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ORCIDKohei Hayashi
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2020 – today
- 2024
[j6]Hitoshi Katayama
, Kohei Hayashi, Yuya Imamura:
Sampled-Data Circular Path Following Control of Four Wheeled Mobile Robots With Steering Angle Saturation. IEEE Control. Syst. Lett. 8: 706-711 (2024)- [c38]Masanori Koyama, Kenji Fukumizu, Kohei Hayashi, Takeru Miyato:
Neural Fourier Transform: A General Approach to Equivariant Representation Learning. ICLR 2024 - [c37]Kei Nakagawa, Kohei Hayashi:
Lf-Net:Generating Fractional Time-Series with Latent Fractional-Net. IJCNN 2024: 1-8 - [i18]Kei Nakagawa, Kohei Hayashi, Yugo Fujimoto:
CFTM: Continuous time fractional topic model. CoRR abs/2402.01734 (2024) - [i17]Noboru Isobe, Masanori Koyama, Kohei Hayashi, Kenji Fukumizu:
Extended Flow Matching: a Method of Conditional Generation with Generalized Continuity Equation. CoRR abs/2402.18839 (2024) - 2023
- [c36]Kohei Hayashi, Yoshihiro Maeda, Norishige Fukushima:
Local Contrast Enhancement with Multiscale Filtering. APSIPA ASC 2023: 765-770 - [c35]Kohei Hayashi, Risa Harada, Naomi Yagi, Yutaka Hata, Yoshiaki Saji, Yoshitada Sakai:
Fuzzy Logic Evaluation of Knee Flexion Angle During Gait. ICMLC 2023: 370-375 - [i16]Soma Onishi, Kenta Oono, Kohei Hayashi:
TabRet: Pre-training Transformer-based Tabular Models for Unseen Columns. CoRR abs/2303.15747 (2023) - [i15]Masanori Koyama, Kenji Fukumizu, Kohei Hayashi, Takeru Miyato:
Neural Fourier Transform: A General Approach to Equivariant Representation Learning. CoRR abs/2305.18484 (2023) - [i14]Kenta Oono, Nontawat Charoenphakdee, Kotatsu Bito, Zhengyan Gao, Yoshiaki Ota, Shoichiro Yamaguchi, Yohei Sugawara, Shin-ichi Maeda, Kunihiko Miyoshi, Yuki Saito, Koki Tsuda, Hiroshi Maruyama, Kohei Hayashi:
Virtual Human Generative Model: Masked Modeling Approach for Learning Human Characteristics. CoRR abs/2306.10656 (2023) - 2022
- [c34]Kohei Hayashi, Kei Nakagawa:
Fractional SDE-Net: Generation of Time Series Data with Long-term Memory. DSAA 2022: 1-10 - [c33]Kohei Hayashi, Rrota Horiuchi, Nobuyoshi Komuro:
Scheduling Method for Improving Transmission and Reception Efficiency in IEEE802.15.4 used Heterogeneous Wireless Sensor Networks. ICCE-TW 2022: 29-30 - [c32]Kohei Hayashi, Naomi Yagi, Yutaka Hata, Yoshiaki Saji, Yoshitada Sakai:
Time Series Knee Joint Angle Analysis During Gait for Patients with Down Syndrome by 3d Pose Estimation. ICMLC 2022: 254-258 - [c31]Hiroaki Mikami, Kenji Fukumizu, Shogo Murai, Shuji Suzuki, Yuta Kikuchi, Taiji Suzuki, Shin-ichi Maeda, Kohei Hayashi:
A Scaling Law for Syn2real Transfer: How Much Is Your Pre-training Effective? ECML/PKDD (3) 2022: 477-492 - [i13]Kohei Hayashi, Kei Nakagawa:
Fractional SDE-Net: Generation of Time Series Data with Long-term Memory. CoRR abs/2201.05974 (2022) - 2021
- [i12]Hiroaki Mikami, Kenji Fukumizu, Shogo Murai, Shuji Suzuki, Yuta Kikuchi, Taiji Suzuki, Shin-ichi Maeda, Kohei Hayashi:
A Scaling Law for Synthetic-to-Real Transfer: A Measure of Pre-Training. CoRR abs/2108.11018 (2021) - 2020
- [j5]Kohei Hayashi, Yuichi Yoshida:
Testing Proximity to Subspaces: Approximate ℓ ∞ Minimization in Constant Time. Algorithmica 82(5): 1277-1297 (2020) - [c30]Kohei Hayashi, Masaaki Imaizumi, Yuichi Yoshida:
On Random Subsampling of Gaussian Process Regression: A Graphon-Based Analysis. AISTATS 2020: 2055-2065 - [i11]Katsuhiko Ishiguro, Kenta Oono, Kohei Hayashi:
Weisfeiler-Lehman Embedding for Molecular Graph Neural Networks. CoRR abs/2006.06909 (2020)
2010 – 2019
- 2019
- [c29]Kohei Hayashi, Taiki Yamaguchi, Yohei Sugawara, Shin-ichi Maeda:
Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks. NeurIPS 2019: 5553-5563 - [i10]Kohei Hayashi, Masaaki Imaizumi, Yuichi Yoshida:
On Random Subsampling of Gaussian Process Regression: A Graphon-Based Analysis. CoRR abs/1901.09541 (2019) - [i9]Takuya Shimada, Shoichiro Yamaguchi, Kohei Hayashi, Sosuke Kobayashi:
Data Interpolating Prediction: Alternative Interpretation of Mixup. CoRR abs/1906.08412 (2019) - [i8]Kohei Hayashi, Taiki Yamaguchi, Yohei Sugawara, Shin-ichi Maeda:
Einconv: Exploring Unexplored Tensor Decompositions for Convolutional Neural Networks. CoRR abs/1908.04471 (2019) - 2018
- [c28]Satoshi Hara, Kohei Hayashi:
Making Tree Ensembles Interpretable: A Bayesian Model Selection Approach. AISTATS 2018: 77-85 - [c27]Huda Hakami, Kohei Hayashi, Danushka Bollegala:
Why does PairDiff work? - A Mathematical Analysis of Bilinear Relational Compositional Operators for Analogy Detection. COLING 2018: 2493-2504 - [c26]Danushka Bollegala, Kohei Hayashi, Ken-ichi Kawarabayashi:
Think Globally, Embed Locally - Locally Linear Meta-embedding of Words. IJCAI 2018: 3970-3976 - 2017
- [j4]Yohei Kondo, Kohei Hayashi, Shin-ichi Maeda:
Sparse Bayesian linear regression with latent masking variables. Neurocomputing 258: 3-12 (2017) - [c25]Masaaki Imaizumi, Kohei Hayashi:
Tensor Decomposition with Smoothness. ICML 2017: 1597-1606 - [c24]Yuto Yamaguchi, Kohei Hayashi:
Tensor Decomposition with Missing Indices. IJCAI 2017: 3217-3223 - [c23]Yuto Yamaguchi, Kohei Hayashi:
When Does Label Propagation Fail? A View from a Network Generative Model. IJCAI 2017: 3224-3230 - [c22]Kohei Hayashi, Yuichi Yoshida:
Fitting Low-Rank Tensors in Constant Time. NIPS 2017: 2473-2481 - [c21]Masaaki Imaizumi, Takanori Maehara, Kohei Hayashi:
On Tensor Train Rank Minimization : Statistical Efficiency and Scalable Algorithm. NIPS 2017: 3930-3939 - [i7]Danushka Bollegala, Kohei Hayashi, Ken-ichi Kawarabayashi:
Think Globally, Embed Locally - Locally Linear Meta-embedding of Words. CoRR abs/1709.06671 (2017) - [i6]Huda Hakami, Kohei Hayashi, Danushka Bollegala:
An Optimality Proof for the PairDiff operator for Representing Relations between Words. CoRR abs/1709.06673 (2017) - 2016
- [c20]Takanori Maehara, Kohei Hayashi, Ken-ichi Kawarabayashi:
Expected Tensor Decomposition with Stochastic Gradient Descent. AAAI 2016: 1919-1925 - [c19]Masaaki Imaizumi, Kohei Hayashi:
Doubly Decomposing Nonparametric Tensor Regression. ICML 2016: 727-736 - [c18]Takuya Konishi, Tomoharu Iwata, Kohei Hayashi, Ken-ichi Kawarabayashi:
Identifying Key Observers to Find Popular Information in Advance. IJCAI 2016: 3761-3767 - [c17]Kohei Hayashi, Yuichi Yoshida:
Minimizing Quadratic Functions in Constant Time. NIPS 2016: 2217-2225 - [c16]Takuya Konishi, Takuya Ohwa, Sumio Fujita, Kazushi Ikeda, Kohei Hayashi:
Extracting Search Query Patterns via the Pairwise Coupled Topic Model. WSDM 2016: 655-664 - [i5]Kohei Hayashi, Takuya Konishi, Tatsuro Kawamoto:
A Tractable Fully Bayesian Method for the Stochastic Block Model. CoRR abs/1602.02256 (2016) - [i4]Kohei Hayashi, Yuichi Yoshida:
Minimizing Quadratic Functions in Constant Time. CoRR abs/1608.07179 (2016) - 2015
- [c15]Yohei Kondo, Shin-ichi Maeda, Kohei Hayashi:
Bayesian Masking: Sparse Bayesian Estimation with Weaker Shrinkage Bias. ACML 2015: 49-64 - [c14]Kohei Hayashi, Shin-ichi Maeda, Ryohei Fujimaki:
Rebuilding Factorized Information Criterion: Asymptotically Accurate Marginal Likelihood. ICML 2015: 1358-1366 - [c13]Kohei Hayashi, Takanori Maehara, Masashi Toyoda, Ken-ichi Kawarabayashi:
Real-Time Top-R Topic Detection on Twitter with Topic Hijack Filtering. KDD 2015: 417-426 - [i3]Kohei Hayashi, Shin-ichi Maeda, Ryohei Fujimaki:
Rebuilding Factorized Information Criterion: Asymptotically Accurate Marginal Likelihood. CoRR abs/1504.05665 (2015) - [i2]Yohei Kondo, Kohei Hayashi, Shin-ichi Maeda:
Bayesian Masking: Sparse Bayesian Estimation with Weaker Shrinkage Bias. CoRR abs/1509.01004 (2015) - 2013
- [c12]Kohei Hayashi, Fumihito Sasamori, Osamu Takyu, Shiro Handa:
Design and implementation of OFDM signal processing on PSoC microcontroller. ICTC 2013: 391-392 - [c11]Kohei Hayashi, Ryohei Fujimaki:
Factorized Asymptotic Bayesian Inference for Latent Feature Models. NIPS 2013: 1214-1222 - 2012
- [j3]Atsuhiro Narita, Kohei Hayashi, Ryota Tomioka, Hisashi Kashima:
Tensor factorization using auxiliary information. Data Min. Knowl. Discov. 25(2): 298-324 (2012) - [j2]Satoshi Oyama, Kohei Hayashi, Hisashi Kashima:
Link Prediction Across Time via Cross-Temporal Locality Preserving Projections. IEICE Trans. Inf. Syst. 95-D(11): 2664-2673 (2012) - [c10]Ryohei Fujimaki, Kohei Hayashi:
Factorized Asymptotic Bayesian Hidden Markov Models. ICML 2012 - [c9]Tsuyoshi Ueno, Kohei Hayashi, Takashi Washio, Yoshinobu Kawahara:
Weighted Likelihood Policy Search with Model Selection. NIPS 2012: 2366-2374 - [c8]Kohei Hayashi, Takashi Takenouchi, Ryota Tomioka, Hisashi Kashima:
Self-measuring Similarity for Multi-task Gaussian Process. ICML Unsupervised and Transfer Learning 2012: 145-154 - [i1]Ryohei Fujimaki, Kohei Hayashi:
Factorized Asymptotic Bayesian Hidden Markov Models. CoRR abs/1206.4679 (2012) - 2011
- [j1]Kohei Hayashi, Takashi Takenouchi, Tomohiro Shibata, Yuki Kamiya, Daishi Kato, Kazuo Kunieda, Keiji Yamada, Kazushi Ikeda:
Exponential family tensor factorization: an online extension and applications. Knowl. Inf. Syst. 33(1): 57-88 (2011) - [c7]Satoshi Oyama, Kohei Hayashi, Hisashi Kashima:
Cross-Temporal Link Prediction. ICDM 2011: 1188-1193 - [c6]Ryota Tomioka, Taiji Suzuki, Kohei Hayashi, Hisashi Kashima:
Statistical Performance of Convex Tensor Decomposition. NIPS 2011: 972-980 - [c5]Atsuhiro Narita, Kohei Hayashi, Ryota Tomioka, Hisashi Kashima:
Tensor Factorization Using Auxiliary Information. ECML/PKDD (2) 2011: 501-516 - 2010
- [c4]Kohei Hayashi, Takashi Takenouchi, Tomohiro Shibata, Yuki Kamiya, Daishi Kato, Kazuo Kunieda, Keiji Yamada, Kazushi Ikeda:
Exponential Family Tensor Factorization for Missing-Values Prediction and Anomaly Detection. ICDM 2010: 216-225
2000 – 2009
- 2009
- [c3]Kohei Hayashi, Junichiro Hirayama, Shin Ishii:
Dynamic Exponential Family Matrix Factorization. PAKDD 2009: 452-462 - [c2]Hiroshi Sakai, Kohei Hayashi, Michinori Nakata, Dominik Slezak:
The Lower System, the Upper System and Rules with Stability Factor in Non-deterministic Information Systems. RSFDGrC 2009: 313-320 - 2007
- [c1]Ikuko Nishikawa, Kohei Hayashi, Kazutoshi Sakakibara:
Complex-valued Neuron to describe the Dynamics after Hopf Bifurcation: an Example of CPG Model for a Biped Locomotion. IJCNN 2007: 327-332
Coauthor Index
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