7 papers
Statistics of Min-max Normalized Eigenvalues in Random Matrices
Hyakka Nakada, Shu Tanaka
Random matrix theory has played an important role in various areas of pure mathematics, mathematical physics, and machine learning. From a practical perspective of data science, in…
What Shape Is Optimal for Masks in Text Removal?
Hyakka Nakada, Marika Kubota
The advent of generative models has dramatically improved the accuracy of image inpainting. In particular, by removing specific text from document images, reconstructing original i…
Robustness of Structured Data Extraction from Perspectively Distorted Documents
Hyakka Nakada, Yoshiyasu Tanaka
Optical Character Recognition (OCR) for data extraction from documents is essential to intelligent informatics, such as digitizing medical records and recognizing road signs. Multi…
Quick design of feasible tensor networks for constrained combinatorial optimization
Hyakka Nakada, Kotaro Tanahashi, Shu Tanaka
Quantum computers are expected to enable fast solving of large-scale combinatorial optimization problems. However, their limitations in fidelity and the number of qubits prevent th…
Initialization Method for Factorization Machine Based on Low-Rank Approximation for Constructing a Corrected Approximate Ising Model
Yuya Seki, Hyakka Nakada, Shu Tanaka
This paper presents an initialization method that can approximate a given approximate Ising model with a high degree of accuracy using a factorization machine (FM), a machine learn…
Systematic and Efficient Construction of Quadratic Unconstrained Binary Optimization Forms for High-order and Dense Interactions
Hyakka Nakada, Shu Tanaka
Quantum Annealing (QA) can efficiently solve combinatorial optimization problems whose objective functions are represented by Quadratic Unconstrained Binary Optimization (QUBO) for…