11 citations · 32 across the 11 of their papers we have counts for
11 papers
Closed-Form Steepest Descent Direction toward Flat Minima: Reducing Upper Bounds on the Loss Hessian Eigenspectrum in Neural Networks
Yuto Omae, Kazuki Sakai, Yohei Kakimoto +3
The flatness hypothesis suggests that flatness of the loss landscape, as measured by the eigenvalues of the loss Hessian, correlates with better neural network generalization. Whil…
Analytical Evaluation of DCA Convergence Properties for Minimizing Prediction Functions of Gaussian RBF Support Vector Regression
Yohei Kakimoto, Yuto Omae, Hirotaka Takahashi
For nonconvex optimization problems whose objective is the prediction function of a trained Support Vector Regression (SVR) model with the Gaussian radial basis function (RBF) kern…
Wolkowicz-Styan Upper Bound on the Hessian Eigenspectrum for Cross-Entropy Loss in Nonlinear Smooth Neural Networks
Yuto Omae, Kazuki Sakai, Yohei Kakimoto +3
Neural networks (NNs) are central to modern machine learning and achieve state-of-the-art results in many applications. However, the relationship between loss geometry and generali…
Cardiomyopathy Diagnosis Model from Endomyocardial Biopsy Specimens: Appropriate Feature Space and Class Boundary in Small Sample Size Data
Masaya Mori, Yuto Omae, Yutaka Koyama +4
As the number of patients with heart failure increases, machine learning (ML) has garnered attention in cardiomyopathy diagnosis, driven by the shortage of pathologists. However, e…
Comparative study of 1D and 2D convolutional neural network models with attribution analysis for gravitational wave detection from compact binary coalescences
Seiya Sasaoka, Naoki Koyama, Diego Dominguez +4
Recent advancements in gravitational wave astronomy have seen the application of convolutional neural networks (CNNs) in signal detection from compact binary coalescences. This stu…
Multi-rules mining algorithm for combinatorially exploded decision trees with modified Aitchison-Aitken function-based Bayesian optimization
Yuto Omae, Masaya Mori, Yohei Kakimoto
Decision trees offer the benefit of easy interpretation because they allow the classification of input data based on if--then rules. However, as decision trees are constructed by a…
Visualizing convolutional neural network for classifying gravitational waves from core-collapse supernovae
Seiya Sasaoka, Naoki Koyama, Diego Dominguez +4
In this study, we employ a convolutional neural network to classify gravitational waves originating from core-collapse supernovae. Training is conducted using spectrograms derived…
FG-SSA: Features Gradient-based Signals Selection Algorithm of Linear Complexity for Convolutional Neural Networks
Yuto Omae, Yusuke Sakai, Hirotaka Takahashi
Recently, many convolutional neural networks (CNNs) for classification by time domain data of multisignals have been developed. Although some signals are important for correct clas…
Theoretical Analysis of SIRVVD Model to Provide Insight on the Target Rate of COVID-19/SARS-CoV-2 Vaccination in Japan
Yuto Omae, Makoto Sasaki, Jun Toyotani +2
The effectiveness of the first and second dose vaccinations are different for COVID-19; therefore, a susceptible-infected-recovered-vaccination1-vaccination2-death (SIRVVD) model t…
A Calculation Model for Estimating Effect of COVID-19 Contact-Confirming Application (COCOA) on Decreasing Infectors
Yuto Omae, Jun Toyotani, Kazuyuki Hara +2
As of 2020, COVID-19 is spreading in the world. In Japan, the Ministry of Health, Labor and Welfare developed COVID-19 Contact-Confirming Application (COCOA). The researches to exa…
Effectiveness of the COVID-19 Contact-Confirming Application (COCOA) based on a Multi Agent Simulation
Yuto Omae, Jun Toyotani, Kazuyuki Hara +2
As of Aug. 2020, coronavirus disease 2019 (COVID-19) is still spreading in the world. In Japan, the Ministry of Health, Labor, and Welfare developed "COVID-19 Contact-Confirming Ap…