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cs.LG2026
OneComp: One-Line Revolution for Generative AI Model Compression
Yuma Ichikawa, Keiji Kimura, Akihiro Yoshida +11
Deploying foundation models is increasingly constrained by memory footprint, latency, and hardware costs. Post-training compression can mitigate these bottlenecks by reducing the p…
cs.LG2025
Quantization Error Propagation: Revisiting Layer-Wise Post-Training Quantization
Yamato Arai, Yuma Ichikawa
Layer-wise PTQ is a promising technique for compressing large language models (LLMs), due to its simplicity and effectiveness without requiring retraining. However, recent progress…
cs.LG2024★ 1 cited
Optimization by Parallel Quasi-Quantum Annealing with Gradient-Based Sampling
Yuma Ichikawa, Yamato Arai
Learning-based methods have gained attention as general-purpose solvers due to their ability to automatically learn problem-specific heuristics, reducing the need for manually craf…