Ichigaku Takigawa

47 papers A* 2A 1B 2C 3Journal 25Unranked 14

Year	Rank	Type	Title / Venue / Authors
2025	—	`conf`	C2Explainer: Customizable Mask-based Counterfactual Explanation for Graph Neural Networks. FAccT Jiali Ma, Ichigaku Takigawa, Akihiro Yamamoto
2025	J	`jnl`	Developing embedding models for cytochrome P450-ligand interaction scoring. Briefings Bioinform. Martin, Ichigaku Takigawa
2023	J	`jnl`	Machine learning refinement of in situ images acquired by low electron dose LC-TEM. CoRR Hiroyasu Katsuno, Yuki Kimura, Tomoya Yamazaki, Ichigaku Takigawa
2022	J	`jnl`	Interval-Memoized Backtracking on ZDDs for Fast Enumeration of All Lower Cost Solutions. CoRR Shin-ichi Minato, Mutsunori Banbara, Takashi Horiyama, Jun Kawahara, Ichigaku Takigawa, Yutaro Yamaguchi
2021	J	`jnl`	Fast improvement of TEM image with low-dose electrons by deep learning. CoRR Hiroyasu Katsuno, Yuki Kimura, Tomoya Yamazaki, Ichigaku Takigawa
2021	J	`jnl`	Minor-embedding heuristics for large-scale annealing processors with sparse hardware graphs of up to 102, 400 nodes. Soft Comput. Yuya Sugie, Yuki Yoshida, Normann Mertig, Takashi Takemoto, Hiroshi Teramoto, Atsuyoshi Nakamura, Ichigaku Takigawa, Shin-ichi Minato, Masanao Yamaoka, Tamiki Komatsuzaki
2020	J	`jnl`	Dual graph convolutional neural network for predicting chemical networks. BMC Bioinform. Shonosuke Harada, Hirotaka Akita, Masashi Tsubaki, Yukino Baba, Ichigaku Takigawa, Yoshihiro Yamanishi, Hisashi Kashima
2020	A*	`conf`	Efficiently Enumerating Substrings with Statistically Significant Frequencies of Locally Optimal Occurrences in Gigantic String. AAAI Atsuyoshi Nakamura, Ichigaku Takigawa, Hiroshi Mamitsuka
2020	J	`jnl`	Minor-embedding heuristics for large-scale annealing processors with sparse hardware graphs of up to 102, 400 nodes. CoRR Yuya Sugie, Yuki Yoshida, Normann Mertig, Takashi Takemoto, Hiroshi Teramoto, Atsuyoshi Nakamura, Ichigaku Takigawa, Shin-ichi Minato, Masanao Yamaoka, Tamiki Komatsuzaki
2019	—	`conf`	Learning Relevant Molecular Representations via Self-Attentive Graph Neural Networks. IEEE BigData Shoma Kikuchi, Ichigaku Takigawa, Satoshi Oyama, Masahito Kurihara
2018	J	`jnl`	Dual Convolutional Neural Network for Graph of Graphs Link Prediction. CoRR Shonosuke Harada, Hirotaka Akita, Masashi Tsubaki, Yukino Baba, Ichigaku Takigawa, Yoshihiro Yamanishi, Hisashi Kashima
2018	—	`conf`	FPGA-Based QBoost with Large-Scale Annealing Processor and Accelerated Hyperparameter Search. ReConFig Takashi Takemoto, Normann Mertig, Masato Hayashi, Saki Susa-Tanaka, Hiroshi Teramoto, Atsuyoshi Nakamura, Ichigaku Takigawa, Shin-ichi Minato, Tamiki Komatsuzaki, Masanao Yamaoka
2018	—	`conf`	Graph Minors from Simulated Annealing for Annealing Machines with Sparse Connectivity. TPNC Yuya Sugie, Yuki Yoshida, Normann Mertig, Takashi Takemoto, Hiroshi Teramoto, Atsuyoshi Nakamura, Ichigaku Takigawa, Shin-ichi Minato, Masanao Yamaoka, Tamiki Komatsuzaki
2018	J	`jnl`	Jointly learning relevant subgraph patterns and nonlinear models of their indicators. CoRR Ryo Shirakawa, Yusei Yokoyama, Fumiya Okazaki, Ichigaku Takigawa
2017	J	`jnl`	An Online Self-Constructive Normalized Gaussian Network with Localized Forgetting. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. Jana Backhus, Ichigaku Takigawa, Hideyuki Imai, Mineichi Kudo, Masanori Sugimoto
2017	J	`jnl`	Exploring phenotype patterns of breast cancer within somatic mutations: a modicum in the intrinsic code. Briefings Bioinform. Sohiya Yotsukura, Masayuki Karasuyama, Ichigaku Takigawa, Hiroshi Mamitsuka
2017	J	`jnl`	Generalized Sparse Learning of Linear Models Over the Complete Subgraph Feature Set. IEEE Trans. Pattern Anal. Mach. Intell. Ichigaku Takigawa, Hiroshi Mamitsuka
2016	J	`jnl`	Dense core model for cohesive subgraph discovery. Soc. Networks Sadamori Koujaku, Ichigaku Takigawa, Mineichi Kudo, Hideyuki Imai
2016	J	`jnl`	Mining approximate patterns with frequent locally optimal occurrences. Discret. Appl. Math. Atsuyoshi Nakamura, Ichigaku Takigawa, Hisashi Tosaka, Mineichi Kudo, Hiroshi Mamitsuka
2016	—	`conf`	Online EM for the Normalized Gaussian Network with Weight-Time-Dependent Updates. ICONIP (4) Jana Backhus, Ichigaku Takigawa, Hideyuki Imai, Mineichi Kudo, Masanori Sugimoto
2016	—	`conf`	Reducing Redundancy with Unit Merging for Self-constructive Normalized Gaussian Networks. ICANN (1) Jana Backhus, Ichigaku Takigawa, Hideyuki Imai, Mineichi Kudo, Masanori Sugimoto
2015	—	`conf`	Community Change Detection in Dynamic Networks in Noisy Environment. WWW (Companion Volume) Sadamori Koujaku, Mineichi Kudo, Ichigaku Takigawa, Hideyuki Imai
2015	J	`jnl`	Ensemble and Multiple Kernel Regressors: Which Is Better? IEICE Trans. Fundam. Electron. Commun. Comput. Sci. Akira Tanaka, Hirofumi Takebayashi, Ichigaku Takigawa, Hideyuki Imai, Mineichi Kudo
2014	—	`conf`	Analyses on Generalization Error of Ensemble Kernel Regressors. S+SSPR Akira Tanaka, Ichigaku Takigawa, Hideyuki Imai, Mineichi Kudo
2014	J	`jnl`	Similarity-based machine learning methods for predicting drug-target interactions: a brief review. Briefings Bioinform. Hao Ding, Ichigaku Takigawa, Hiroshi Mamitsuka, Shanfeng Zhu
2014	C	`conf`	Theoretical Analyses on Ensemble and Multiple Kernel Regressors. ACML Akira Tanaka, Ichigaku Takigawa, Hideyuki Imai, Mineichi Kudo
2013	J	`jnl`	Fast algorithms for finding a minimum repetition representation of strings and trees. Discret. Appl. Math. Atsuyoshi Nakamura, Tomoya Saito, Ichigaku Takigawa, Mineichi Kudo, Hiroshi Mamitsuka
2012	—	`conf`	Extended Analyses for an Optimal Kernel in a Class of Kernels with an Invariant Metric. SSPR/SPR Akira Tanaka, Ichigaku Takigawa, Hideyuki Imai, Mineichi Kudo
2011	J	`jnl`	A spectral approach to clustering numerical vectors as nodes in a network. Pattern Recognit. Motoki Shiga, Ichigaku Takigawa, Hiroshi Mamitsuka
2011	J	`jnl`	Efficiently mining Mach. Learn. Ichigaku Takigawa, Hiroshi Mamitsuka
2010	C	`conf`	Algorithms for Finding a Minimum Repetition Representation of a String. SPIRE Atsuyoshi Nakamura, Tomoya Saito, Ichigaku Takigawa, Hiroshi Mamitsuka, Mineichi Kudo
2010	J	`jnl`	Mining metabolic pathways through gene expression. Bioinform. Timothy Hancock, Ichigaku Takigawa, Hiroshi Mamitsuka
2009	J	`jnl`	Convex sets as prototypes for classifying patterns. Eng. Appl. Artif. Intell. Ichigaku Takigawa, Mineichi Kudo, Atsuyoshi Nakamura
2009	J	`jnl`	Efficiently finding genome-wide three-way gene interactions from transcript- and genotype-data. Bioinform. Mitsunori Kayano, Ichigaku Takigawa, Motoki Shiga, Koji Tsuda, Hiroshi Mamitsuka
2009	J	`jnl`	Field independent probabilistic model for clustering multi-field documents. Inf. Process. Manag. Shanfeng Zhu, Ichigaku Takigawa, Jia Zeng, Hiroshi Mamitsuka
2008	B	`conf`	Classification by reflective convex hulls. ICPR Mineichi Kudo, Atsuyoshi Nakamura, Ichigaku Takigawa
2008	C	`conf`	Mining significant tree patterns in carbohydrate sugar chains. ECCB Kosuke Hashimoto, Ichigaku Takigawa, Motoki Shiga, Minoru Kanehisa, Hiroshi Mamitsuka
2008	J	`jnl`	Probabilistic path ranking based on adjacent pairwise coexpression for metabolic transcripts analysis. Bioinform. Ichigaku Takigawa, Hiroshi Mamitsuka
2007	A	`conf`	A Probabilistic Model for Clustering Text Documents with Multiple Fields. ECIR Shanfeng Zhu, Ichigaku Takigawa, Shuqin Zhang, Hiroshi Mamitsuka
2007	A*	`conf`	A spectral clustering approach to optimally combining numericalvectors with a modular network. KDD Motoki Shiga, Ichigaku Takigawa, Hiroshi Mamitsuka
2007	—	`conf`	Annotating gene function by combining expression data with a modular gene network. ISMB/ECCB (Supplement of Bioinformatics) Motoki Shiga, Ichigaku Takigawa, Hiroshi Mamitsuka
2006	—	`conf`	Applying Gaussian Distribution-Dependent Criteria to Decision Trees for High-Dimensional Microarray Data. VDMB Raymond Wan, Ichigaku Takigawa, Hiroshi Mamitsuka
2006	—	`conf`	Combining Vector-Space and Word-Based Aspect Models for Passage Retrieval. TREC Raymond Wan, Ichigaku Takigawa, Hiroshi Mamitsuka, Vo Ngoc Anh
2005	—	`conf`	The Convex Subclass Method: Combinatorial Classifier Based on a Family of Convex Sets. MLDM Ichigaku Takigawa, Mineichi Kudo, Atsuyoshi Nakamura
2004	—	`conf`	On the Minimum l ICA Ichigaku Takigawa, Mineichi Kudo, Atsuyoshi Nakamura, Jun Toyama
2004	J	`jnl`	Performance analysis of minimum ℓ IEEE Trans. Signal Process. Ichigaku Takigawa, Mineichi Kudo, Jun Toyama
2004	B	`conf`	Projection Learning Based Kernel Machine Design Using Series of Monotone Increasing Reproducing Kernel Hilbert Spaces. KES Akira Tanaka, Ichigaku Takigawa, Hideyuki Imai, Mineichi Kudo, Masaaki Miyakoshi