Ichigaku Takigawa

Publication Activity (10 Years)

Years Active: 2004-2023
Publications (10 Years): 19

Top Topics

Link Prediction

Machine Learning

Convolutional Neural Network

Top Venues

Briefings Bioinform.

Discret. Appl. Math.

IEICE Trans. Fundam. Electron. Commun. Comput. Sci.

Publications

Hiroyasu Katsuno, Yuki Kimura, Tomoya Yamazaki, Ichigaku Takigawa
Machine learning refinement of in situ images acquired by low electron dose LC-TEM. CoRR (2023)
Shin-ichi Minato, Mutsunori Banbara, Takashi Horiyama, Jun Kawahara, Ichigaku Takigawa, Yutaro Yamaguchi
Interval-Memoized Backtracking on ZDDs for Fast Enumeration of All Lower Cost Solutions. CoRR (2022)
Hiroyasu Katsuno, Yuki Kimura, Tomoya Yamazaki, Ichigaku Takigawa
Fast improvement of TEM image with low-dose electrons by deep learning. CoRR (2021)
Yuya Sugie, Yuki Yoshida, Normann Mertig, Takashi Takemoto, Hiroshi Teramoto, Atsuyoshi Nakamura, Ichigaku Takigawa, Shin-ichi Minato, Masanao Yamaoka, Tamiki Komatsuzaki
Minor-embedding heuristics for large-scale annealing processors with sparse hardware graphs of up to 102, 400 nodes. Soft Comput. 25 (3) (2021)
Shonosuke Harada, Hirotaka Akita, Masashi Tsubaki, Yukino Baba, Ichigaku Takigawa, Yoshihiro Yamanishi, Hisashi Kashima
Dual graph convolutional neural network for predicting chemical networks. BMC Bioinform. (3) (2020)
Yuya Sugie, Yuki Yoshida, Normann Mertig, Takashi Takemoto, Hiroshi Teramoto, Atsuyoshi Nakamura, Ichigaku Takigawa, Shin-ichi Minato, Masanao Yamaoka, Tamiki Komatsuzaki
Minor-embedding heuristics for large-scale annealing processors with sparse hardware graphs of up to 102, 400 nodes. CoRR (2020)
Atsuyoshi Nakamura, Ichigaku Takigawa, Hiroshi Mamitsuka
Efficiently Enumerating Substrings with Statistically Significant Frequencies of Locally Optimal Occurrences in Gigantic String. AAAI (2020)
Shoma Kikuchi, Ichigaku Takigawa, Satoshi Oyama, Masahito Kurihara
Learning Relevant Molecular Representations via Self-Attentive Graph Neural Networks. IEEE BigData (2019)
Takashi Takemoto, Normann Mertig, Masato Hayashi, Saki Susa-Tanaka, Hiroshi Teramoto, Atsuyoshi Nakamura, Ichigaku Takigawa, Shin-ichi Minato, Tamiki Komatsuzaki, Masanao Yamaoka
FPGA-Based QBoost with Large-Scale Annealing Processor and Accelerated Hyperparameter Search. ReConFig (2018)
Ryo Shirakawa, Yusei Yokoyama, Fumiya Okazaki, Ichigaku Takigawa
Jointly learning relevant subgraph patterns and nonlinear models of their indicators. CoRR (2018)
Shonosuke Harada, Hirotaka Akita, Masashi Tsubaki, Yukino Baba, Ichigaku Takigawa, Yoshihiro Yamanishi, Hisashi Kashima
Dual Convolutional Neural Network for Graph of Graphs Link Prediction. CoRR (2018)
Yuya Sugie, Yuki Yoshida, Normann Mertig, Takashi Takemoto, Hiroshi Teramoto, Atsuyoshi Nakamura, Ichigaku Takigawa, Shin-ichi Minato, Masanao Yamaoka, Tamiki Komatsuzaki
Graph Minors from Simulated Annealing for Annealing Machines with Sparse Connectivity. TPNC (2018)
Jana Backhus, Ichigaku Takigawa, Hideyuki Imai, Mineichi Kudo, Masanori Sugimoto
An Online Self-Constructive Normalized Gaussian Network with Localized Forgetting. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. (3) (2017)
Sohiya Yotsukura, Masayuki Karasuyama, Ichigaku Takigawa, Hiroshi Mamitsuka
Exploring phenotype patterns of breast cancer within somatic mutations: a modicum in the intrinsic code. Briefings Bioinform. 18 (4) (2017)
Ichigaku Takigawa, Hiroshi Mamitsuka
Generalized Sparse Learning of Linear Models Over the Complete Subgraph Feature Set. IEEE Trans. Pattern Anal. Mach. Intell. 39 (3) (2017)
Sadamori Koujaku, Ichigaku Takigawa, Mineichi Kudo, Hideyuki Imai
Dense core model for cohesive subgraph discovery. Soc. Networks 44 (2016)
Atsuyoshi Nakamura, Ichigaku Takigawa, Hisashi Tosaka, Mineichi Kudo, Hiroshi Mamitsuka
Mining approximate patterns with frequent locally optimal occurrences. Discret. Appl. Math. 200 (2016)
Jana Backhus, Ichigaku Takigawa, Hideyuki Imai, Mineichi Kudo, Masanori Sugimoto
Reducing Redundancy with Unit Merging for Self-constructive Normalized Gaussian Networks. ICANN (1) (2016)
Jana Backhus, Ichigaku Takigawa, Hideyuki Imai, Mineichi Kudo, Masanori Sugimoto
Online EM for the Normalized Gaussian Network with Weight-Time-Dependent Updates. ICONIP (4) (2016)
Sadamori Koujaku, Mineichi Kudo, Ichigaku Takigawa, Hideyuki Imai
Community Change Detection in Dynamic Networks in Noisy Environment. WWW (Companion Volume) (2015)
Akira Tanaka, Hirofumi Takebayashi, Ichigaku Takigawa, Hideyuki Imai, Mineichi Kudo
Ensemble and Multiple Kernel Regressors: Which Is Better? IEICE Trans. Fundam. Electron. Commun. Comput. Sci. (11) (2015)
Akira Tanaka, Ichigaku Takigawa, Hideyuki Imai, Mineichi Kudo
Theoretical Analyses on Ensemble and Multiple Kernel Regressors. ACML (2014)
Akira Tanaka, Ichigaku Takigawa, Hideyuki Imai, Mineichi Kudo
Analyses on Generalization Error of Ensemble Kernel Regressors. S+SSPR (2014)
Hao Ding, Ichigaku Takigawa, Hiroshi Mamitsuka, Shanfeng Zhu
Similarity-based machine learning methods for predicting drug-target interactions: a brief review. Briefings Bioinform. 15 (5) (2014)
Atsuyoshi Nakamura, Tomoya Saito, Ichigaku Takigawa, Mineichi Kudo, Hiroshi Mamitsuka
Fast algorithms for finding a minimum repetition representation of strings and trees. Discret. Appl. Math. 161 (10-11) (2013)
Akira Tanaka, Ichigaku Takigawa, Hideyuki Imai, Mineichi Kudo
Extended Analyses for an Optimal Kernel in a Class of Kernels with an Invariant Metric. SSPR/SPR (2012)
Motoki Shiga, Ichigaku Takigawa, Hiroshi Mamitsuka
A spectral approach to clustering numerical vectors as nodes in a network. Pattern Recognit. 44 (2) (2011)
Ichigaku Takigawa, Hiroshi Mamitsuka
-tolerance closed frequent subgraphs. Mach. Learn. 82 (2) (2011)
Timothy Hancock, Ichigaku Takigawa, Hiroshi Mamitsuka
Mining metabolic pathways through gene expression. Bioinform. 26 (17) (2010)
Atsuyoshi Nakamura, Tomoya Saito, Ichigaku Takigawa, Hiroshi Mamitsuka, Mineichi Kudo
Algorithms for Finding a Minimum Repetition Representation of a String. SPIRE (2010)
Mitsunori Kayano, Ichigaku Takigawa, Motoki Shiga, Koji Tsuda, Hiroshi Mamitsuka
Efficiently finding genome-wide three-way gene interactions from transcript- and genotype-data. Bioinform. 25 (21) (2009)
Ichigaku Takigawa, Mineichi Kudo, Atsuyoshi Nakamura
Convex sets as prototypes for classifying patterns. Eng. Appl. Artif. Intell. 22 (1) (2009)
Shanfeng Zhu, Ichigaku Takigawa, Jia Zeng, Hiroshi Mamitsuka
Field independent probabilistic model for clustering multi-field documents. Inf. Process. Manag. 45 (5) (2009)
Kosuke Hashimoto, Ichigaku Takigawa, Motoki Shiga, Minoru Kanehisa, Hiroshi Mamitsuka
Mining significant tree patterns in carbohydrate sugar chains. ECCB (2008)
Ichigaku Takigawa, Hiroshi Mamitsuka
Probabilistic path ranking based on adjacent pairwise coexpression for metabolic transcripts analysis. Bioinform. 24 (2) (2008)
Mineichi Kudo, Atsuyoshi Nakamura, Ichigaku Takigawa
Classification by reflective convex hulls. ICPR (2008)
Motoki Shiga, Ichigaku Takigawa, Hiroshi Mamitsuka
Annotating gene function by combining expression data with a modular gene network. ISMB/ECCB (Supplement of Bioinformatics) (2007)
Shanfeng Zhu, Ichigaku Takigawa, Shuqin Zhang, Hiroshi Mamitsuka
A Probabilistic Model for Clustering Text Documents with Multiple Fields. ECIR (2007)
Motoki Shiga, Ichigaku Takigawa, Hiroshi Mamitsuka
A spectral clustering approach to optimally combining numericalvectors with a modular network. KDD (2007)
Raymond Wan, Ichigaku Takigawa, Hiroshi Mamitsuka
Applying Gaussian Distribution-Dependent Criteria to Decision Trees for High-Dimensional Microarray Data. VDMB (2006)
Raymond Wan, Ichigaku Takigawa, Hiroshi Mamitsuka, Vo Ngoc Anh
Combining Vector-Space and Word-Based Aspect Models for Passage Retrieval. TREC (2006)
Ichigaku Takigawa, Mineichi Kudo, Atsuyoshi Nakamura
The Convex Subclass Method: Combinatorial Classifier Based on a Family of Convex Sets. MLDM (2005)
Ichigaku Takigawa, Mineichi Kudo, Atsuyoshi Nakamura, Jun Toyama
-Norm Signal Recovery in Underdetermined Source Separation. ICA (2004)
Akira Tanaka, Ichigaku Takigawa, Hideyuki Imai, Mineichi Kudo, Masaaki Miyakoshi
Projection Learning Based Kernel Machine Design Using Series of Monotone Increasing Reproducing Kernel Hilbert Spaces. KES (2004)
Ichigaku Takigawa, Mineichi Kudo, Jun Toyama
-norm solutions for underdetermined source separation. IEEE Trans. Signal Process. 52 (3) (2004)