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Sponsored AI Networking Cookbook: Practical recipes for AI-assisted network automation and development AI Networking Cookbook: Practical recipes for AI-assisted network automation and development

Author

Jangho Kim

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Book Open access Aug 2026

Quantized Model Soup Shake-Up: Weight Perturbation for Enhanced Ensemble Diversity

Model soup, averaging the weights of multiple fine-tuned models, delivers ensemble-level accuracy at single-model inference cost, but its success requires both linear mode connectivity (LMC) and sufficient diversity among candidates. We study these two requirements under quantization-aware training (QAT). First, we sho...

Jinwook Chung, Sungyeop Jung, Weronika Czorapinska et al. · 0 citations
Preprint Aug 2026

SQuaT: Self-Supervised Knowledge Distillation via Student-Aware Quantized Teacher Features

SQuaT (Student-Aware Quantized Teacher Features), a label-free QAT framework with KD that theoretically eliminates this lower bound on the distillation loss by applying the student's quantization parameters to quantize the teacher's features during distillation is proposed.

H. Lee, Hyeonsik Jo, Jinwook Chung et al. · 0 citations

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