60 citations · 140 across the 9 of their papers we have counts for
9 papers
Simple Domain Generalization Methods are Strong Baselines for Open Domain Generalization
Masashi Noguchi, Shinichi Shirakawa
In real-world applications, a machine learning model is required to handle an open-set recognition (OSR), where unknown classes appear during the inference, in addition to a domain…
A Two-phase Framework with a Bézier Simplex-based Interpolation Method for Computationally Expensive Multi-objective Optimization
Ryoji Tanabe, Youhei Akimoto, Ken Kobayashi +3
This paper proposes a two-phase framework with a Bézier simplex-based interpolation method (TPB) for computationally expensive multi-objective optimization. The first phase in TPB…
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…
Controlling Model Complexity in Probabilistic Model-Based Dynamic Optimization of Neural Network Structures
Shota Saito, Shinichi Shirakawa
A method of simultaneously optimizing both the structure of neural networks and the connection weights in a single training loop can reduce the enormous computational cost of neura…
Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search
Youhei Akimoto, Shinichi Shirakawa, Nozomu Yoshinari +3
High sensitivity of neural architecture search (NAS) methods against their input such as step-size (i.e., learning rate) and search space prevents practitioners from applying them…
Parameterless Stochastic Natural Gradient Method for Discrete Optimization and its Application to Hyper-Parameter Optimization for Neural Network
Kouhei Nishida, Hernan Aguirre, Shota Saito +2
Black box discrete optimization (BBDO) appears in wide range of engineering tasks. Evolutionary or other BBDO approaches have been applied, aiming at automating necessary tuning of…
Sample Reuse via Importance Sampling in Information Geometric Optimization
Shinichi Shirakawa, Youhei Akimoto, Kazuki Ouchi +1
In this paper we propose a technique to reduce the number of function evaluations, which is often the bottleneck of the black-box optimization, in the information geometric optimiz…
Dynamic Optimization of Neural Network Structures Using Probabilistic Modeling
Shinichi Shirakawa, Yasushi Iwata, Youhei Akimoto
Deep neural networks (DNNs) are powerful machine learning models and have succeeded in various artificial intelligence tasks. Although various architectures and modules for the DNN…
A Genetic Programming Approach to Designing Convolutional Neural Network Architectures
Masanori Suganuma, Shinichi Shirakawa, Tomoharu Nagao
The convolutional neural network (CNN), which is one of the deep learning models, has seen much success in a variety of computer vision tasks. However, designing CNN architectures…