5 citations · 5 across the 6 of their papers we have counts for
7 papers
Phase Transition in Binary Compressed Sensing via Annealing with Adaptive Regularization
Xiaoxin Huang, Masayuki Ohzeki
Regularization choice changes the recovery phase diagrams of annealing-based binary compressed sensing. We develop a regularization-selection method that combines systematic parame…
Sparse Signal Recovery Using Log-Sum Regularization and Adaptive Smoothing
Keisuke Morita, Masayuki Ohzeki
We study sparse signal recovery from noisy linear observations using nonconvex log-sum regularization. The log-sum penalty reduces the shrinkage bias of regularization and…
Phase transition in compressed sensing using log-sum penalty and adaptive smoothing
Keisuke Morita, Federico Ricci-Tersenghi, Masayuki Ohzeki
In many real-world problems, recovering sparse signals from underdetermined linear systems remains a fundamental challenge. Although norm minimization is widely used, it s…
Filtering out mislabeled training instances using black-box optimization and quantum annealing
Makoto Otsuka, Kento Kodama, Keisuke Morita +1
This study proposes an approach for removing mislabeled instances from contaminated training datasets by combining surrogate model-based black-box optimization (BBO) with postproce…
Reconsideration of optimization for reduction of traffic congestion
Masayuki Ohzeki
One of the most impressive applications of a quantum annealer was optimizing a group of Volkswagen to reduce traffic congestion using a D-Wave system. A simple formulation of a qua…
Solution space and storage capacity of fully connected two-layer neural networks with generic activation functions
Sota Nishiyama, Masayuki Ohzeki
The storage capacity of a binary classification model is the maximum number of random input-output pairs per parameter that the model can learn. It is one of the indicators of the…
Random Postprocessing for Combinatorial Bayesian Optimization
Keisuke Morita, Yoshihiko Nishikawa, Masayuki Ohzeki
Model-based sequential approaches to discrete "black-box" optimization, including Bayesian optimization techniques, often access the same points multiple times for a given objectiv…