100 and AMD MI250, on various AI models, from vision tasks to language models, for both pretraining and finetuning scenarios.

Xuan Truong Nguyen Department of Next Generation Semico Convergence, Open Sharing System, Seoul National University et al. · 0 citations

#machine learning Preprint Sep 2026

Vague2Detect: Handling Ambiguous Prompts in Knowledge-Based Open-World Detection

Real-world detectors must often interpret functional or ambiguous prompts, yet conventional models such as YOLO remain restricted to fixed class lists. Even open-vocabulary models like YOLO-World frequently misalign vague language with the intended objects. Building on our prior work Commonsense-Guided Open-World Objec...

Ibrohimjon Muminov, Jihie Kim Dongguk University, Seoul et al. · 0 citations