1 citations · 1 across the 4 of their papers we have counts for
4 papers
Prediction Algorithms Achieving Bayesian Decision Theoretical Optimality Based on Decision Trees as Data Observation Processes
Yuta Nakahara, Shota Saito, Naoki Ichijo +2
In the field of decision trees, most previous studies have difficulty ensuring the statistical optimality of a prediction of new data and suffer from overfitting because trees are…
(1+1)-CMA-ES with Margin for Discrete and Mixed-Integer Problems
Yohei Watanabe, Kento Uchida, Ryoki Hamano +3
The covariance matrix adaptation evolution strategy (CMA-ES) is an efficient continuous black-box optimization method. The CMA-ES possesses many attractive features, including inva…
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…
Efficient Search of Multiple Neural Architectures with Different Complexities via Importance Sampling
Yuhei Noda, Shota Saito, Shinichi Shirakawa
Neural architecture search (NAS) aims to automate architecture design processes and improve the performance of deep neural networks. Platform-aware NAS methods consider both perfor…