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ORCIDIchigaku Takigawa
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2020 – today
- 2023
[i6]Hiroyasu Katsuno, Yuki Kimura, Tomoya Yamazaki, Ichigaku Takigawa:
Machine learning refinement of in situ images acquired by low electron dose LC-TEM. CoRR abs/2310.20279 (2023)- 2022
- [i5]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 abs/2201.08118 (2022) - 2021
- [j18]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): 1731-1749 (2021) - [i4]Hiroyasu Katsuno, Yuki Kimura, Tomoya Yamazaki, Ichigaku Takigawa:
Fast improvement of TEM image with low-dose electrons by deep learning. CoRR abs/2106.01718 (2021) - 2020
- [j17]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. 21-S(3): 94 (2020) - [c21]Atsuyoshi Nakamura, Ichigaku Takigawa, Hiroshi Mamitsuka:
Efficiently Enumerating Substrings with Statistically Significant Frequencies of Locally Optimal Occurrences in Gigantic String. AAAI 2020: 5240-5247 - [i3]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 abs/2004.03819 (2020)
2010 – 2019
- 2019
- [c20]Shoma Kikuchi, Ichigaku Takigawa, Satoshi Oyama, Masahito Kurihara:
Learning Relevant Molecular Representations via Self-Attentive Graph Neural Networks. IEEE BigData 2019: 5364-5369 - 2018
- [c19]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: 1-8 - [c18]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: 111-123 - [i2]Ryo Shirakawa, Yusei Yokoyama, Fumiya Okazaki, Ichigaku Takigawa:
Jointly learning relevant subgraph patterns and nonlinear models of their indicators. CoRR abs/1807.02963 (2018) - [i1]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 abs/1810.02080 (2018) - 2017
- [j16]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): 619-633 (2017) - [j15]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. 100-A(3): 865-876 (2017) - [j14]Ichigaku Takigawa, Hiroshi Mamitsuka:
Generalized Sparse Learning of Linear Models Over the Complete Subgraph Feature Set. IEEE Trans. Pattern Anal. Mach. Intell. 39(3): 617-624 (2017) - 2016
- [j13]Atsuyoshi Nakamura, Ichigaku Takigawa, Hisashi Tosaka, Mineichi Kudo, Hiroshi Mamitsuka:
Mining approximate patterns with frequent locally optimal occurrences. Discret. Appl. Math. 200: 123-152 (2016) - [j12]Sadamori Koujaku, Ichigaku Takigawa, Mineichi Kudo, Hideyuki Imai:
Dense core model for cohesive subgraph discovery. Soc. Networks 44: 143-152 (2016) - [c17]Jana Backhus, Ichigaku Takigawa, Hideyuki Imai, Mineichi Kudo, Masanori Sugimoto:
Reducing Redundancy with Unit Merging for Self-constructive Normalized Gaussian Networks. ICANN (1) 2016: 444-452 - [c16]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: 538-546 - 2015
- [j11]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. 98-A(11): 2315-2324 (2015) - [c15]Sadamori Koujaku, Mineichi Kudo, Ichigaku Takigawa, Hideyuki Imai:
Community Change Detection in Dynamic Networks in Noisy Environment. WWW (Companion Volume) 2015: 793-798 - 2014
- [j10]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): 734-747 (2014) - [c14]Akira Tanaka, Ichigaku Takigawa, Hideyuki Imai, Mineichi Kudo:
Theoretical Analyses on Ensemble and Multiple Kernel Regressors. ACML 2014 - [c13]Akira Tanaka, Ichigaku Takigawa, Hideyuki Imai, Mineichi Kudo:
Analyses on Generalization Error of Ensemble Kernel Regressors. S+SSPR 2014: 273-281 - 2013
- [j9]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): 1556-1575 (2013) - 2012
- [c12]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: 345-353 - 2011
- [j8]Ichigaku Takigawa, Hiroshi Mamitsuka:
Efficiently mining δ-tolerance closed frequent subgraphs. Mach. Learn. 82(2): 95-121 (2011) - [j7]Motoki Shiga, Ichigaku Takigawa, Hiroshi Mamitsuka:
A spectral approach to clustering numerical vectors as nodes in a network. Pattern Recognit. 44(2): 236-251 (2011) - 2010
- [j6]Timothy Hancock, Ichigaku Takigawa, Hiroshi Mamitsuka:
Mining metabolic pathways through gene expression. Bioinform. 26(17): 2128-2135 (2010) - [c11]Atsuyoshi Nakamura, Tomoya Saito, Ichigaku Takigawa, Hiroshi Mamitsuka, Mineichi Kudo:
Algorithms for Finding a Minimum Repetition Representation of a String. SPIRE 2010: 185-190
2000 – 2009
- 2009
- [j5]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): 2735-2743 (2009) - [j4]Ichigaku Takigawa, Mineichi Kudo, Atsuyoshi Nakamura:
Convex sets as prototypes for classifying patterns. Eng. Appl. Artif. Intell. 22(1): 101-108 (2009) - [j3]Shanfeng Zhu, Ichigaku Takigawa, Jia Zeng, Hiroshi Mamitsuka:
Field independent probabilistic model for clustering multi-field documents. Inf. Process. Manag. 45(5): 555-570 (2009) - 2008
- [j2]Ichigaku Takigawa, Hiroshi Mamitsuka:
Probabilistic path ranking based on adjacent pairwise coexpression for metabolic transcripts analysis. Bioinform. 24(2): 250-257 (2008) - [c10]Kosuke Hashimoto, Ichigaku Takigawa, Motoki Shiga, Minoru Kanehisa, Hiroshi Mamitsuka:
Mining significant tree patterns in carbohydrate sugar chains. ECCB 2008: 167-173 - [c9]Mineichi Kudo, Atsuyoshi Nakamura, Ichigaku Takigawa:
Classification by reflective convex hulls. ICPR 2008: 1-4 - 2007
- [c8]Shanfeng Zhu, Ichigaku Takigawa, Shuqin Zhang, Hiroshi Mamitsuka:
A Probabilistic Model for Clustering Text Documents with Multiple Fields. ECIR 2007: 331-342 - [c7]Motoki Shiga, Ichigaku Takigawa, Hiroshi Mamitsuka:
Annotating gene function by combining expression data with a modular gene network. ISMB/ECCB (Supplement of Bioinformatics) 2007: 468-478 - [c6]Motoki Shiga, Ichigaku Takigawa, Hiroshi Mamitsuka:
A spectral clustering approach to optimally combining numericalvectors with a modular network. KDD 2007: 647-656 - 2006
- [c5]Raymond Wan, Ichigaku Takigawa, Hiroshi Mamitsuka, Vo Ngoc Anh:
Combining Vector-Space and Word-Based Aspect Models for Passage Retrieval. TREC 2006 - [c4]Raymond Wan, Ichigaku Takigawa, Hiroshi Mamitsuka:
Applying Gaussian Distribution-Dependent Criteria to Decision Trees for High-Dimensional Microarray Data. VDMB 2006: 40-49 - 2005
- [c3]Ichigaku Takigawa, Mineichi Kudo, Atsuyoshi Nakamura:
The Convex Subclass Method: Combinatorial Classifier Based on a Family of Convex Sets. MLDM 2005: 90-99 - 2004
- [j1]Ichigaku Takigawa, Mineichi Kudo, Jun Toyama:
Performance analysis of minimum ℓ1-norm solutions for underdetermined source separation. IEEE Trans. Signal Process. 52(3): 582-591 (2004) - [c2]Ichigaku Takigawa, Mineichi Kudo, Atsuyoshi Nakamura, Jun Toyama:
On the Minimum l1-Norm Signal Recovery in Underdetermined Source Separation. ICA 2004: 193-200 - [c1]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: 1058-1064
Coauthor Index
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