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Yuta Nakahara
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2020 – today
- 2024
- [j9]Yuta Nakahara, Toshiyasu Matsushima:
Batch Updating of a Posterior Tree Distribution Over a Meta-Tree. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 107(3): 523-525 (2024) - [i10]Ryota Maniwa, Naoki Ichijo, Yuta Nakahara, Toshiyasu Matsushima:
Boosting-Based Sequential Meta-Tree Ensemble Construction for Improved Decision Trees. CoRR abs/2402.06386 (2024) - [i9]Keito Tajima, Naoki Ichijo, Yuta Nakahara, Toshiyasu Matsushima:
An Algorithmic Framework for Constructing Multiple Decision Trees by Evaluating Their Combination Performance Throughout the Construction Process. CoRR abs/2402.06452 (2024) - 2023
- [c10]Yuta Nakahara, Shota Saito, Koshi Shimada, Toshiyasu Matsushima:
Hyperparameter Learning of Bayesian Context Tree Models. ISIT 2023: 537-542 - [c9]Yuta Nakahara:
Tree-Structured Gaussian Mixture Models and Their Variational Inference. SMC 2023: 1129-1135 - [i8]Yuta Nakahara, Toshiyasu Matsushima:
Batch Updating of a Posterior Tree Distribution over a Meta-Tree. CoRR abs/2303.09705 (2023) - [i7]Yuta Nakahara, Shota Saito, Naoki Ichijo, Koki Kazama, Toshiyasu Matsushima:
Prediction Algorithms Achieving Bayesian Decision Theoretical Optimality Based on Decision Trees as Data Observation Processes. CoRR abs/2306.07060 (2023) - 2022
- [j8]Yuta Nakahara, Shota Saito, Akira Kamatsuka, Toshiyasu Matsushima:
Probability Distribution on Full Rooted Trees. Entropy 24(3): 328 (2022) - [j7]Yuta Nakahara, Toshiyasu Matsushima:
Stochastic Model of Block Segmentation Based on Improper Quadtree and Optimal Code under the Bayes Criterion. Entropy 24(8): 1152 (2022) - [j6]Yasushi Esaki, Yuta Nakahara, Toshiyasu Matsushima:
The Ratio of the Desired Parameters of Deep Neural Networks. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 105-A(3): 433-435 (2022) - [c8]Yuta Nakahara, Toshiyasu Matsushima:
Stochastic Model of Block Segmentation Based on Improper Quadtree and Optimal Code under the Bayes Criterion. DCC 2022: 153-162 - [c7]Yuta Nakahara, Shota Saito, Akira Kamatsuka, Toshiyasu Matsushima:
Probability Distribution on Rooted Trees. ISIT 2022: 174-179 - [i6]Yuta Nakahara, Shota Saito, Akira Kamatsuka, Toshiyasu Matsushima:
Probability Distribution on Rooted Trees. CoRR abs/2201.09460 (2022) - [i5]Ryohei Oka, Yuta Nakahara, Toshiyasu Matsushima:
Stochastic 2D Signal Generative Model with Wavelet Packets Basis Regarded as a Random Variable and Bayes Optimal Processing. CoRR abs/2202.00568 (2022) - 2021
- [j5]Nao Dobashi, Shota Saito, Yuta Nakahara, Toshiyasu Matsushima:
Meta-Tree Random Forest: Probabilistic Data-Generative Model and Bayes Optimal Prediction. Entropy 23(6): 768 (2021) - [j4]Yuta Nakahara, Toshiyasu Matsushima:
A Stochastic Model for Block Segmentation of Images Based on the Quadtree and the Bayes Code for It. Entropy 23(8): 991 (2021) - [c6]Yuta Nakahara, Toshiyasu Matsushima:
Hyperparameter Learning of Stochastic Image Generative Models with Bayesian Hierarchical Modeling and Its Effect on Lossless Image Coding. ITW 2021: 1-6 - [i4]Yuta Nakahara, Toshiyasu Matsushima:
A Stochastic Model for Block Segmentation of Images Based on the Quadtree and the Bayes Code for It. CoRR abs/2106.03349 (2021) - [i3]Yuta Nakahara, Shota Saito, Akira Kamatsuka, Toshiyasu Matsushima:
Probability Distribution on Full Rooted Trees. CoRR abs/2109.12825 (2021) - 2020
- [j3]Yuta Nakahara, Toshiyasu Matsushima:
Analysis of Decoding Error Probability of Spatially "Mt. Fuji" Coupled LDPC Codes in Waterfall Region of the BEC. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 103-A(12): 1337-1346 (2020) - [c5]Yuta Nakahara, Toshiyasu Matsushima:
A Stochastic Model of Block Segmentation Based on the Quadtree and the Bayes Code for It. DCC 2020: 293-302 - [c4]Yasushi Esaki, Yuta Nakahara, Toshiyasu Matsushima:
Theoretical Analysis of the Advantage of Deepening Neural Networks. ICMLA 2020: 479-484 - [c3]Yuta Nakahara, Toshiyasu Matsushima:
Autoregressive Image Generative Models with Normal and t-distributed Noise and the Bayes Codes for Them. ISITA 2020: 81-85 - [i2]Yasushi Esaki, Yuta Nakahara, Toshiyasu Matsushima:
Theoretical Analysis of the Advantage of Deepening Neural Networks. CoRR abs/2009.11479 (2020)
2010 – 2019
- 2019
- [c2]Yuta Nakahara, Toshiyasu Matsushima:
Covariance Evolution for Spatially "Mt. Fuji" Coupled LDPC Codes. ITW 2019: 1-5 - [i1]Yuta Nakahara, Toshiyasu Matsushima:
Covariance Evolution for Spatially "Mt. Fuji" Coupled LDPC Codes. CoRR abs/1904.05396 (2019) - 2018
- [j2]Yuta Nakahara, Toshiyasu Matsushima:
A Note on Weight Distributions of Spatially "Mt. Fuji" Coupled LDPC Codes. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 101-A(12): 2194-2198 (2018) - 2017
- [j1]Yuta Nakahara, Shota Saito, Toshiyasu Matsushima:
Spatially "Mt. Fuji" Coupled LDPC Codes. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 100-A(12): 2594-2606 (2017) - 2016
- [c1]Yuta Nakahara, Shota Saito, Toshiyasu Matsushima:
Spatially "Mt. Fuji" coupled LDPC codes. ISITA 2016: 201-205
Coauthor Index
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