3 citations · 4 across the 4 of their papers we have counts for
4 papers
Surrogate Benchmarks for Model Merging Optimization
Rio Akizuki, Yuya Kudo, Nozomu Yoshinari +4
Model merging techniques aim to integrate the abilities of multiple models into a single model. Most model merging techniques have hyperparameters, and their setting affects the pe…
Bandit-Based Prompt Design Strategy Selection Improves Prompt Optimizers
Rin Ashizawa, Yoichi Hirose, Nozomu Yoshinari +2
Prompt optimization aims to search for effective prompts that enhance the performance of large language models (LLMs). Although existing prompt optimization methods have discovered…
Neural Architecture Search for Improving Latency-Accuracy Trade-off in Split Computing
Shoma Shimizu, Takayuki Nishio, Shota Saito +3
This paper proposes a neural architecture search (NAS) method for split computing. Split computing is an emerging machine-learning inference technique that addresses the privacy an…
NAS-HPO-Bench-II: A Benchmark Dataset on Joint Optimization of Convolutional Neural Network Architecture and Training Hyperparameters
Yoichi Hirose, Nozomu Yoshinari, Shinichi Shirakawa
The benchmark datasets for neural architecture search (NAS) have been developed to alleviate the computationally expensive evaluation process and ensure a fair comparison. Recent N…