8 citations · 16 across the 19 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
Statistical Mechanics of Min-Max Problems
Yuma Ichikawa, Koji Hukushima
Min-max optimization problems, also known as saddle point problems, have attracted significant attention due to their applications in various fields, such as fair beamforming, gene…
Training-Free Time-Series Anomaly Detection: Leveraging Image Foundation Models
Nobuo Namura, Yuma Ichikawa
Recent advancements in time-series anomaly detection have relied on deep learning models to handle the diverse behaviors of time-series data. However, these models often suffer fro…