10 citations · 11 across the 5 of their papers we have counts for
5 papers
Batch Acquisition Function Evaluations and Decouple Optimizer Updates for Faster Bayesian Optimization
Kaichi Irie, Shuhei Watanabe, Masaki Onishi
Bayesian optimization (BO) efficiently finds high-performing parameters by maximizing an acquisition function, which models the promise of parameters. A major computational bottlen…
Speeding Up Multi-Objective Hyperparameter Optimization by Task Similarity-Based Meta-Learning for the Tree-Structured Parzen Estimator
Shuhei Watanabe, Noor Awad, Masaki Onishi +1
Hyperparameter optimization (HPO) is a vital step in improving performance in deep learning (DL). Practitioners are often faced with the trade-off between multiple criteria, such a…
Efficient stereo matching on embedded GPUs with zero-means cross correlation
Qiong Chang, Aolong Zha, Weimin Wang +5
Mobile stereo-matching systems have become an important part of many applications, such as automated-driving vehicles and autonomous robots. Accurate stereo-matching methods usuall…
How does AI play football? An analysis of RL and real-world football strategies
Atom Scott, Keisuke Fujii, Masaki Onishi
Recent advances in reinforcement learning (RL) have made it possible to develop sophisticated agents that excel in a wide range of applications. Simulations using such agents can p…
Warm Starting CMA-ES for Hyperparameter Optimization
Masahiro Nomura, Shuhei Watanabe, Youhei Akimoto +2
Hyperparameter optimization (HPO), formulated as black-box optimization (BBO), is recognized as essential for automation and high performance of machine learning approaches. The CM…