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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

This work proposes Vague2Detect, a hybrid pipeline in which a fine-tuned Sentence-BERT retrieves candidates from a structured household Knowledge Base, and YOLO-World verifies their presence in the image, and improves performance to 61% VPSR with high precision, and up to 85% when augmented with GPT fallback.

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