1 citations · 1 across the 4 of their papers we have counts for
5 papers
A Mixture Autoregressive Image Generative Model on Quadtree Regions for Gaussian Noise Removal via Variational Bayes and Gradient Methods
Shota Saito, Yuta Nakahara, Kohei Horinouchi +3
This paper addresses the problem of image denoising for grayscale images. We propose a probabilistic image generative model that combines a quadtree region-partitioning model with…
Variable Splitting Binary Tree Models Based on Bayesian Context Tree Models for Time Series Segmentation
Yuta Nakahara, Shota Saito, Kohei Horinouchi +4
We propose a variable splitting binary tree (VSBT) model based on Bayesian context tree (BCT) models for time series segmentation. Unlike previous applications of BCT models, the t…
Soft Bayesian Context Tree Models for Real-Valued Time Series
Shota Saito, Yuta Nakahara, Toshiyasu Matsushima
This paper proposes the soft Bayesian context tree model (Soft-BCT), which is a novel BCT model for real-valued time series. The Soft-BCT considers soft (probabilistic) splits of t…
A Lower Bound for the Number of Linear Regions of Ternary ReLU Regression Neural Networks
Yuta Nakahara, Manabu Kobayashi, Toshiyasu Matsushima
With the advancement of deep learning, reducing computational complexity and memory consumption has become a critical challenge, and ternary neural networks (NNs) that restrict par…
Boosting-Based Sequential Meta-Tree Ensemble Construction for Improved Decision Trees
Ryota Maniwa, Naoki Ichijo, Yuta Nakahara +1
A decision tree is one of the most popular approaches in machine learning fields. However, it suffers from the problem of overfitting caused by overly deepened trees. Then, a meta-…