Optimal layout recycling based on graph theoretic linear programming approach. - Yuji Shigehiro; Takashi Nagata; Isao Shirakawa; Takashi Kambe (VLSI, 1993)

Automatic layout recycling based on layout description and linear programming. - Yuji Shigehiro; Takashi Nagata; Isao Shirakawa; Itthichai Arungsrisangchai; Hiromitsu Takahashi (IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 1996)

Electric Direct-drive Stepping Motor for Robots. - Takashi Nagata; Koichi Suzumori; Takefumi Kanda; Kazuo Uzuka; Isao Enomoto (ICRA, 2004)

Development of Electromagnetic Nutation Motor (Electromagnetic Investigation). - Koichi Suzumori; Takashi Nagata; Takefumi Kanda; Kazuo Uzuka; Isao Enomoto (J. Robotics Mechatronics, 2004)

Simple local interpolation of surfaces using normal vectors. - Takashi Nagata (Comput. Aided Geom. Des., 2005)

Robust engine torque control by iterative learning control. - Takashi Nagata; Masayoshi Tomizuka (ACC, 2009)

Suppressing the Total Costs of Executing Tasks Using Mobile Agents. - Takashi Nagata; Munehiro Takimoto; Yasushi Kambayashi (HICSS, 2009)

Smooth Local Interpolation of Surfaces Using Normal Vectors. - Takashi Nagata (J. Appl. Math., 2010)

Cooperatively Searching Objects Based on Mobile Agents. - Takashi Nagata; Munehiro Takimoto; Yasushi Kambayashi (Trans. Comput. Collect. Intell., 2013)

VO2 estimation using 6-axis motion sensor with sports activity classification. - Takashi Nagata; Naoteru Nakamura; Masato Miyatake; Akira Yuuki; Hiroyuki Yomo; Takashi Kawabata; Shinsuke Hara (EMBC, 2016)

Applying neural network to VO2 estimation using 6-axis motion sensing data. - Naoteru Nakamura; Takashi Nagata; Masato Miyatake; Akira Yuuki; Hiroyuki Yomo; Takashi Kawabata; Shinsuke Hara (EMBC, 2016)

VO2 estimation using 6-axis motion sensing data. - Masato Miyatake; Naoteru Nakamura; Takashi Nagata; Akira Yuuki; Hiroyuki Yomo; Takashi Kawabata; Shinsuke Hara (ISMICT, 2016)

Noise peak filtering in multi-dimensional NMR spectra using convolutional neural networks. - Naohiro Kobayashi; Yoshikazu Hattori; Takashi Nagata; Shoko Shinya; Peter Güntert; Chojiro Kojima; Toshimichi Fujiwara (Bioinform., 2018)

How Does the Recently Discovered Peptide MIP Exhibit Much Higher Binding Affinity than an Anticancer Protein p53 for an Oncoprotein MDM2? - Tatsuya Yamada; Tomohiko Hayashi; Simon Hikiri; Naohiro Kobayashi; Hiroshi Yanagawa; Mitsunori Ikeguchi; Masato Katahira; Takashi Nagata; Masahiro Kinoshita (J. Chem. Inf. Model., 2019)

Domain Adaptation In Reinforcement Learning Via Latent Unified State Representation. - Jinwei Xing; Takashi Nagata; Kexin Chen 002; Xinyun Zou; Emre Neftci; Jeffrey L. Krichmar (AAAI, 2021)

Uncertainty Aware Model Integration on Reinforcement Learning. - Takashi Nagata; Jinwei Xing; Tsutomu Kumazawa; Emre Neftci (IJCNN, 2022)

Policy Distillation with Selective Input Gradient Regularization for Efficient Interpretability. - Jinwei Xing; Takashi Nagata; Xinyun Zou; Emre Neftci; Jeffrey L. Krichmar (CoRR, 2022)

Achieving efficient interpretability of reinforcement learning via policy distillation and selective input gradient regularization. - Jinwei Xing; Takashi Nagata; Xinyun Zou; Emre Neftci; Jeffrey L. Krichmar (Neural Networks, 2023)